Gene marker related to cancer prognosis and application thereof

Abstract

The invention discloses a gene marker related to prognosis of a cancer patient and application of the gene marker, and relates to the technical field of biomedicine.The biomarker does not need to depend on a tumor tissue sample, has high accuracy, high sensitivity and high specificity, is easy and convenient to judge, can be used for auxiliary diagnosis, and has a good application prospect. The tumor treatment effect is guaranteed, the life quality of a patient is kept to the maximum extent, and the treatment effect of a poor prognosis patient is improved as much as possible.

Description

Technical Field

[0001] The present invention relates to the field of biomedicine technology, and in particular to a gene marker associated with cancer prognosis and an application thereof. Background Art

[0002] Neoadjuvant chemoradiotherapy is a cancer treatment strategy, and the sensitivity of neoadjuvant chemoradiotherapy varies significantly among individuals. The degree of tumor regression after neoadjuvant chemoradiotherapy is significantly correlated with the patient's long-term survival rate and quality of life. If the tumor regression can be predicted in advance, a prospective and rigorous waiting and observation strategy can be adopted after neoadjuvant chemoradiotherapy, which can enable these patients to avoid surgery to preserve the anus. For some other patients, even after receiving intensive neoadjuvant chemoradiotherapy, the tumor regression is still unsatisfactory. If it can be predicted in advance that the tumor regression is limited, the treatment strategy can be adjusted from the beginning of treatment to improve the survival rate of these patients and prolong their survival time.

[0003] In the prior art, commonly used methods for predicting the efficacy of neoadjuvant chemoradiotherapy include immunofluorescence detection, tumor tissue section staining, immune cell density detection, CER5 expression level detection, etc.

[0004] As an example, immunofluorescence detection is to perform immunofluorescence staining on tumor tissue slices before treatment, and then determine the HLA-DR / MHC-II + CD20 in cells and tumor stroma + The density of B cells in the blood is used to judge the response of patients to neoadjuvant chemoradiotherapy. The disadvantages of this method include the need to obtain tumor tissue and the uncertainty of the prediction effect.

[0005] As an example, the tumor tissue section staining method, for example, performs HE staining on tumor tissue sections obtained by biopsy before treatment, and predicts the patient's response to neoadjuvant chemoradiotherapy based on the density of immune cells in the tumor area. The disadvantages of the tumor tissue section staining method include that tumor tissue must be obtained, and HE staining cannot determine the type of immune cells, resulting in inaccurate prediction results.

[0006] As an example, the immune cell density detection method, for example, slices of tumor tissue before treatment and performs immunohistochemical staining to predict the patient's response to neoadjuvant chemoradiotherapy based on the density of immune cells in the tumor area. The disadvantages of the immune cell density detection method include the need to obtain tumor tissue; in addition, the relative high and low density of immune cells is difficult to define.

[0007] As an example, the expression level of CER5 is determined by, for example, obtaining a sample of a primary solid tumor or its local or distant metastasis from a patient to determine the expression level of CER5, thereby determining its clinical response (WO2015092075A1). The disadvantages of this method include whether it is applicable to the determination of the treatment response of colorectal cancer patients, and it relies on tumor or tumor metastasis samples.

[0008] Therefore, there is an urgent need for a method with high sensitivity, high accuracy, high specificity, and independent of tumor or tumor metastasis samples that can be used to determine the efficacy of neoadjuvant chemoradiotherapy. Summary of the invention

[0009] In order to solve at least one of the above problems, the present disclosure provides a gene marker related to cancer prognosis and its application. The use of the gene marker provided by the present disclosure to evaluate the efficacy of radiotherapy and chemotherapy in cancer patients can be independent of tumor tissue samples, which is simpler than the existing technology and has high accuracy, high sensitivity and high specificity.

[0010] According to one aspect of the present disclosure, a gene marker associated with the prognosis of a cancer patient is provided, wherein the gene marker is associated with an IFNG signaling pathway.

[0011] In some embodiments, the gene markers include one or more of the following genes: JAK1 and IRF1.

[0012] In some embodiments, the gene markers further include one or more of the following genes: CD44, IFNGR1, PIAS1, CAMK2B, SP100, CAMK2A, OAS1, ICAM1, JAK2, SMAD7, MID1, TRIM35, TRIM14, TRIM2, TRIM3, OAS3, OAS2, IFNG, PTPN6, TRIM38, STAT1, SUMO1, TRIM62, GBP3, GBP1, IRF6, TRIM25, TRIM6, MT2A, IRF3, IRF5, TRIM21, TRIM5, TRIM22, TRIM45, OASL, IRF4, TRIM26, TRIM10, TRIM31, HLA-F, TRIM29, PML, IRF8, CAMK2D, CAMK2G, NCAM1, TRIM48, FCGR1A, GBP5, IFNGR2, GBP2, GBP4, VCAM1, TRIM17, TRIM46, PRKCD, B2M, TRIM68, IRF2, HLA-DPA1, PTAFR, TRIM8, PTPN2, PTPN11, HLA-DQB1, CIITA, GBP6, SOCS3, SOCS1, IRF7, HLA-DRB3, HLA-DRB1, PTPN1, HLA-DQB2, HLA-D QA1, FCGR1BP, HLA-DRB5, HLA-DRA, HLA-C, HLA-E, TRIM10, TRIM31, HLA-G, HLA-F, HLA-DPA1, HLA-DQA2, HLA-DQA1, HLA-DRA, HLA-H, HLA-B, HLA-E, HLA- A, HLA-A, HLA-G, HLA-F, GBP7, IRF9, HLA-DPB1, IFI30, TRIM31, HLA-B, HLA-DQA2, HLA-DPB1, HLA-A, HLA-DPA1, TRIM31, HLA-DQB2, HLA-B, HLA-DQA2, TRI M31, HLA-E, HLA-C, HLA-DQB1, TRIM26, HLA-DQB2, HLA-DRA, HLA-DPB1, HLA-DRB4, TRIM10, HLA-DRA, HLA-DRB1, HLA-DPA1, HLA-DQB2, HLA-C, HLA-DQB2, T RIM26, HLA-B, HLA-DRA, HLA-E, TRIM10, TRIM10, HLA-DQB2, HLA-DPA1, HLA-F, TRIM26, HLA-E, HLA-G, HLA-DQB2, HLA-DRA, HLA-DPB1, TRIM26, HLA-DRB4,HLA-DQB1, HLA-DPA1, HLA-DQA2, TRIM26, HLA-DRB3, HLA-DQA2, HLA-A, HLA-DQA1, HLA-B, HLA-DQ B2, HLA-G, HLA-DQA2, HLA-DQB1, TRIM31, HLA-E, TRIM26, TRIM26, HLA-B, HLA-DRA, TRIM10, HLA-F , HLA-G, HLA-A, HLA-G, HLA-DPA1, HLA-DPA1, HLA-E, HLA-DPB1, HLA-C, TRIM10, HLA-G, HLA-F, HLA -DQA2, TRIM10, HLA-DPB1, HLA-DQA2, TRIM34, IFNGR2, B2M, HLA-G, IRF7, HLA-DRA, IRF9, TRIM17. ,

[0013] In some embodiments, the cancer patient has been or will be treated with radiation therapy and / or chemotherapy.

[0014] In some embodiments, the cancer patient has undergone or will undergo neoadjuvant chemoradiotherapy.

[0015] In some embodiments, the tumor comprises a solid tumor and / or a hematological tumor.

[0016] In some embodiments, the tumor is selected from one or more of liver cancer, colon cancer, rectal cancer, head and neck cancer, colorectal cancer, lung cancer, breast cancer, cervical cancer, ovarian cancer, pancreatic cancer, bile duct cancer, kidney cancer, glioma, liposarcoma, melanoma, lymphoma, lung cancer, squamous cell carcinoma, chondrosarcoma and fibrosarcoma.

