A gene marker associated with cancer prognosis and use thereof

Abstract

The application discloses a gene marker related to the prognosis of a cancer patient and an application thereof, relates to the technical field of biological medicine, and uses the biomarker provided in the application, does not need to depend on a tumor tissue sample, has high accuracy, high sensitivity and high specificity, is simple to judge, can be used for auxiliary diagnosis, helps to guarantee the tumor curative effect while maximally preserving the survival quality of the patient, and maximally improves the curative effect of a patient with an adverse prognosis.

Description

Technical Field

[0001] This invention relates to the field of biomedical technology, and in particular to a gene marker related to cancer prognosis and its application. Background Technology

[0002] Neoadjuvant chemoradiotherapy is a cancer treatment strategy, but its sensitivity varies significantly among individuals. The degree of tumor regression after neoadjuvant chemoradiotherapy is significantly correlated with long-term survival and quality of life. If tumor regression can be predicted in advance, a prospective and rigorous wait-and-see strategy after neoadjuvant chemoradiotherapy can help these patients avoid surgery while preserving their anus. In some patients, even after receiving intensive neoadjuvant chemoradiotherapy, tumor regression remains unsatisfactory. If limited tumor regression can be predicted in advance, the treatment strategy can be adjusted from the beginning to improve the survival rate and prolong survival time for these patients.

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

[0004] As an example, immunofluorescence detection involves staining pre-treatment tumor tissue sections with immunofluorescence to determine HLA-DR / MHC-II ratios based on tumor region. + CD20 in cells and tumor stroma + The density of B cells is used to assess the response of patients undergoing neoadjuvant chemoradiotherapy. Disadvantages of this method include the requirement to obtain tumor tissue and uncertain predictive efficacy.

[0005] As an example, tumor tissue section staining involves staining tumor tissue sections obtained from pre-treatment biopsies with hematoxylin and eosin (HE) to predict a patient's response to neoadjuvant chemoradiotherapy based on the density of immune cells in the tumor area. Disadvantages of tumor tissue section staining include the requirement of obtaining tumor tissue, the inability of HE staining to determine the type of immune cells, and the resulting inaccuracies in the predictions.

[0006] As an example, immunohistochemical staining of pre-treatment tumor tissue sections can be used to predict a patient's response to neoadjuvant chemoradiotherapy based on the density of immune cells in the tumor area. Disadvantages of immunohistochemical staining include the requirement to obtain tumor tissue and the difficulty in accurately determining the relative density of immune cells.

[0007] As an example, CER5 expression level detection is used to determine CER5 expression levels, for instance, from samples obtained from primary solid tumors or their local or distant metastases from patients, in order to assess clinical response (WO2015092075A1). Disadvantages of this approach include uncertainty about its applicability to assessing treatment response in colorectal cancer patients and its reliance on tumor or metastatic samples.

[0008] Therefore, there is an urgent need for a method that is highly sensitive, accurate, and specific, and does not depend on tumor or tumor metastasis samples, to determine the efficacy of neoadjuvant chemoradiotherapy. Summary of the Invention

[0009] To address at least one of the above problems, this disclosure provides a gene biomarker related to cancer prognosis and its application. Using the gene biomarker provided in this disclosure to evaluate the efficacy of radiotherapy and chemotherapy in cancer patients can be done without relying on tumor tissue samples, and is simpler than existing technologies, with high accuracy, high sensitivity and high specificity.

[0010] According to one aspect of this disclosure, a genetic biomarker associated with the prognosis of cancer patients is provided, said genetic biomarker being associated with the IFNG signaling pathway.

[0011] In some embodiments, the genetic marker includes one or more of the following genes: JAK1 and IRF1.

[0012] In some embodiments, the genetic marker further includes 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 implementations, the cancer patient has already undergone or will undergo radiotherapy and / or chemotherapy.

[0014] In some implementations, the cancer patient has already undergone or is about to undergo neoadjuvant chemoradiotherapy.

[0015] In some embodiments, the tumor includes solid tumors and / or hematologic malignancies.

[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 implementations, the cancer is colorectal cancer.

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

[0019] In some embodiments, the reagents used to detect the genetic marker include primer pairs or probes targeting the genetic marker.

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

[0021] According to another aspect of this disclosure, a method for screening genetic biomarkers associated with cancer prognosis is provided, the method comprising the steps of: MSigDB-based...

[0022] The REACTOME_INTERFERON_GAMMA_SIGNALING gene set was used to screen gene markers using LASSO regression, which was then used as the gene set for scoring.

[0023] According to another aspect of this disclosure, a method for evaluating cancer prognosis is provided, the method comprising the step of detection using the said genetic marker.

[0024] In some implementations, the method includes the following steps:

[0025] 1) Quality control and filtering of transcriptome sequencing data from cancer subjects' samples were performed to obtain filtered sequencing data;

[0026] 2) Transcript quantification was performed on the filtered sequencing data;

[0027] 3) The genetic markers are scored, and the cancer prognosis is evaluated based on the scores.