[0017] In some embodiments, the cancer is colorectal cancer.

[0018] According to another aspect of the present disclosure, a detection kit for evaluating cancer prognosis is provided, the kit comprising reagents for detecting the gene markers.

[0019] In some embodiments, the reagents for detecting the gene markers include primer pairs or probes targeting the gene markers.

[0020] In some embodiments, the probe is a fluorescent probe or a hybridization probe.

[0021] According to another aspect of the present disclosure, a method for screening gene markers associated with cancer prognosis is provided, the method comprising the following steps:

[0022] The REACTOME_INTERFERON_GAMMA_SIGNALING gene set was used to perform LASSO regression to screen gene markers as the gene set used for scoring.

[0023] According to another aspect of the present disclosure, a method for evaluating cancer prognosis is provided, the method comprising the step of detecting using the gene markers.

[0024] In some embodiments, the method comprises the following steps:

[0025] 1) performing quality control and filtering on the transcriptome sequencing data of the samples of the cancer subjects to obtain filtered sequencing data;

[0026] 2) Quantify transcripts from the filtered sequencing data;

[0027] 3) Scoring the gene markers and evaluating cancer prognosis based on the scores.

[0028] In some embodiments, the sample in step 1) comprises a body fluid sample or a tissue sample.

[0029] In some embodiments, the body fluid sample includes: saliva, whole blood, serum, plasma, milk, urine, lumbar or ventricular CSF, lymph fluid, prostatic fluid, semen, sputum, feces, tears, tumor cells, alveolar lavage fluid, sputum, pus, nasopharyngeal swab, oral swab, cerebrospinal fluid, pleural effusion, amniotic fluid, peritoneal fluid, aqueous humor, vitreous humor, vaginal discharge and their processed products.

[0030] In some embodiments, the tissue sample includes tissues, paraffin sections, and processed materials thereof.

[0031] In some embodiments, the volume of the body fluid sample comprises 1 to 10 mL.

[0032] In some embodiments, the volume of the body fluid sample comprises 1 mL, 2 mL, 3 mL, 4 mL, 5 mL, 6 mL, 7 mL, 8 mL, 9 mL, or 10 mL.

[0033] In some embodiments, the quality control in step 1) includes evaluating any of the following: the ratio of repeated sequences, the alignment ratio, the unique alignment ratio, the exon alignment ratio, the intron alignment ratio, the intergenic region alignment ratio, the expression efficiency, the detected transcripts, the detected genes or one or more data of the sequence coverage uniformity.

[0034] In some embodiments, the filtering in step 1) includes removing adapter sequences and low-quality reads from the 3' end.

[0035] In some embodiments, the quality control and filtering in step 1) adopt one or more of Trimmomatic, Skewer and fastp.

[0036] In some embodiments, the quantification in step 3) uses one or more of the following software: RSEM, eXpress, HTseq, Cufflinks, StringTie, Sailfish, Salmon, quasimapping or Kallisto.

[0037] In some embodiments, the quantification in step 2) is performed based on sequencing data.

[0038] In some embodiments, the sequencing data is in fastq format.

[0039] In some embodiments, the result obtained by the quantification in step 2) includes transcripts per million reads (TPM) or logarithmic TPM of each transcript.

[0040] In some embodiments, the quantification in step 2) is obtained using tximport software.

[0041] In some embodiments, the scoring in step 3) is performed using a gene set variation analysis (GSVA) ​​method.

[0042] In some embodiments, the GSVA method comprises the following steps: kernel density estimation (kcdf); expression level statistic ranking: ranking the gene expression level statistics of each sample; gene set ranking statistic calculation and GSVA enrichment score calculation.

[0043] In some embodiments, the judgment threshold in step 3) is 0, and the judgment criteria include when the score is ≤0, it is judged that the subject has no response to the chemoradiotherapy; when the score is >0, it is judged that the subject has responded to the chemoradiotherapy, and the higher the score, the better the chemoradiotherapy effect of the subject.

[0044] According to another aspect of the present disclosure, a model for evaluating cancer prognosis is provided, the model comprising a transcript sequencing module, a quality control filtering module, a transcript quantification module, a scoring module and a judgment module.

[0045] According to another aspect of the present disclosure, a storage medium is provided, on which machine executable instructions are stored. When the machine executable instructions are executed, a machine can execute the method provided by the present disclosure.

[0046] According to another aspect of the present disclosure, there is provided a use of the gene marker described in the first aspect in preparing a kit for evaluating cancer prognosis.

[0047] According to another aspect of the present disclosure, provided is the use of the gene marker and the method in judging, diagnosing or predicting the prognosis of a cancer patient.

[0048] In some embodiments, the cancer patient has been or will be treated with radiation therapy and / or chemotherapy.

[0049] In some embodiments, the cancer patient has undergone or will undergo neoadjuvant chemoradiotherapy.

[0050] In some embodiments, the tumor comprises a solid tumor and / or a hematological tumor.

[0051] In some embodiments, the tumor is selected from one or more of liver cancer, colon cancer, rectal cancer, head and neck cancer, colorectal cancer, lung cancer, breast cancer, cervical cancer, ovarian cancer, pancreatic cancer, bile duct cancer, kidney cancer, glioma, liposarcoma, melanoma, lymphoma, lung cancer, squamous cell carcinoma, chondrosarcoma and fibrosarcoma.

[0052] In some embodiments, the cancer is colorectal cancer.

[0053] Beneficial effects of the present invention:

[0054] The method of using the gene markers provided by the present invention to judge the efficacy of radiotherapy and chemotherapy in cancer subjects has the following advantages: it is non-invasive to the patient and only requires a small amount of blood; it does not rely on tumor tissue samples, has high accuracy, high sensitivity and high specificity, and the samples used are easier to obtain; it is low cost; and it is easy to judge.

[0055] The gene marker and the method can be used for auxiliary diagnosis, which helps to preserve the patient's quality of life to the greatest extent while ensuring the tumor treatment effect, and improve the treatment effect of patients with poor prognosis as much as possible. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 The treatment patterns of the 26 patients collected are shown.

[0057] Figure 2 Heat maps of seven cellular modules based on the co-occurrence patterns of tumor-enriched cell subsets in tumor samples are shown.

[0058] Figure 3 Radar plots showing the abundance of the four cellular modules after treatment with different treatments.

[0059] Figure 4 The abundance of cell modules after different neoadjuvant treatments is shown.

[0060] Figure 5 The results show that c13-CD8-Tem-CCL4 cells are expressed in CD45 + The frequencies in cells (Pre n=26, nCT n=8, nRT n=5, nRCT n=9).

[0061] Figure 6 The expression of c13-CD8-Tem-CCL4 cells in CD45 cells in CR and non-CR patients before and after neoadjuvant chemoradiotherapy (nRCT) + The frequencies in cells (CR: anterior n = 7, posterior n = 5; non-CR: anterior n = 4, posterior n = 3).

[0062] Figure 7 The correlation between the frequency of c13-CD8-Tem-CCL4 and the magnitude of tumor shrinkage in nRCT-treated tumors is shown.

[0063] Figure 8 The c13-CD8-Tem-CCL4 cells (y-axis) are compared with other tumor-infiltrating CD8 + Comparison of the mean expression of each gene in T cells (x-axis).

[0064] Fig. 9 shows tumor infiltrating CD8 + Visualization of UMAP of T cell subsets (left) and IFNG gene expression distribution (right).

[0065] Fig.10 Shows tumor-infiltrating CD8 + Changes in IFNG expression in T cells.

[0066] Fig.11 The tumor-infiltrating CD8 + Changes in IFNG expression in T cells.

[0067] Fig.12 CytoSig predicts IFNG signal intensity in the tumor microenvironment (TME) of locally advanced rectal cancer (LARC) after different neoadjuvant treatments.

[0068] Fig.13 Figure 3 shows that CytoSig predicted the IFNG signal intensity in the tumor microenvironment of LARC complete response and incomplete response samples after nRCT treatment.

[0069] Fig.14 CD8 and IFNγ immunostaining before and after nRCT treatment shows IFNγ + CD8 +Distribution of T cells (white arrows) in tumor tissues and IFNγ in tumor samples before and after nRCT treatment + CD8 + Quantitative analysis of T cell density (n=13).

[0070] Fig.15 The expression of IFNγ in tumor tissues of CR and non-CR patients before and after nRCT treatment is shown. + CD8 + Distribution of T cells (white arrows) and quantitative analysis of their cell density (n=18).

[0071] Fig.16 The IFNG signature scores of colorectal cancer before and after neoadjuvant chemoradiotherapy from the public dataset GSE94104 are shown. TRG, tumor regression grade (the lower the TRG score, the better the patient response).

[0072] Fig.17 IFNG expression and IFNG signal signature scores before and after neoadjuvant chemoradiotherapy for colorectal cancer from the public dataset GSE15781 are shown.

[0073] Fig.18 The expression heat map of IFNG signal characteristics in various types of cells in peripheral blood samples of patients treated with different neoadjuvant methods is shown.

[0074] Fig.19 Box plots are shown showing the comparison of IFNG signal characteristics of PBMC samples of CR and non-CR patients before and after nRCT treatment (CR: n=7 before treatment, n=5 after treatment; non-CR: n=3 before treatment, n=3 after treatment).

[0075] Fig. 20 Shown is the scoring and comparison of IFNG signals in blood using an in-house bulk RNA-seq dataset from PBMC samples of CR and non-CR patients before and after nRCT treatment (pre n=3; non-CR: Post n=3; CR: Post n=5).

[0076] Fig.21 Shown is the receiver operating characteristic (ROC) curve of the performance of the PBMC IFNG signature score in distinguishing CR and non-CR patients based on in-house bulk RNA-seq data analysis.

[0077] Fig. 22 Shown is a comparison of scores of subjects with a poor response (non-CR) and complete response (CR) to neoadjuvant chemoradiotherapy using JAK1 and IRF1 scores.

[0078] Fig.23The ratio of false positives to true positives using JAK1 and IRF1 scoring is shown.

[0079] Fig.24 The comparison of scores between subjects with poor response (non-CR) and subjects with complete response (CR) to neoadjuvant chemoradiotherapy after treatment obtained by the control group scoring in Example 5 is shown. DETAILED DESCRIPTION

[0080] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with embodiments. The specific embodiments described herein are only used to explain the present invention and are not intended to constitute any limitation of the present invention. In addition, in the following description, the description of known structures and technologies is omitted to avoid unnecessary confusion of the concepts of the present disclosure. Such structures and technologies are also described in many publications.

[0081] definition

[0082] Unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as commonly used in the field to which the present invention belongs. For the purpose of interpreting this specification, the following definitions will apply, and where appropriate, terms used in the singular will also include the plural form, and vice versa.

[0083] As used herein, the articles "a," "an," and "an" include plural referents unless the context clearly dictates otherwise.

[0084] The expression "about" as used herein is understood by one of ordinary skill in the art and varies within a certain range depending on the context in which it is used. If one of ordinary skill in the art is not aware of the use of the term based on the context in which it is used, "about" will mean up to plus or minus 10% of the specified value.

[0085] The term "sample" as used herein refers to a composition obtained from or derived from a subject (e.g., an individual of interest) that contains cells and / or other molecular entities to be characterized and / or identified based on, for example, physical, biochemical, chemical and / or physiological characteristics. For example, a sample refers to any sample derived from a subject of interest that is expected or known to contain cells and / or molecular entities to be characterized. Samples include, but are not limited to, tissue samples (e.g., tumor tissue samples), primary or cultured cells or cell lines, and body fluid samples. Wherein tissue samples include, but are not limited to, tissues, paraffin sections, and their processed products. The body fluid samples include, but are not limited to, saliva, whole blood, serum, plasma, emulsion, urine, lumbar or ventricular CSF, lymph, prostatic fluid, semen, sputum, feces, tears, tumor cells, alveolar lavage fluid, sputum, pus, nasopharyngeal swabs, oral swabs, cerebrospinal fluid, pleural effusion, amniotic fluid, peritoneal fluid, aqueous humor, vitreous humor, vaginal discharge, and their processed products.

[0086] The term "subject" as used herein refers to any animal, including humans and non-human animals. Non-human animals include all vertebrates, for example, mammals, such as non-human primates (particularly higher primates), sheep, dogs, rodents (such as mice or rats), guinea pigs, goats, pigs, cats, rabbits, cows, and any livestock or pets; and non-mammals, such as chickens, amphibians, reptiles, etc. In a preferred embodiment, the subject is a human.

[0087] As used herein, the term "sequencing" refers to the process of determining the sequence (eg, the identity and order of monomeric units) of a biological molecule, eg, a nucleic acid such as DNA or RNA. Exemplary sequencing methods include, but are not limited to, targeted sequencing, single molecule real-time sequencing, exon or exome sequencing, intron sequencing, electron microscopy-based sequencing, panel sequencing, transistor-mediated sequencing, direct sequencing, random shotgun sequencing, Sanger dideoxy terminator sequencing, whole genome sequencing, hybridization sequencing, pyrophosphate sequencing, capillary electrophoresis, duplex sequencing, cycle sequencing, single base extension sequencing, solid phase sequencing, high throughput sequencing, massively parallel signature sequencing, emulsion PCR, low denaturation temperature co-amplification PCR (COLD-PCR), multiplex PCR, reversible dye terminator sequencing, paired end sequencing, near-term sequencing, exonuclease sequencing, ligation sequencing, short read sequencing, single molecule sequencing, sequencing by synthesis, real-time sequencing, reverse terminator sequencing, nanopore sequencing, 454 sequencing, Solexa Genome Analyzer sequencing, SOLiD™ sequencing, MS-PET sequencing, DNA nanoball sequencing (DNBSEQ), composite probe anchor polymerase sequencing (cPAS), and combinations thereof. In some embodiments, sequencing can be performed by a genetic analyzer, such as a genetic analyzer commercially available from Illumina, Inc., Pacific Biosciences, Inc., Applied Biosystems / Thermo Fisher Scientific, or Shenzhen MGI Intelligent Manufacturing Technology Co., Ltd., etc. For example, BGI DNBseq sequencing platforms such as BGISEQ-500, BGISEQ-50, MGISEQ-2000, MGISEQ-200, DNBSEQ-T7, DNBSEQ-G99, DNBSEQ-T20X2, or Illumina's HiSeq2000, HiSeq2500, HiSeq4000, HiSeqX10, NovaSeq6000, etc.

[0088] The term "colorectal cancer (CRC)" used in this article is one of the common digestive system malignancies that seriously affect human health and survival. It originates in the large intestine and includes colon cancer and rectal cancer. Colorectal cancer presents different clinical symptoms and signs due to its different locations, mainly abdominal pain, blood in the stool, abdominal mass, intestinal obstruction, etc., with or without systemic symptoms such as anemia, fever and weight loss.

[0089] The term "gene marker" as used herein is any gene whose expression level in a tissue or cell is altered compared to the expression level in a normal or healthy cell or tissue.

[0090] Those skilled in the art will recognize that the utility of the present invention is not limited to quantifying gene expression of any particular variant of the marker genes of the present invention.