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

[0029] In some embodiments, the bodily fluid samples include: saliva, whole blood, serum, plasma, milk, urine, lumbar or ventricular CSF, lymph, prostatic fluid, semen, sputum, feces, tears, tumor cells, bronchoalveolar lavage fluid, sputum, pus, nasopharyngeal swabs, oral swabs, cerebrospinal fluid, pleural effusion, peritoneal fluid, amniotic fluid, peritoneal fluid, aqueous humor, vitreous humor, vaginal discharge, and their processed forms.

[0030] In some embodiments, the tissue sample includes tissue, paraffin sections, and their processed forms.

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

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

[0033] In some implementations, the quality control in step 1) includes evaluating one or more of the following: the ratio of repetitive sequences, alignment rate, unique alignment rate, exon alignment rate, intron alignment rate, intergenic region alignment rate, expression efficiency, detected transcripts, and uniformity of detected gene or sequence coverage.

[0034] In some implementations, the filtering in step 1) includes removing the connector sequence and low-quality read lengths from the 3' end.

[0035] In some implementations, the quality control and filtering in step 1) employ one or more of Trimmomatic, Skewer, and FastP.

[0036] In some implementations, 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 implementations, the quantification in step 2) is based on sequencing data.

[0038] In some implementations, the sequencing data is in FastQ format.

[0039] In some implementations, the quantitative result obtained in step 2) includes transcript per million readings (TPM) or logarithmic TPM for each transcript.

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

[0041] In some implementations, the scoring in step 3) is performed using the Gene Set Variation Analysis (GSVA) ​​method.

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

[0043] In some implementations, the threshold for judgment in step 3) is 0, and the judgment criteria include: when the score is ≤0, the subject is judged to have no response to the radiotherapy and chemotherapy; when the score is >0, the subject is judged to have a response to the radiotherapy and chemotherapy, and the higher the score, the better the radiotherapy and chemotherapy effect of the subject.

[0044] According to another aspect of this 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 this disclosure, a storage medium is provided that stores machine-executable instructions, which, when executed, cause a machine to perform the methods provided in this disclosure.

[0046] According to another aspect of this disclosure, the use of the genetic markers described in the first aspect in the preparation of kits for evaluating cancer prognosis is provided.

[0047] According to another aspect of this disclosure, the application of the said genetic markers and the said methods in determining, diagnosing or predicting the prognosis of cancer patients is provided.

[0048] In some implementations, the cancer patient has already undergone or will undergo radiotherapy and / or chemotherapy.

[0049] In some implementations, the cancer patient has already undergone or is about to undergo neoadjuvant chemoradiotherapy.

[0050] In some embodiments, the tumor includes solid tumors and / or hematologic malignancies.

[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 implementations, the cancer is colorectal cancer.

[0053] The beneficial effects of this invention are:

[0054] The method for using the gene markers provided in this disclosure to determine the efficacy of radiotherapy and chemotherapy in cancer subjects has the following advantages: it is non-invasive to patients, requiring only a small amount of blood; it does not rely on tumor tissue samples, and has high accuracy, high sensitivity, and high specificity, with the samples being easier to obtain; it is low-cost; and the assessment is simple.

[0055] The genetic markers and methods described herein can be used to assist in diagnosis, helping to preserve the patient's quality of life to the greatest extent possible while ensuring the efficacy of tumor treatment, and improving the treatment outcomes for patients with poor prognoses as much as possible. Attached Figure Description

[0056] Figure 1 The treatment methods for the 26 patients collected are shown.

[0057] Figure 2 A heatmap of seven cellular modules based on the co-occurrence patterns of tumor-enriched cell subpopulations in tumor samples is shown.

[0058] Figure 3 The abundance radar plots of four cell modules after treatment with different therapeutic methods are shown.

[0059] Figure 4 The abundance of cellular modules after treatment with different neoadjuvant methods is shown.

[0060] Figure 5 This study demonstrates the role of different neoadjuvant therapies in the treatment of c13-CD8-Tem-CCL4 cells at CD455. + Frequency in cells (Pre n=26, nCTn=8, nRTn=5, nRCT n=9).

[0061] Figure 6 This shows the presence of c13-CD8-Tem-CCL4 cells in CD45 in patients with and without complete remission (CR) before and after neoadjuvant chemoradiotherapy (nRCT). + Frequency in cells (CR: front n=7, back n=5; non-CR: front n=4, back n=3).

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

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

[0064] Figure 9 It shows tumor-invasive CD8 + Visualization of T cell subsets using UMAP (left) and distribution of IFNG gene expression (right).

[0065] Figure 10 This study illustrates tumor-invasive CD8 cells after treatment with different neoadjuvant therapies. + Changes in IFNG expression in T cells.

[0066] Figure 11 This shows the tumor-invasive CD8 levels in CR and non-CR patients before and after nRCT treatment. + Changes in IFNG expression in T cells.

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

[0068] Figure 13 The study shows how CytoSig predicts IFNG signal intensity in the tumor microenvironment of fully responsive and incompletely responsive LARC samples after nRCT treatment.

[0069] Figure 14 The image shows CD8 and IFNγ immunostaining before and after nRCT treatment, revealing IFNγ. + CD8 +Distribution of T cells (white arrows) in tumor tissue, and IFNγ levels in tumor samples before and after nRCT treatment. + CD8 + Quantitative analysis of T cell density (n=13).