[0091] The term "evaluation" as used herein refers not only to "judgment", but also includes "determination", "differentiation", "identification", "inspection", "detection" and "prediction", etc. It includes distinguishing / differentiation or assisting in distinguishing / differentiating cancer and benign lesions, judging / determining cancer and the degree of development of cancer, predicting the risk of cancer, treatment effect, prognosis and risk of recurrence. In some embodiments, the diagnosis includes distinguishing or assisting in distinguishing malignant tumors and benign lesions, judging the degree of development of cancer, or predicting the risk of cancer, treatment effect, prognosis and risk of recurrence.

[0092] The term "prognosis" as used herein refers to a prediction of the possible course and outcome of a clinical condition or disease. The prognosis of a patient is usually determined by evaluating factors or symptoms that symbolize a favorable or unfavorable disease course or outcome. The term "prognosis" does not mean that the course or outcome of a condition can be predicted with 100% accuracy. On the contrary, it will be understood by those skilled in the art that the term "prognosis" refers to an increased probability that a particular process or outcome will occur; that is, the process or outcome is more likely to occur in a patient who exhibits a given condition than in an individual who does not exhibit the condition. Cancer prognosis can be the degree of response of a cancer patient to chemoradiotherapy, such as a good response or a poor response, including neoadjuvant chemoradiotherapy.

[0093] The term "response" as used herein may refer to a change in a subject's condition that occurs due to treatment or in association with treatment. In some embodiments, the response is or includes a beneficial response. In some embodiments, a beneficial response may include stabilization of the condition (e.g., prevention or delay of a deterioration that is expected or usually observed to occur in the absence of treatment), alleviation of one or more symptoms of the condition (e.g., reduction in frequency and / or intensity), and / or improvement in the prospects for cure of the condition, etc. In some embodiments, "response" may refer to the response of an organism, an organ, a tissue, a cell or a cell component, or an in vitro system. In some embodiments, the response is or includes a clinical response. In some embodiments, the presence, extent, and / or nature of the response may be measured and / or characterized according to specific criteria; in some embodiments, such criteria may include clinical criteria and / or objective criteria. In some embodiments, the techniques for assessing the response may include, but are not limited to, clinical examination, positron emission tomography, chest X-ray CT scan, MRI, ultrasound, endoscopy, laparoscopy, the presence or level of specific markers in a sample, cytology, and / or histology. When the response of interest is or includes the response of a tumor to a therapy, one of ordinary skill will be aware of a variety of established techniques for assessing such a response, including, for example, determining tumor burden, tumor size, tumor stage, etc. In light of the present disclosure, one of ordinary skill in the art will be aware of and / or will appreciate strategies for determining specific response criteria for individual tumors, tumor types, patient populations or groups, etc., as well as for determining appropriate references thereof.

[0094] The term "treatment" as used herein refers to the sequential or simultaneous administration of an anti-tumor agent, and / or an anti-vascular agent, and / or an anti-stromal agent, and / or an immunostimulatory agent or inhibitor, and / or a blood cell proliferation agent, and / or radiotherapy, and / or hyperthermia, and / or cryotherapy for cancer treatment. These administrations can be performed in an adjuvant and / or neoadjuvant mode. The composition of such a regimen may vary depending on the dose of each single agent, the time range of application, and the frequency of administration that defines the therapeutic window. Various combinations of various drugs and / or physical methods, as well as various schedules, are currently being studied.

[0095] As used herein, the term "chemotherapy" has its ordinary meaning in the art and refers to a treatment that includes administering a chemotherapeutic agent to a patient.

[0096] The term "radiotherapy" as used herein has its general meaning in the art and refers to treatment using ionizing radiation. Ionizing radiation deposits energy, which injures or destroys cells in the area being treated (target tissue) by destroying the genetic material of the cells, making it impossible for these cells to continue to grow. A commonly used radiotherapy involves photons, such as X-rays. Depending on the amount of energy they have, rays can be used to destroy cancer cells on the surface of the body or deep in the body. The higher the energy of the X-ray beam, the deeper the depth of the X-rays into the target tissue. Linear accelerators and betatrons can produce X-rays with increasing energy. Using a machine to focus radiation (such as X-rays) on the cancer site is called external irradiation radiotherapy. Gamma rays are another form of photons used in radiotherapy. Gamma rays are spontaneously generated because some elements (such as radium, uranium and cobalt 60) release radiation when they decompose or decay. In some embodiments, radiotherapy is external radiotherapy. Examples of external radiation therapy include, but are not limited to, conventional external beam radiation therapy; three-dimensional conformal radiation therapy (3D-CRT), which sends shaped beams of radiation from different directions to closely follow the shape of the tumor; intensity modulated radiation therapy (IMRT), such as helical tomotherapy, which shapes the radiation beam to closely follow the shape of the tumor and also varies the radiation dose based on the shape of the tumor; conformal proton beam radiation therapy; image-guided radiation therapy (IGRT), which combines scanning and radiation techniques to provide real-time images of the tumor to guide radiation therapy; intraoperative radiation therapy (IORT), which delivers radiation directly to the tumor during surgery; stereotactic radiosurgery, which can deliver large, precise radiation doses to small tumor areas in a single treatment; hyperfractionated radiation therapy, such as continuous hyperfractionated accelerated radiation therapy (CHART), in which the subject is given more than one radiation therapy treatment (fractionation) per day; and hypofractionated radiation therapy, in which the dose of each radiation treatment is larger, but the number of radiation treatments is smaller.

[0097] As used herein, the term "neoadjuvant therapy" refers to a preoperative treatment regimen, which may generally include, for example, chemotherapy, radiation therapy, targeted therapy, hormonal therapy, and / or immunotherapy, which is intended to shrink the primary tumor so that local treatment (e.g., surgery) is less destructive or more effective, or to enable surgery-sparing or organ-sparing.

[0098] The term "transcripts per million (TPM)" used in this article is a normalized expression that divides the expression of each gene by the total number of transcripts and multiplies it by a normalization factor, usually 1,000,000. The probability distribution of TPM is continuous because it is a measure of relative expression and can take any real value. The advantage of TPM is that it can compare the expression levels of different genes in the same sample, as well as the expression differences between different samples, without being affected by gene length and sequencing depth.

[0099] Examples and drawings are provided below to help understand the present invention. However, it should be understood that these examples and drawings are only used to illustrate the present invention, but do not constitute any limitation. The actual protection scope of the present invention is set forth in the claims. It should be understood that any modifications and changes can be made without departing from the spirit of the present invention.

[0100] Example

[0101] Materials and methods:

[0102] 1) Sample collection

[0103] This study was approved by the research and ethics committees of Shenzhen Bay Laboratory (SZBL) (no.YL2023-005) and Sun Yat-sen University Cancer Center (SYSUCC) (no.B2022-557-01). Twenty-six patients with locally advanced rectal cancer (LARC) with pathological diagnosis of microsatellite stable / mismatch repair normal (MSS / pMMR) from Sun Yat-sen University Cancer Center were included in this study. The patients were divided into 4 groups based on the treatment method: Pre group (before treatment), nCT group, nRT group and nRCT group ( Figure 1 Patients in the nCT group received neoadjuvant chemotherapy with the FOLFOXIRI regimen (oxaliplatin 85 mg / m2 intravenously every 14 days). 2 irinotecan 150 mg / m 2 , folinic acid 400 mg / m 2 , and fluorouracil 2400 mg / m 2 Patients in the nRT group received short-course radiotherapy (5 × 5 Gy), and patients in the nRCT group received short-course radiotherapy (5 × 5 Gy) and an additional 6–8 cycles of FOLFOXIRI within 11–18 days after the last radiotherapy.

[0104] All patients underwent routine colonoscopy and biopsy, and tumor tissue was collected by colonoscopic biopsy less than 2 weeks before treatment ( Figure 1 ). After the respective neoadjuvant treatment, paired fresh tumor tissues were collected by surgical resection or endoscopic biopsy. Among them, neoadjuvant radiotherapy (nRT) was collected in the treatment period before nRCT, and chemotherapy was continued because the efficacy was not ideal. In summary, neoadjuvant chemoradiotherapy (nRCT) had the best treatment effect. This study was approved by the Research and Ethics Committee of the Cancer Center of Sun Yat-sen University (No.B2022-557).

[0105] Before and after treatment, peripheral blood was collected from each eligible patient, 1-10 mL of whole blood was drawn, and EDTA was used as an anticoagulant.

[0106] 2) PBMC Isolation

[0107] PBMCs were isolated from peripheral blood using Ficoll density gradient centrifugation. In short, Ficoll was added to a 15 mL centrifuge tube according to the amount of whole blood, and then whole blood was slowly added. Centrifuge at room temperature for 30 min with an acceleration speed of 1, a deceleration speed of 0, and 800 g. After centrifugation, the middle buffy coat layer was taken and washed with PBS containing 2% fetal bovine serum to remove Ficoll. Red blood cells were removed using red blood cell lysis buffer (Miltenyi Biotec, 130-095-929). The number of PBMCs was counted using a Countstar cell counter.

[0108] 3) Isolation of single cells from tumor tissue

[0109] The patient's tumor tissue was weighed and isolated using a human tumor dissociation kit (Miltenyi Biotec, 130-095-929). Briefly, the tumor tissue was cut into small pieces in a C tube (Miltenyi Biotec, 130-093-237), and the three dissociation enzymes in the kit were added according to the weight of the tumor tissue. The C tube was placed in a gentesmacs with a heater. TM Tissue dissociation was performed on an Octo dissociator using the 37C_h_TDK_1 program for 1 h. After dissociation, cells were filtered with a 70 μm filter and then washed with 10 mL of PBS containing 2% FBS. Cells were centrifuged at 300 g for 10 min, and cell pellets were collected and counted by trypan blue staining.

[0110] 4) Preparation and sequencing of single-cell RNA libraries

[0111] Cell pellets obtained from PBMCs and tumor tissues were resuspended in sorting buffer (PBS containing 1% FBS) and then labeled with DAPI, anti-CD45, and anti-CD235a antibodies and analyzed using a BD FACSAria TM III instrument (BD Biosciences) for cell sorting. DAPI negative cells are viable cells, and DAPI in PBMCs is sorted separately. - CD235a - CD45 + DAPI in cell and tumor tissue cell suspension - CD235a - of cells.

[0112] 10×Chromium Single cell 5`Library (10×Genomics, PN-1000263) was used to load 10,000 to 20,000 cells to prepare for library construction according to the instructions. The purified library was constructed according to the instructions using the library construction kit (10×Genomics, PN-1000190), and then sequenced using the BGI MGISEQ-2000 sequencer (BGI, 1000019023). The TCR sequence was amplified using the Chromium Single Cell Human TCRAmplification Kit (10×Genomics, PN-1000252), and the V(D)J library was constructed using the library construction kit (10×Genomics, PN1000190) and sequenced by the Illumina Novasek6000 sequencer.

[0113] Unless otherwise stated, single-cell sequencing was used in the examples.

[0114] 5) Bulk RNA library preparation and sequencing

[0115] Using TRIzol TM Total RNA was isolated from each PBMC sample using Sigma-Aldrich Reagent (Invitrogen, USA). RNA quality was then tested by gel electrophoresis and using Qubit (Thermo, Waltham, MA, USA). Libraries were constructed using VAHTS Stranded mRNA-seq Library Prep Kit for Illumina (Vazyme, China) and sequenced using an Illumina Novasek 6000 instrument.

[0116] 6) Single-cell RNA-seq data preprocessing

[0117] The generated scRNA-seq data were aligned and quantified with the GRCh38 human reference genome, and the generated characteristic barcode matrix was used for downstream filtering and analysis. Filtering was performed using a detection threshold of 200-8,000 genes and 500-100,000 unique molecular identifiers (UMIs), retaining high-quality cells and removing cells with more than 40% mitochondrial gene content and low-expressed genes (less than 3 cells detected).

[0118] Throughout downstream analysis, doublets were removed based on unexpected co-expression of typical markers such as LYZ (myeloid marker) and MS4A1 (B cell marker). Gene expression for each cell was then normalized and log-transformed. Cell cycle scores were calculated using the score_genes_cell_cycle function of Scanpy (version 1.9.1).

[0119] 7) Dimensionality reduction, batch correction and unsupervised clustering

[0120] The scanpy.pp.highly_variable_genes function (batch_key = 'sample', n_top_genes = 4000) was used to detect highly variable genes (HVGs) in each sample, and then merged to avoid the influence of batch effect. The scanpy.tl.pca function (svd_solver = 'arpack', n_comps = 50) was then used to perform principal component analysis (PCA) on the HVG matrix.

[0121] Use scanpy.external.pp.bbknn (version 1.5.1, default parameters) to remove the batch effect of the data. Use the unified manifold approximation and projection (UMAP) implemented in the scanpy.tl.umap function to further reduce the dimension and visualize it.

[0122] For the first round of unsupervised clustering, CD8 + T cells, CD4 + Seven major cell types were annotated, including T cells, gdT, ILC, myeloid cells, B cells, and non-immune cells. Next, we performed a second round of clustering in each major cell type to obtain the subpopulation structure. It is worth noting that during the clustering process, double cell exclusion was performed based on the unexpected co-expression of typical markers such as LYZ (myeloid cell marker) and MS4A1 (B cell marker).

[0123] In epithelial cells, malignant cells were identified based on copy number variation using Numbat (version 1.2.2), which integrates information from gene expression, allele ratios, and haplotypes.

[0124] 8) Differential expression analysis

[0125] The nonparametric Wilcoxon rank-sum test was used to find the differentially expressed genes between the two types of cells, and the p-values ​​were corrected by Bonferroni according to the total number of genes.

[0126] 9) Calculation of gene signature scores

[0127] To assess the functional dynamics of cell subpopulations after treatment, we collected a series of gene signatures. The signature score of the gene set in the cell was calculated using the scanpy.tl.score_genes function, which is the mean expression of a set of genes minus the mean expression of a set of reference genes.

[0128] 10) Quantitative analysis of cell distribution

[0129] To characterize the distribution of cell subsets and determine the distribution of cells of a certain subset in a specific tissue or treatment time point, we calculated the odds ratio (OR) described in previous studies. For a given cell subset, an OR>1 indicates that the cell subset is observed more frequently in a specific situation than in other situations (i.e., enriched), and an OR<1 indicates that the cell subset is observed less frequently in a specific situation than in other situations (i.e., depleted).

[0130] 11) Identification of cell modules

[0131] To systematically investigate the potential dynamics of the TME during treatment, we explored the co-occurrence patterns between different cell subpopulations. The Spearman's rank correlation coefficient (Spearman'sρ) of the frequency of each cell subpopulation in all tumor samples was calculated. Unsupervised hierarchical clustering of cell subpopulations based on these correlation values ​​was then performed using the hclust function (method = 'complete'), and the results were visualized using the ComplexHeatmap package (version 2.10.0). In order to ensure the accuracy of the clustering results and avoid possible distortion caused by the low frequency of some subpopulations, the tumor-enriched cell subpopulations determined by the above ORs were retained for cell module analysis. Seven highly correlated cell modules were finally identified. The abundance of the cell module in each sample was calculated as the average proportion of the subpopulations belonging to that cell module.

[0132] 12) Prediction of cytokine signaling activity

[0133] CytoSig (version 0.1) was used to predict the cytokine signaling activity for each sample. We calculated the average gene expression for each sample. Next, we calculated the cytokine signaling activity using the CytoSig.Ridge_significance_test function. To address the signal redundancy problem, CytoSig uses ridge regression to find features in the transcriptome that can be attributed to the effects of cytokines on the expression of their target genes.