[0070] Figure 15 This shows the IFNγ levels in tumor tissues of CR and non-CR patients before and after nRCT treatment. + CD8 + Distribution of T cells (white arrows) and quantitative analysis of their cell density (n=18).

[0071] Figure 16 The IFNG signal characteristic scores for colorectal cancer before and after neoadjuvant chemoradiotherapy are shown from the public dataset GSE94104. TRG represents the tumor regression grade (a lower TRG score indicates a better patient response).

[0072] Figure 17 The data show IFNG expression and IFNG signal feature scores before and after neoadjuvant chemoradiotherapy for colorectal cancer, derived from the public dataset GSE15781.

[0073] Figure 18 A heatmap showing the expression characteristics of IFNG signaling in various cells of peripheral blood samples from patients treated with different neoadjuvant therapies is presented.

[0074] Figure 19 Box plots show a comparison of IFNG signal characteristics in PBMC samples before and after nRCT treatment in CR and non-CR patients (CR: n=7 before treatment, n=5 after treatment; non-CR: n=3 before treatment, n=3 after treatment).

[0075] Figure 20 The scoring and comparison of IFNG signals in blood using an internal batch RNA-seq dataset from PBMC samples of CR and non-CR patients before and after nRCT treatment are shown (pre n=3; non-CR: Post n=3; CR: Post n=5).

[0076] Figure 21 The receiver operating characteristic (ROC) curves of the PBMC IFNG signal feature score in distinguishing between CR and non-CR patients are shown based on internal batch RNA-seq data analysis.

[0077] Figure 22 The scores of subjects who had poor response (non-CR) to neoadjuvant chemoradiotherapy and those who had complete response (CR) to neoadjuvant chemoradiotherapy are shown in the JAK1 and IRF1 scores.

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

[0079] Figure 24 The comparison of scores obtained from the control group scores in Example 5 for subjects with poor response to neoadjuvant chemoradiotherapy (non-CR) and those with complete response to neoadjuvant chemoradiotherapy (CR) is shown. Detailed Implementation

[0080] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. The specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention in any way. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts of this disclosure. Such structures and techniques have also been described in many publications.

[0081] definition

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

[0083] Unless the context clearly indicates otherwise, the terms “a” and “an” as used herein include plural references.

[0084] The term "about" as used herein is as 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 unfamiliar with the use of this term in the context in which it is used, "about" will mean a particular value plus or minus 10%.

[0085] As used herein, the term "sample" refers to a composition obtained from or derived from a subject (e.g., an individual of interest) containing 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. Tissue samples include, but are not limited to, tissues, paraffin sections, and their processed forms. Body fluid samples include, but are not limited to, saliva, whole blood, serum, plasma, milk, urine, lumbar or ventricular CSF, lymph, prostatic fluid, semen, sputum, feces, tears, tumor cells, bronchoalveolar lavage fluid, sputum, pus, nasopharyngeal swabs, oral swabs, cerebrospinal fluid, pleural effusion, peritoneal fluid, aqueous humor, vitreous humor, vaginal discharge, and their processed forms.

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

[0087] As used in this article, the term “sequencing” refers to the process of determining the sequence (e.g., the identity and order of monomeric units) of a biomolecule, such as a nucleic acid, like 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 termination sequencing, whole genome sequencing, hybridization sequencing, pyrosequencing, capillary electrophoresis, double-strand sequencing, cyclic sequencing, single-base extension sequencing, solid-phase sequencing, high-throughput sequencing, massively parallel signature sequencing, emulsion PCR, low denaturing 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, synthetic sequencing, real-time sequencing, reverse terminator sequencing, nanopore sequencing, 454 sequencing, Solexa genome analyzer sequencing, SOLiD™ sequencing, MS-PET sequencing, DNA nanosphere sequencing (DNBSEQ), complex probe anchored polymerization sequencing (cPAS), and combinations thereof. In some implementations, sequencing can be performed using a gene analyzer, such as those commercially available from Illumina, Inc., Pacific Biosciences, Inc., Applied Biosystems / Thermo Fisher Scientific, or BGI Genomics Co., Ltd. Examples include BGI's DNBseq sequencing platforms such as BGISEQ-500, BGISEQ-50, MGISEQ-2000, MGISEQ-200, DNBSEQ-T7, DNBSEQ-G99, and DNBSEQ-T20X2, or Illumina's HiSeq2000, HiSeq2500, HiSeq4000, HiSeqX10, and NovaSeq6000.

[0088] The term "colorectal carcinoma (CRC)" used in this article refers to one of the most common and serious malignant tumors of the digestive system that significantly impact human health and survival. It originates in the large intestine and includes colon cancer and rectal cancer. Colorectal cancer presents with varying clinical symptoms and signs depending on its location, primarily including abdominal pain, rectal bleeding, abdominal mass, and intestinal obstruction, with or without systemic symptoms such as anemia, fever, and weight loss.

[0089] As used in this article, the term "gene marker" refers to 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 this invention is not limited to quantifying the gene expression of any particular variant of the biomarker gene of this invention.