[0134] 13) Batch RNA-seq data preprocessing

[0135] Batch RNA-seq data were filtered using Skewer (version 0.2.2). Adapter sequences and low-quality reads (<30) were removed from the 3' end. Kallisito (version 0.44) was used for quantification, and the transcript expression level (TPM) of the same gene was aggregated and calculated using tximport (version 1.22.0) software, and the TPM was logarithmically transformed.

[0136] 14) Microarray Data Analysis

[0137] To validate the finding that higher levels of IFNG were associated with nRCT response, we collected published clinically annotated microarray data from patients with advanced colorectal cancer who underwent RCT. Two datasets (GSE94104, GSE15781) were downloaded from the Gene Expression Omnibus database using the getGEO function in GEOquery (version 2.62.2). Raw gene expression values ​​from microarray experiments were background corrected, log-transformed, and quantile normalized using the Robust Multi-array Average (RMA) algorithm. For each sample, expression measurements of all probes corresponding to the same gene ID were averaged to obtain a single measurement. We used the GSVA package (version 1.42.0) to calculate the feature score of the gene set “REACTOME_INTERFERON_GAMMA_SIGNALING” as an indicator of IFNG response.

[0138] 15) Multiple immunofluorescence staining

[0139] Paraffin-embedded tissues were cut into 5 μm thick histological sections and stained using the PanoPANEL kit (Panovue) based on Tyramide signal amplification (TSA) technology according to the instructions. The slides were first baked at 60°C for 1 h, dewaxed in xylene, and then rehydrated twice in 100% alcohol, 95% and 70% alcohol. Antigens were extracted by blocking with citrate buffer (PH 6.0) for 30 min under heat induction. The blocking buffer was removed and the slides were incubated with primary antibodies, including CD8 (zsbb-bio, ZA-0508) and IFNγ (Abcam, ab231036) antibodies for 1 h at room temperature. They were washed three times with 1× TBST buffer (tris-buffered saline, containing 0.1% Tween20) and incubated with HRP-labeled secondary antibodies for 15 min at room temperature. TSA working solution diluted 1:100 (Panovue) was used for incubation at room temperature for 10 min, and then microwave heating was performed to achieve signal amplification. Repeat this process until all antibodies are stained. Finally, use DAPI to stain the nucleus. Use OlympusVS200 to scan the stained slides and analyze them with OlyVIA analysis software (version 4.1).

[0140] Example 1. Transcriptome analysis of cells after neoadjuvant therapy found that the M2 module containing c13 was significantly increased after nRCT treatment

[0141] In order to better understand the changes in the tumor microenvironment (TME) of patients with locally advanced rectal cancer (LARC) after neoadjuvant therapy, this example performed cell modular analysis on the single-cell transcriptome data of tumor tissue.

[0142] The results revealed seven different cell modules (M1 to M7) with different subpopulation characteristics ( Figure 2 ). M1 exhibits exhaustive and anti-inflammatory properties, represented by c14-CD8-Tex-CXCL13 and c27-CD4-Treg-TNFRSF9. M2 exhibits effector and pro-inflammatory characteristics, including c13-CD8-Tem-CCL4 and c40-cDC1-CLEC9A cells. M3 appears to be associated with tertiary lymphoid structures (TLS) characterized by germinal center B cells (c63-GCB-LRMP) and T follicular helper cells (c24-CD4-Tfh-CXCL13). M4 exhibits certain regulatory characteristics, represented by c29-CD4-Treg-KLRB1 and c26-CD4-Th17-IL17A. M6 includes CD4 and CD8 memory subsets. In addition, M5 is dominated by stromal cell clusters with related features of angiogenesis and adhesion. M7 includes mast cell and fibroblast populations and is associated with mucosal immunity.

[0143] Next, we evaluated the TME changes induced by different treatments at the cell module level. The results showed that the neoadjuvant chemoradiotherapy (nRCT) group with the best treatment effect had fewer M1 features and more M2 features ( Figure 3 and Figure 4 ). The pro-inflammatory CD8 cell cluster c13-CD8-Tem-CCL4 ( Figure 2 ).

[0144] Example 2. Correlation between c13-CD8-Tem-CCL4 cell subsets and nRCT response

[0145] The analysis of the c13-CD8-Tem-CCL4 cell subset showed that this subset decreased slightly after nRT, but increased significantly after nRCT ( Figure 5 ), especially in patients with complete response (CR), but not in patients with non-complete response (non-CR) ( Figure 6 Importantly, the proportion of the c13-CD8-Tem-CCL4 cell subset in tumor tissue cells was positively correlated with the degree of tumor shrinkage. The higher the proportion of the c13-CD8-Tem-CCL4 cell subset, the greater the tumor shrinkage ( Figure 7 ).

[0146] Compared with other tumor-infiltrating CD8 + Compared with T cells, the c13-CD8-Tem-CCL4 cell subset highly expressed the IFNG gene ( Figure 8 and Fig. 9 ). And in the neoadjuvant chemoradiotherapy (nRCT) group with the best treatment effect, CD8 + T cells expressed the highest level of IFNG gene ( Fig.10 ), especially in patients with complete response (CR) after nRCT treatment, CD8 + The expression of IFNG gene in T cells increased significantly ( Fig.11 ).

[0147] Accordingly, analysis of IFNG gene activity in the tumor microenvironment (TME) revealed that IFNG activity was highest in the neoadjuvant chemoradiotherapy (nRCT) group ( Fig.12 ), especially in patients with complete response (CR) ( Fig.13 ).

[0148] In summary, it can be concluded that CD8 + The expression level of IFNG gene in T cells and the activity of IFNG gene in the tumor microenvironment may be used as indicators to judge the response of LARC patients to neoadjuvant chemoradiotherapy.

[0149] Example 3. Further verification of the correlation between IFNG and nRCT response

[0150] In order to further verify the correlation between c13-CD8-Tem-CCL4 cell subsets and IFNG and nRCT response, this example used multi-color immunofluorescence staining using tumor tissue sections of patients before and after treatment.

[0151] The results showed that IFNγ in tumor tissue after neoadjuvant chemoradiotherapy (nRCT) + CD8 T cells increased significantly ( Fig.14 ), especially in patients with complete response (CR) ( Fig.15 ).

[0152] Next, we used IFNG and IFNG signaling pathway-related gene sets (IFNG signaling signature) and public data GSE94104 and GSE15781 for validation. Consistent with the above results, the expression levels of IFNG genes and genes related to their signaling pathways in the tumor microenvironment increased significantly after neoadjuvant chemoradiotherapy ( Fig.16 and 17 ), and the better the patient's treatment effect, the stronger the expression level ( Fig.16 ).