[0091] The term "evaluation" as used herein refers not only to "judgment" but also includes "determination," "differentiation," "identification," "examination," "detection," and "prediction." This includes differentiating or assisting in differentiating / identifying cancer and benign lesions, determining / assessing cancer and its progression, and predicting cancer risk, treatment effectiveness, prognosis, and recurrence risk. In some embodiments, the diagnosis includes differentiating or assisting in differentiating malignant tumors and benign lesions, determining the progression of cancer, or predicting cancer risk, treatment effectiveness, prognosis, and recurrence risk.

[0092] As used herein, the term "prognosis" refers to the prediction of the likely course and outcome of a clinical condition or disease. A patient's prognosis is typically determined by evaluating factors or symptoms that indicate 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. Rather, those skilled in the art will understand that the term "prognosis" refers to an increased probability that a particular course or outcome will occur; that is, the course or outcome is more likely to occur in a patient presenting with a given condition compared to an individual who does not present with the condition. Cancer prognosis can be assessed by the degree of response of a cancer patient to radiotherapy or chemotherapy, such as a good or poor response, including neoadjuvant radiotherapy or chemotherapy.

[0093] As used herein, the term "response" can refer to a change in a subject's condition resulting from or related to treatment. In some embodiments, a 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 exacerbations expected or commonly observed in the absence of treatment), relief of one or more symptoms of the condition (e.g., reduction in frequency and / or intensity), and / or improved prospects for a cure. In some embodiments, "response" can refer to a biological, organ, tissue, cell, or cellular component or in vitro system response. In some embodiments, a response is or includes a clinical response. In some embodiments, the presence, extent, and / or nature of a response may be measured and / or characterized according to specific criteria; in some embodiments, such criteria may include clinical and / or objective criteria. In some embodiments, techniques used to assess a response may include, but are not limited to, clinical examination, positron emission tomography (PET), chest X-ray CT scan, MRI, ultrasound, endoscopy, laparoscopy, the presence or level of specific biomarkers in a sample, cytology, and / or histology. When the response of interest is or includes the response of a tumor to a therapy, those skilled in the art will recognize a variety of established techniques for assessing such a response, including, for example, determining tumor burden, tumor size, tumor stage, etc. Based on this disclosure, those skilled in the art will recognize and / or understand the specific response criteria used to determine individual tumors, tumor types, patient populations or groups, etc., and the strategies used to determine their appropriate reference.

[0094] As used herein, the term "treatment" refers to the sequential or simultaneous administration of antitumor agents, and / or antiangiogenic agents, and / or anti-interstitial agents, and / or immunostimulants or inhibitors, and / or hematopoietic agents, and / or radiotherapy, and / or hyperthermia, and / or cryotherapy for cancer treatment. These administrations may be performed in adjuvant and / or neoadjuvant modes. The composition of such regimens may vary depending on the dosage of each individual agent, the time frame of administration, and the frequency of dosing to define the therapeutic window. Various combinations of various drugs and / or physical methods, as well as various timelines, are currently being investigated.

[0095] The term “chemotherapy” as used herein has its general meaning in the art and refers to treatment that includes the administration of chemotherapy agents to a patient.

[0096] As used herein, the term "radiotherapy" has its general meaning in the art and refers to treatment using ionizing radiation. Ionizing radiation deposits energy that damages or destroys cells in the area being treated (target tissue) by disrupting their genetic material, preventing them from continuing to grow. A common form of radiotherapy involves photons, such as X-rays. Depending on their energy levels, these rays can be used to destroy cancer cells on the surface of the body or deep within it. The higher the energy of the X-ray beam, the deeper the X-rays penetrate the target tissue. Linear accelerators and electron induction accelerators produce increasingly higher energies of X-rays. Focusing radiation (such as X-rays) onto the cancer site using a machine is called external beam radiotherapy. Gamma rays are another form of photon used in radiotherapy. Gamma rays are spontaneously generated because certain elements (such as radium, uranium, and cobalt-60) release radiation when they decompose or decay. In some implementations, radiotherapy is external beam radiotherapy. Examples of external beam radiotherapy include, but are not limited to, conventional external beam radiotherapy; three-dimensional conformal radiotherapy (3D-CRT), which emits beams of radiation from different directions to closely conform to the shape of the tumor; intensity-modulated radiotherapy (IMRT), such as helical tomotherapy, which shapes the radiation beam to closely conform to the shape of the tumor and also adjusts the radiation dose according to the shape of the tumor; conformal proton beam radiotherapy; image-guided radiotherapy (IGRT), which combines scanning and radiotherapy techniques to provide real-time images of the tumor to guide radiotherapy; intraoperative radiotherapy (IORT), which delivers radiation directly to the tumor during surgery; stereotactic radiosurgery, which can deliver a large and precise radiation dose to a small tumor area in a single treatment; hyperfractionated radiotherapy, such as continuous hyperfractionated accelerated radiotherapy (CHART), in which the subject receives more than one radiotherapy treatment per day (fractionation); and hypofractionated radiotherapy, in which the dose per treatment is large, but the number of treatments is small.

[0097] The term “neoadjuvant therapy” as used in this article refers to preoperative treatment regimens, which may typically include, for example, chemotherapy, radiotherapy, targeted therapy, hormone therapy, and / or immunotherapy, designed to shrink the primary tumor so that local treatment (such as surgery) is less disruptive or more effective, or to preserve the surgery or the organ.

[0098] The term "transcripts per million (TPM)" used in this article is a normalized expression measure that divides the expression level of each gene by the total number of transcripts and multiplies it by a normalization factor, typically 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 allows comparison of expression levels of different genes within the same sample, as well as expression differences between different samples, regardless of gene length and sequencing depth.

[0099] The following embodiments and accompanying drawings are provided to aid in understanding the present invention. However, it should be understood that these embodiments and drawings are for illustrative purposes only and do not constitute any limitation. The actual scope of protection 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 has been 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) pathologically diagnosed as microsatellite stable / mismatch repair normal (MSS / pMMR) from Sun Yat-sen University Cancer Center were included in this study. Patients were divided into four groups based on their treatment methods: Pre-treatment group, 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 150mg / m 2 Folic acid 400mg / m 2 and fluorouracil 2400mg / m 2 (A total of 6-8 cycles). Patients in the nRT group received short-course radiotherapy (5×5Gy), while patients in the nRCT group received short-course radiotherapy (5×5Gy) and then received 6-8 cycles of FOLFOXIRI treatment within 11-18 days after the last radiotherapy.

[0104] All patients underwent routine colonoscopy and biopsy, with tumor tissue collected via colonoscopy biopsy within two weeks prior to treatment. Figure 1 Following their respective neoadjuvant therapies, paired fresh tumor tissues were collected via surgical resection or endoscopic biopsy. Neoadjuvant radiotherapy (nRT) was performed in the treatment phase preceding nRCT; chemotherapy was continued only after insufficient efficacy. In conclusion, neoadjuvant chemoradiotherapy (nRCT) showed the best treatment outcome. This study was approved by the Research and Ethics Committee of Sun Yat-sen University Cancer Center (No. B2022-557).

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

[0106] 2) PBMC separation

[0107] PBMCs were isolated from peripheral blood using Ficoll density gradient centrifugation. Briefly, Ficoll was added to a 15 mL centrifuge tube according to the total blood volume, followed by slow addition of whole blood. Centrifugation was performed at 800 g for 30 min at room temperature with an acceleration of 1 and a deceleration of 0. After centrifugation, the middle white membrane layer was collected and washed with PBS containing 2% fetal bovine serum to remove Ficoll. Red blood cells were removed using erythrocyte 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 separated using a human tumor dissociation kit (Miltenyi Biotec, 130-095-929). In short, the tumor tissue was cut into small pieces in C-tubes (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-tubes were placed in a gentesmacs with a heater. TM Tissue dissociation was performed for 1 hour using the 37C_h_TDK_1 program on an Octo dissociator. After dissociation, cells were filtered through a 70 μm filter membrane and then washed with 10 mL of PBS containing 2% FBS. The cells were centrifuged at 300 g for 10 min, and the cell pellet was collected, stained with trypan blue, and counted.

[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, using BD FACSAria. TM Cell sorting was performed using instrument III (BD Biosciences). DAPI-negative cells were considered viable, and DAPI levels in PBMCs were sorted separately. - CD235a - CD45 + DAPI in cell and tumor tissue suspension - CD235a - . cells.

[0112] Using a 10×Chromium Single Cell 5` Library (10×Genomics, PN-1000263), 10,000–20,000 cells were loaded according to the manufacturer's instructions to prepare the library. The purified library was constructed using a library construction kit (10×Genomics, PN-1000190) according to the manufacturer's instructions, and then sequenced using a BGI MGISEQ-2000 sequencer (BGI, 1000019023). TCR sequences were amplified using the Chromium Single Cell Human TCRAmplification Kit (10×Genomics, PN-1000252). The V(D)J library was constructed using a library construction kit (10×Genomics, PN1000190) and sequenced using an Illumina Novaseq 6000 sequencer.

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

[0114] 5) Preparation and sequencing of bulk RNA libraries

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

[0116] 6) Preprocessing of single-cell RNA-seq data

[0117] The generated scRNA-seq data were aligned and quantified with the GRCh38 human reference genome, and the generated feature 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) to retain high-quality cells and remove cells with mitochondrial gene content exceeding 40% and low-expression genes (fewer than 3 cells detected).

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

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

[0120] Highly variable genes (HVGs) were detected in each sample using the `scanpy.pp.highly_variable_genes` function (batch_key='sample', n_top_genes=4000), and then merged to avoid batch effects. Principal component analysis (PCA) was then performed on the HVG matrix using the `scanpy.tl.pca` function (svd_solver='arpack', n_comps=50).

[0121] Use `scanpy.external.pp.bbknn(version 1.5.1, defaultparameters)` to remove batch effects from the data. Further dimensionality reduction and visualization are achieved using the Unified Manifold Approximation and Projection (UMAP) implemented in the `scanpy.tl.umap` function.