[0153] Produced IFNG genes containing specific genes: CD44, IFNGR1, PIAS1, CAMK2B, SP100, CAMK2A, OAS1,ICAM1,JAK2,SMAD7,MID1,TRIM35,TRIM14,TRIM2,TRIM3,OAS3,OAS2, IFNG,PTPN6,TRIM38,STAT1,SUMO1,TRIM62,GBP3,GBP1,IRF6,TRIM25,TRI M6,MT2A,IRF1,IRF3,IRF5,TRIM21,TRIM5,TRIM22,TRIM45,OASL,IRF4,TRI M26,TRIM10,TRIM31,HLA-F,TRIM29,PML,IRF8,CAMK2D,CAMK2G,NCAM1,TR IM48,FCGR1A,GBP5,IFNGR2,JAK1,GBP2,GBP4,VCAM1,TRIM17,TRIM46,PRKC D,B2M,TRIM68,IRF2,HLA-DPA1,PTAFR,TRIM8,PTPN2,PTPN11,HLA-DQB1,C IITA,GBP6,SOCS3,SOCS1,IRF7,HLA-DRB3,HLA-DRB1,PTPN1,HLA-DQB2,HLA -DQA1,FCGR1BP,MLA-DRB5,MLA-DRA,MLA-C,MLA-E,TRIM10,TRIM31,GLA-G ,HLA-F,HLA-DPA1,HLA-DQA2,HLA-DQA1,HLA-DRA,HLA-H,HLA-B,HLA-E,HLA -A,THE-A,THE-G,THE-F,GBP7,IRF9,THE-DPB1,IFI30,TRIM31,THE-B,THE -DQA2,HLA-DPB1,HLA-A,HLA-DPA1,TRIM31,HLA-DQB2,HLA-B,HLA-DQA2,TR IM31,HLA-E,HLA-C,HLA-DQB1,TRIM26,HLA-DQB2,HLA-DRA,HLA-DPB1,HLA -DRB4,TRIM10,HLA-DRA,HLA-DRB1,HLA-DPA1,HLA-DQB2,HLA-C,HLA-DQB2, TRIM26,THE-B,THE-DRA,THE-E,TRIM10,TRIM10,THE-DQB2,THE-DPA1,THE- F,TRIM26,HLA-E,HLA-G,HLA-DQB2,HLA-DRA,HLA-DPB1,TRIM26,HLA-DRB4HLA-DQB1, HLA-DPA1, HLA-DQA2, TRIM26, HLA-DRB3, HLA-DQA2, HLA-A, HLA-DQA1, HLA-B, HLA-DQ B2, HLA-G, HLA-DQA2, HLA-DQB1, TRIM31, HLA-E, TRIM26, TRIM26, HLA-B, HLA-DRA, TRIM10, HLA-F , HLA-G, HLA-A, HLA-G, HLA-DPA1, HLA-DPA1, HLA-E, HLA-DPB1, HLA-C, TRIM10, HLA-G, HLA-F, HLA -DQA2, TRIM10, HLA-DPB1, HLA-DQA2, TRIM34, IFNGR2, B2M, HLA-G, IRF7, HLA-DRA, IRF9, TRIM17. ,

[0154] We further analyzed the peripheral blood samples of patients before and after neoadjuvant treatment and found that after neoadjuvant chemoradiotherapy (nRCT), various immune cells in the peripheral blood of patients were enriched with IFNG signaling pathway-related gene set signal characteristics ( Fig.18 ), which was significantly increased in patients with complete response (CR) ( Fig.19 ).

[0155] Example 4. LARC peripheral blood bulk RNA sequencing (bulk RNA sequencing)

[0156] In order to further expand clinical applications and reduce testing costs, we performed lower-cost bulk RNA sequencing on peripheral blood samples of LARC patients to explore whether bulk RNA data has the same characteristics as single-cell transcriptome data.

[0157] The analysis found that in the batch sequencing RNA data, the signal characteristics of the gene set related to the IFNG signaling pathway also increased significantly in patients with complete response (CR) after neoadjuvant chemoradiotherapy ( Fig. 20 ). In addition, AUC analysis revealed that the signal characteristics of the IFNG signaling pathway-related gene set in peripheral blood can well (AUC = 0.8) distinguish patients with complete response and incomplete response to neoadjuvant chemoradiotherapy ( Fig.21 ).

[0158] Example 5. Screening of biomarker combinations to determine the efficacy of neoadjuvant chemoradiotherapy for colorectal cancer

[0159] In order to further improve the prediction effect, the gene set related to the IFNG signaling pathway was screened.

[0160] Eight patients with colorectal cancer from the Cancer Center of Sun Yat-sen University received neoadjuvant chemoradiotherapy before surgery: first, they received a short course of radiotherapy (5×5Gy) for up to 8 days. Chemotherapy was started within 11-18 days after the last radiotherapy, and at least within 4 weeks, they received 4-6 cycles of mFOLFOXIRI treatment (oxaliplatin (85mg / m2) and folinic acid protein (400mg / m2)), intravenous infusion for 2h / d1, followed by irinotecan (150-165mg / m2) intravenous infusion for 90min / d1, and fluorouracil (2400-2800mg / m2) continuous intravenous infusion for 46h / d1-d2). Treatment was discontinued when intolerable toxicity, disease progression, or other conditions that the doctor believed were not suitable for continuing chemotherapy occurred. Venous blood was collected from patients with colorectal cancer within a few days to months after neoadjuvant chemoradiotherapy, and sequencing libraries were constructed and analyzed.

[0161] GSVA was used to score the gene set related to the IFNG signaling pathway: Based on the REACTOME_INTERFERON_GAMMA_SIGNALING gene set of MSigDB, LASSO regression was performed to screen two genes JAK1 and IRF1 related to the IFNG signaling pathway as the gene set for scoring; the control group used all genes in the REACTOME_INTERFERON_GAMMA_SIGNALING gene set for scoring;

[0162] The GSVA scoring steps are as follows:

[0163] 1) Kernel density estimation (kcdf): First, use all samples (here, the reference sample plus the new test sample) to perform kernel density estimation of the cumulative density function;

[0164] 2) Ranking of expression level statistics: Ranking the gene expression level statistics of each sample;

[0165] 3) Calculation of Kolmogorov-Smirnov-like ranking statistics of gene sets: For each gene set, calculate the Kolmogorov-Smirnov-like ranking statistics;

[0166] 4) GSVA enrichment score calculation, the output of the algorithm is a matrix containing the pathway enrichment scores for each gene set and sample;

[0167] A high score means that chemotherapy is effective for the patient. The tool uses default parameters. The specific principle and code can be found at: https: / / github.com / rcastelo / GSVA; the results of JAK1 and IRF1 scoring are shown in Table 1 and Figures 22-23 As shown, compared with Fig.24 shown.

[0168] Table 1. Prediction results of JAK1 and IRF1 in determining the efficacy of neoadjuvant chemoradiotherapy in colorectal cancer

[0169] Patient number Patient staging Response to neoadjuvant chemoradiotherapy Prediction score RC15 T2N+ CR 0.75 RC16 T3N+ CR 0.46 RC20 T3N+ CR 0.13 RC21 T3N+ CR 0.79 RC22 T4bN+ CR 0.47 RC24 T2N+ Non-CR -0.75 RC25 T3N+ Non-CR -0.65 RC26 T3N+ Non-CR -0.55

[0170] As shown in Table 1, patients RC24, RC25, and RC26 had a poor response to neoadjuvant chemoradiotherapy (incomplete response, non-CR), and the scores of JAK1 and IRF1 after sequencing were below 0; patients RC15, RC16, RC20, RC21, and RC22 had a good response to neoadjuvant chemoradiotherapy (complete response, CR), and the scores of JAK1 and IRF1 after sequencing were above 0.

[0171] like Fig. 22 As shown, patients with a complete response to neoadjuvant chemoradiotherapy had significantly increased scores compared with patients with a poor response (non-CR) after neoadjuvant chemoradiotherapy.

[0172] like Fig.23 As shown, ROC-AUC = 1, which is an indicator that can well distinguish patients with complete response and incomplete response (the higher the AUC, the better, with the highest being 1).

[0173] The above-mentioned good response to neoadjuvant chemoradiotherapy includes pathological complete response (pCR) for the surgical group, which is defined as no residual viable tumor cells in the primary tumor or cleared lymph nodes by pathological examination after radical surgery; and clinical complete response for the non-surgical group, which is defined as ① no irregularities or palpable tumors in rectal examination; ② no visible lesions except flat scars, telangiectasia or mucosal blanching in endoscopic examination; additional criteria: ③ magnetic resonance imaging (MRI) or endorectal ultrasound (ERUS) imaging shows no residual tumor in the primary site and draining lymph nodes; ④ scar biopsy is negative; ⑤ the initially elevated CEA level returns to normal (<5ng / mL). The opposite is a poor response.

[0174] Statistical results show that this indicator can well distinguish patients who respond to and do not respond to neoadjuvant chemoradiotherapy.