[0122] For the first round of unsupervised clustering, based on known marker genes (such as EPCAM, PECAM1, DCN, PTPRC, CPA3, CD14, LYZ, CD3D, CD3G, CD4, CD8A, NKG7, MS4A1, MZB1, etc.), CD8... + T cells, CD4 + Seven major cell types were labeled: T cells, gdT cells, ILC cells, myeloid cells, B cells, and non-immune cells. Next, a second round of clustering was performed within each major cell type to obtain subpopulation structures. Notably, during the clustering process, two-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 using Numbat (version 1.2.2) based on copy number variation, 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 identify differentially expressed genes between the two cell types, and the p-value was corrected using bonferroni based on the total number of genes.

[0126] 9) Calculation of gene characteristic scores

[0127] To assess the functional dynamics of cell subsets after treatment, we collected a series of gene signatures. The `scanpy.tl.score_genes` function was used to calculate the signature scores of the gene sets in the cells; the scores were the average expression of one set of genes minus the average expression of a set of reference genes.

[0128] 10) Quantitative analysis of cell distribution

[0129] To characterize the distribution of cell subpopulations and determine the distribution of a particular subpopulation in a specific tissue or at a specific treatment time point, we calculated the odds ratio (OR) described in previous studies. For a given cell subpopulation, an OR > 1 indicates that the subpopulation was observed more frequently under a given condition than under other conditions (i.e., enrichment), while an OR < 1 indicates that the subpopulation was observed less frequently under a given condition than under other conditions (i.e., depletion).

[0130] 11) Identification of cell modules

[0131] To systematically investigate the potential dynamics of TME during treatment, we explored co-occurrence patterns among different cell subpopulations. Spearman's rank correlation coefficient (ρ) was calculated for the frequency of each cell subpopulation in all tumor samples. 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). To ensure the accuracy of the clustering results and avoid distortions potentially caused by the low frequency of certain subpopulations, tumor-enriched cell subpopulations identified by the above ORs were retained for cell module analysis. Seven highly correlated cell modules were ultimately identified. The abundance of a cell module in each sample was calculated as the average proportion of the subpopulation belonging to that cell module.

[0132] 12) Prediction of cytokine signaling activity

[0133] CytoSig (version 0.1) was used to predict cytokine signaling activity for each sample. We calculated the mean gene expression for each sample. Next, we used the CytoSig.Ridge_significance_test function to calculate cytokine signaling activity. To address signal redundancy, CytoSig employed ridge regression to identify features in the transcriptome attributable to the effects of cytokines on their target gene expression.

[0134] 13) Batch RNA-seq data preprocessing

[0135] Batch RNA-seq data were filtered using Skewer (version 0.2.2). Adapted sequences and low-quality reads (<30) were removed from the 3' end. Quantification was performed using Kallisito (version 0.44). Transcript expression levels (TPMs) belonging to the same gene were aggregated using tximport (version 1.22.0) software to calculate gene expression levels, and the TPMs were logarithmically transformed.

[0136] 14) Microarray data analysis

[0137] To validate the finding that higher levels of IFNG are associated with nRCT response, we collected published clinically annotated microarray data from patients with advanced colorectal cancer who underwent RCT treatment. Two datasets (GSE94104 and GSE15781) were downloaded from the Gene Expression Omnibus database using the getGEO function in GEOquery (version 2.62.2). The raw gene expression values ​​from the microarray experiments were subjected to background correction, logarithmic transformation, and quantile normalization using the Robust Multi-array Average (RMA) algorithm. For each sample, the 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) Multiplex immunofluorescence staining

[0139] Paraffin-embedded tissue was cut into 5 μm thick histological sections and stained using the PanoPANEL kit (Panovue) based on Tyramide Signal Amplification (TSA) technology according to the manufacturer's instructions. The slides were first baked at 60°C for 1 h, dewaxed in xylene, and then rehydrated twice, sequentially in 100% ethanol, 95% ethanol, and 70% ethanol. Antigen was extracted by blocking with citrate buffer (pH 6.0) for 30 min under heat induction. After removing the blocking buffer, the slides were incubated with primary antibodies, including CD8 (zsbb-bio, ZA-0508) and IFNγ (Abcam, ab231036), at room temperature for 1 h. The slides were washed three times with 1×TBST buffer (tris-buffered saline containing 0.1% Tween 20) and incubated with HRP-labeled secondary antibody at room temperature for 15 min. The slides were then incubated with a 1:100 (Panovue) diluted TSA working solution at room temperature for 10 min, followed by microwave heating to amplify the signal. Repeat this process until all antibodies are stained. Finally, stain the cell nuclei with DAPI. Scan the stained slides using an Olympus VS200 and analyze them using OlyVIA analysis software (version 4.1).

[0140] Example 1. Transcriptome analysis of cells after neoadjuvant therapy revealed a significant increase in the c13-containing M2 module after nRCT treatment.

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

[0142] The results revealed seven distinct cellular 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 features, including c13-CD8-Tem-CCL4 and c40-cDC1-CLEC9A cells. M3 appears to be associated with a tertiary lymphoid structure (TLS) characterized by germinal center B cells (c63-GCB-LRMP) and T follicular helper cells (c24-CD4-Tfh-CXCL13). M4 exhibits certain regulatory features, represented by c29-CD4-Treg-KLRB1 and c26-CD4-Th17-IL17A. M6 includes CD4 and CD8 memory subsets. Furthermore, M5 is predominantly composed of stromal cell clusters and exhibits features related to angiogenesis and adhesion. M7 includes mast cells and fibroblast populations and is associated with mucosal immunity.

[0143] Next, we assessed the TME changes induced by different treatment methods at the cellular module level. The results showed that, compared to pre-treatment levels, the neoadjuvant chemoradiotherapy (nRCT) group, which had the best treatment outcome, exhibited fewer M1 features and more M2 features. Figure 3 and Figure 4 M2 cells are enriched with pro-inflammatory CD8 cell clusters c13-CD8-Tem-CCL4 (). Figure 2 ).

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

[0145] Analysis of the c13-CD8-Tem-CCL4 cell subset revealed that this subset slightly decreased after nRT, but significantly increased after nRCT. Figure 5 The level of [something] increased, particularly in patients with a complete response (CR), but not in patients with an incomplete 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] CD8 infiltrates with other tumors + Compared to T cells, the c13-CD8-Tem-CCL4 cell subset highly expresses the IFNG gene ( Figure 8 and Figure 9 Furthermore, in the neoadjuvant chemoradiotherapy (nRCT) group, which showed the best treatment outcome, CD8... + T cells express the highest level of IFNG gene ( Figure 10 Especially in patients with complete response (CR) after nRCT treatment, CD8 + The expression of the IFNG gene in T cells increased significantly. Figure 11 ).

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

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

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

[0150] To further verify the correlation between the c13-CD8-Tem-CCL4 cell subset and IFNG with nRCT response, this embodiment used tumor tissue sections from patients before and after treatment for multicolor immunofluorescence staining.

[0151] The results showed that IFNγ levels in tumor tissues after neoadjuvant chemoradiotherapy (nRCT) were significantly higher than those after chemoradiotherapy (nRCT). + CD8 T cells significantly increased ( Figure 14 ), especially in patients with complete response (CR) Figure 15 ).

[0152] Next, we validated our findings using IFNG and the IFNG signaling pathway-related gene set (IFNG signaling signature), as well as the public datasets GSE94104 and GSE15781. Consistent with the above results, the expression levels of IFNG genes and their signaling pathway-related genes in the tumor microenvironment significantly increased after neoadjuvant chemoradiotherapy. Figure 16 and 17 ), and the better the patient's treatment, the stronger the expression level ( ). Figure 16 ).

[0153] Produced IFNG genes with high-density lipoprotein 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,THE-DRB5,THE-DRA,THE-C,THE-E,TRIM10,TRIM31,THE-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,THE-DPB1,THA-A,THA-DPA1,TRIM31,THA-DQB2,THA-B,THA-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-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. ,

[0154] We further analyzed peripheral blood samples from patients before and after neoadjuvant therapy and found that after neoadjuvant chemoradiotherapy (nRCT), various immune cells in the patients' peripheral blood were enriched with genes related to the IFNG signaling pathway. Figure 18 This characteristic is particularly pronounced in patients with complete response (CR). Figure 19 ).

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

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

[0157] Analysis revealed that the signaling characteristics of the IFNG signaling pathway-related gene set were also significantly elevated in patients with complete response (CR) after neoadjuvant chemoradiotherapy in batch sequencing RNA data. Figure 20 Furthermore, AUC analysis revealed that the signal characteristics of gene sets related to the IFNG signaling pathway in peripheral blood could effectively distinguish between patients with complete and incomplete responses to neoadjuvant chemoradiotherapy (AUC = 0.8). Figure 21 ).

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

[0159] To further improve the prediction results, a set of genes related to the IFNG signaling pathway was screened.

[0160] Eight colorectal cancer patients from Sun Yat-sen University Cancer Center received neoadjuvant chemoradiotherapy before surgery: They first received a short course of radiotherapy (5 × 5 Gy), with a maximum course of 8 days. Chemotherapy began 11-18 days after the last radiotherapy session, and at least within 4 weeks, they received 4-6 cycles of mFOLFOXIRI (oxaliplatin (85 mg / m2) and leucovorin (400 mg / m2)) intravenously every 2 hours on day 1, followed by irinotecan (150-165 mg / m2) intravenously every 90 minutes on day 1, and fluorouracil (2400-2800 mg / m2) continuously intravenously every 46 hours on day 1-2. Treatment was discontinued when intolerable toxicity occurred, disease progression occurred, or other conditions deemed unsuitable for continued chemotherapy by the physician. Venous blood samples were collected from colorectal cancer patients several days to several months after neoadjuvant chemoradiotherapy for sequencing library construction and analysis.

[0161] GSVA was used to score genes related to the IFNG signaling pathway: Based on the REACTOME_INTERFERON_GAMMA_SIGNALING gene set from MSigDB, LASSO regression was used to screen for two genes related to the IFNG signaling pathway, JAK1 and IRF1, which were used 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, the kernel density of the cumulative density function is estimated using all samples (in this case, the reference samples plus the newly measured samples);

[0164] 2) Ranking of expression level statistics: The gene expression level statistics for each sample are ranked.