[0175] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention is described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A gene marker associated with the prognosis of cancer patients, characterized in that: The gene markers include one or more genes related to the IFNG signaling pathway.

2. The gene marker according to claim 1, characterized in that The gene markers include one or more of the following genes: JAK1 and IRF1; Preferably, the gene markers further include one or more of the following genes: CD44, IFNGR1, PIAS1, CAMK2B, SP100, CAMK2A, OAS1, ICAM1, JAK2, SMAD7, MID1, TRIM35, TRIM14, TRIM2, TRIM3, OAS3, OAS2, IFNG, PTPN6, TRIM38, STAT1, SUMO1, TRIM62, GBP3, GBP1, IRF6, TRIM25, TRIM6, MT2A, IRF3, IRF5, TRIM21, TRIM5, TRIM22, TRIM45, OASL, IRF4, TRIM26, TRIM10, TRIM31, HLA-F, TRIM29, PML, IRF8, CAMK2D, CAMK2G, NCAM1, TRIM48, FCGR1A, GBP5, IFNGR2, GBP2, GBP4, VCAM1, TRIM17, TRIM46, PRKCD, B2M, TRIM68, IRF2, HLA-DPA1, PTAFR, TRIM8, PTPN2, PTPN11, HLA-DQB1, CIITA, GBP6, SOCS3, SO CS1, IRF7, HLA-DRB3, HLA-DRB1, PTPN1, HLA-DQB2, HLA-DQA1, FCGR1BP, HLA-DRB5, HLA-DRA, HLA-C, HLA-E, TRIM10, TRIM31, HLA-G, HLA-F, HLA-DPA 1. HLA-DQA2, HLA-DQA1, HLA-DRA, HLA-H, HLA-B, HLA-E, HLA-A, HLA-A, HLA-G, HLA-F, GBP7, IRF9, HLA-DPB1, IFI30, TRIM31, HLA-B, HLA-DQA2, HLA- DPB1, HLA-A, HLA-DPA1, TRIM31, HLA-DQB2, HLA-B, HLA-DQA2, TRIM31, HLA-E, HLA-C, HLA-DQB1, TRIM26, HLA-DQB2, HLA-DRA, HLA-DPB1, HLA-DRB4, TRIM10, HLA-DRA, HLA-DRB1, HLA-DPA1, HLA-DQB2, HLA-C, HLA-DQB2, TRIM26, HLA-B, HLA-DRA, HLA-E, TRIM10, TRIM10, HLA-DQB2, HLA-DPA1, HLA- F, TRIM26, HLA-E, HLA-G, HLA-DQB2, HLA-DRA, HLA-DPB1, TRIM26, HLA-DRB4, HLA-DQB1, HLA-DPA1, HLA-DQA2, TRIM26, HLA-DRB3, HLA-DQA2, HLA-A, HLA-DQA1, HLA-B, HLA-DQB2, HLA-G, HLA-DQA2, HLA-DQB1, TRIM31, HLA-E, TRIM26, TRIM26, HLA-B, HLA-DRA, TRIM10, HLA-F, HLA-G, HLA-A, HLA-G, HLA-DPA1, HLA-DPA1, HLA-E, HLA-DPB1, HLA-C, TRIM10, HLA-G, HLA-F, HLA-DQA2, TRIM10, HLA-DPB1, HLA-DQA2, TRIM34, IFNGR2, B2M, HLA-G, IRF7, HLA-DRA, IRF9, and TRIM17; Preferably, the cancer patient has undergone or will undergo radiotherapy and / or chemotherapy, more preferably neoadjuvant chemoradiotherapy; Preferably, the tumor comprises a solid tumor and / or a blood tumor; Preferably, the tumor is selected from one or more of liver cancer, colon cancer, rectal cancer, head and neck cancer, colorectal cancer, lung cancer, breast cancer, cervical cancer, ovarian cancer, pancreatic cancer, bile duct cancer, kidney cancer, glioma, liposarcoma, melanoma, lymphoma, lung cancer, squamous cell carcinoma, chondrosarcoma and fibrosarcoma.

3. A detection kit for evaluating cancer prognosis, characterized in that: The kit comprises: a reagent for detecting the gene marker according to claim 1 or 2.

4. The kit according to claim 3, characterized in that The reagents for detecting the gene markers include primer pairs or probes for the gene markers; Preferably, the probe is a fluorescent probe or a hybridization probe.

5. A method for evaluating cancer prognosis, comprising the step of detecting using the gene marker according to claim 1 or 2, Preferably, the method comprises the following steps: 1) performing quality control and filtering on the transcriptome sequencing data of the samples of the cancer subjects to obtain filtered sequencing data; 2) Quantify transcripts from the filtered sequencing data; 3) Scoring the gene markers according to claim 1 or 2, and evaluating cancer prognosis based on the scores.

6. The method according to claim 5, characterized in that The sample in step 1) includes a body fluid sample or a tissue sample; Preferably, the body fluid sample includes: saliva, whole blood, serum, plasma, milk, urine, lumbar or ventricular CSF, lymph fluid, prostatic fluid, semen, sputum, feces, tears, tumor cells, alveolar lavage fluid, sputum, pus, nasopharyngeal swab, oral swab, cerebrospinal fluid, pleural effusion, amniotic fluid, peritoneal fluid, aqueous humor, vitreous humor, vaginal discharge and their processed products; and / or, The tissue samples include tissues, paraffin sections and their processed products.

7. The method according to claim 5, characterized in that The quality control in step 1) includes evaluating any one or more of the following: evaluating one or more of the following data: the ratio of repeated sequences, the alignment ratio, the unique alignment ratio, the exon alignment ratio, the intron alignment ratio, the intergenic region alignment ratio, the expression efficiency, the detected transcripts, the detected genes or the sequence coverage uniformity; Preferably, the filtering in step 1) includes removing the adapter sequence and low-quality reads from the 3' end; Preferably, the quality control and filtration in step 1) adopt one or more of Trimmomatic, Skewer and Fastp; Preferably, the quantification in step 2) uses one or more of the following software: RSEM, eXpress, HTseq, Cufflinks, StringTie, Sailfish, Salmon, quasimapping or Kallisto; Preferably, the quantification in step 2) is performed based on sequencing data; Preferably, the quantitative result obtained in step 2) includes the TPM or logarithmic TPM of each transcript; Preferably, the quantification in step 2) is obtained using tximport software; Preferably, the scoring in step 3) is performed using a gene set variation analysis method; Preferably, the threshold value of the judgment in step 3) is 0, and the judgment criteria include when the score is ≤0, it is judged that the subject has no response to the chemoradiotherapy; when the score is >0, it is judged that the subject has responded to the chemoradiotherapy, and the higher the score, the better the chemoradiotherapy effect of the subject.

8. A storage medium having machine executable instructions stored thereon, wherein the machine executable instructions, when executed, cause a machine to perform the method of claims 5 to 7.

9. Use of the gene marker according to claim 1 or 2 in preparing a kit for evaluating cancer prognosis.

10. Use of the gene marker according to claim 1 or 2, or the method according to any one of claims 5 to 7 in determining or predicting the prognosis of a cancer patient; Preferably, the patient has undergone or will undergo radiotherapy and / or chemotherapy, more preferably neoadjuvant chemoradiotherapy; Preferably, the tumor comprises a solid tumor and / or a blood tumor; Preferably, the tumor is selected from one or more of liver cancer, colon cancer, rectal cancer, head and neck cancer, colorectal cancer, lung cancer, breast cancer, cervical cancer, ovarian cancer, pancreatic cancer, bile duct cancer, kidney cancer, glioma, liposarcoma, melanoma, lymphoma, lung cancer, squamous cell carcinoma, chondrosarcoma and fibrosarcoma.

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