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

[0166] 4) GSVA enrichment score calculation: The algorithm outputs a matrix containing the pathway enrichment scores for each gene set and sample.

[0167] A high score indicates that chemotherapy and radiotherapy are effective for the patient. All parameters used in the tool are default. For detailed principles and code, please see: https: / / github.com / rcastelo / GSVA; the JAK1 and IRF1 scoring results are shown in Table 1 and... Figures 22-23 As shown, the comparison is as follows Figure 24 As shown.

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

[0169] 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 poor response to neoadjuvant chemoradiotherapy (incomplete response, non-CR), with JAK1 and IRF1 scores below 0 after sequencing; patients RC15, RC16, RC20, RC21, and RC22 had good response to neoadjuvant chemoradiotherapy (complete response, CR), with JAK1 and IRF1 scores above 0 after sequencing.

[0171] like Figure 22 As shown, patients who fully responded to neoadjuvant chemoradiotherapy had significantly higher scores compared to patients who did not respond well to neoadjuvant chemoradiotherapy (non-CR).

[0172] like Figure 23 As shown, ROC-AUC = 1, this index can effectively distinguish between patients with complete and incomplete responses (the higher the AUC, the better, with a maximum of 1).

[0173] The above-mentioned good response to neoadjuvant chemoradiotherapy includes, for the surgical group, pathological complete response (pCR), defined as the absence of residual surviving tumor cells in the primary tumor or dissected lymph nodes upon pathological examination after radical surgery; and for the non-surgical group, clinical complete response, defined as: ① no irregularities or palpable tumors on digital rectal examination; ② no visible lesions on endoscopic examination except for flat scars, telangiectasia, or mucosal whitening; additional criteria: ③ no residual tumors in the primary site and draining lymph nodes as shown by magnetic resonance imaging (MRI) or endorectal ultrasound (ERUS); ④ negative scar biopsy; and ⑤ initial elevated CEA levels returning to normal (<5 ng / mL). Conversely, a poor response is defined as the absence of these criteria.

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

[0175] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. The application of gene markers related to cancer patient prognosis in the preparation of kits for evaluating cancer prognosis, characterized in that, The genetic markers consist of JAK1 and IRF1; the cancer patient has undergone or will undergo neoadjuvant chemoradiotherapy; the cancer is locally advanced rectal cancer with microsatellite stable / mismatch repair normal (MSS / pMMR); The genetic markers are used to evaluate the response of cancer patients to neoadjuvant chemoradiotherapy. The evaluation of cancer prognosis includes the following steps: 1) Quality control and filtering of transcriptome sequencing data from cancer subjects' samples were performed to obtain filtered sequencing data; 2) Transcript quantification was performed on the filtered sequencing data; and 3) The genetic markers are scored, and the cancer prognosis is evaluated based on the scores. In step 3), the scoring is performed using the Gene Set Variation Analysis (GSVA) ​​method. The threshold for judgment in step 3) is 0. The judgment criteria include: when the score is ≤0, the subject is judged to have no response to the radiotherapy and chemotherapy; when the score is >0, the subject is judged to have a response to the radiotherapy and chemotherapy, and the higher the score, the better the radiotherapy and chemotherapy effect of the subject.

2. The application according to claim 1, characterized in that, The sample in step 1) includes a body fluid sample, a tissue sample, or a tumor cell sample.

3. The application according to claim 2, characterized in that, The bodily fluid samples include: saliva, whole blood, serum, plasma, breast milk, urine, lymph, prostatic fluid, semen, feces, tears, bronchoalveolar lavage fluid, sputum, pus, nasopharyngeal swabs, oral swabs, cerebrospinal fluid, pleural effusion, peritoneal fluid, amniotic fluid, peritoneal fluid, aqueous humor, vitreous humor, or vaginal discharge; and / or, The tissue sample includes tissue or paraffin section.

4. The application according to claim 1, characterized in that, The quality control in step 1) includes evaluating one or more of the following: the ratio of repetitive sequences, the unique alignment rate, the exon alignment rate, the intron alignment rate, the intergenic region alignment rate, the expression efficiency, the detected transcripts, and the uniformity of detected gene or sequence coverage.

5. The application according to claim 1, characterized in that, The quality control in step 1) includes evaluating the comparison rate.

6. The application according to claim 1, characterized in that, The filtering in step 1) includes removing the connector sequence and low-quality read lengths from the 3' end.

7. The application according to claim 1, characterized in that, The quality control and filtering in step 1) employ one or more of Trimmomatic, Skewer, and FastP.

8. The application according to claim 1, characterized in that, The quantification in step 2) uses one or more of the following software: RSEM, eXpress, HTseq, Cufflinks, StringTie, Sailfish, Salmon, quasimapping, or Kallisto.

9. The application according to claim 1, characterized in that, The quantification in step 2) is based on sequencing data.

10. The application according to claim 1, characterized in that, The quantitative results obtained in step 2) include the TPM or logarithmic TPM for each transcript.

11. The application according to claim 1, characterized in that, The quantification mentioned in step 2) is obtained using the tximport software.

12. A computer-readable storage medium storing machine-executable instructions that, when executed, cause a machine to perform the steps of evaluating cancer prognosis as described in any one of claims 1-11.

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