Application of SSRP1 and / or SUPT16H gene inhibitor in treatment of hepatocellular carcinoma

Abstract

The invention belongs to the technical field of gene engineering, and particularly relates to application of an SSRP1 and / or SUPT16H gene inhibitor in treatment of hepatocellular carcinoma. The invention provides an application of an inhibitor of SSRP1 and / or SUPT16H genes in preparation of a pharmaceutical composition for treating cancers. According to the invention, the high expression of SSRP1 and SUPT16H is obviously related to poor prognosis through pan-cancer analysis of SSRP1 and SUPT16H. In liver cancer, the FACT is preferentially enriched in liver cells, and the expression of the FACT is increased along with the increase of quasi time, which indicates that the FACT participates in tumor evolution and transcriptome reprogramming processes. According to the invention, FK866 and NPK76-II-72-1 are screened as potential inhibitors through drug sensitivity analysis, and FK866 and NPK76-II-72-1 are screened as the potential inhibitors. Therefore, an inhibitor of the SSRP1 and / or SUPT16H gene can be used for prognosis evaluation of the hepatocellular carcinoma patient, and can be used as a treatment target of the hepatocellular carcinoma patient.

Description

Technical Field

[0001] The present invention belongs to the technical field of genetic engineering, and particularly relates to the use of inhibitors of SSRP1 and / or SUPT16H genes in treating hepatocellular carcinoma. Background Art

[0002] Cancer is a complex malignant disease characterized by the combined effects of genetic mutations and epigenetic regulation to drive tumorigenesis, progression, and therapeutic resistance. Although significant progress has been made in cancer treatment, it remains a major global public health problem. Although new therapies have improved the survival rate of some cancers, most tumors still show resistance to existing treatment options. A major factor leading to this resistance is a complex network of epigenetic modifications, such as DNA methylation, histone modifications, and non-coding RNA changes, which enable cancer cells to evade treatment and adapt to environmental stress. Given the complexity of these epigenetic mechanisms, there is an urgent need to develop innovative, targeted therapeutic strategies aimed at reversing or regulating these abnormal epigenetic modifications to improve treatment efficacy and patient prognosis.

[0003] The FACT complex, composed of SSRP1 and SUPT16H subunits, serves as a key histone chaperone, playing a central role in gene transcription regulation, DNA replication, and damage repair. By promoting nucleosome grouping and reorganization, the complex maintains chromatin structural integrity and genomic stability. The FACT complex enhances RNA polymerase II elongation efficiency and reshapes chromatin spatial conformation, effectively inhibiting R-loop formation and subsequent transcriptional arrest. In cancer cells, FACT not only prevents the accumulation of R-loops but also maintains an open chromatin state, ensuring normal transcription and minimizing the risk of transcriptional errors. Furthermore, the FACT complex plays a key role in repairing transcription-related DNA damage, thereby preventing chromatin blockage and the formation of genomic breaks that lead to mutations and cancer progression. For example, in lung cancer, the FACT complex cooperates with the BRCA1 signaling pathway to repair transcription-related damage, thereby reducing the frequency of somatic mutations. Notably, the FACT complex exhibits functional synergy with the SWI / SNF chromatin remodeling complex, promoting chromatin remodeling to ensure continuous gene expression and transcriptional stability. These functional characteristics suggest that the FACT complex has the potential to serve as an important target for innovative cancer therapies, particularly in epigenetic-based approaches.

[0004] However, the drugs for cancer treatment in the existing technology are still limited, so there is a need to develop new drugs for cancer treatment. Summary of the Invention

[0005] The present invention aims to provide the use of inhibitors of the SSRP1 and / or SUPT16H genes in the preparation of pharmaceutical compositions for the treatment of cancer. The present invention confirms a strong correlation between FACT expression and tumor progression and identifies FK688 and NPK76-II-72-1 as therapeutic agents for tumors with high FACT expression.

[0006] In order to achieve the above objectives, the first object of the present invention is to provide a use of an inhibitor of SSRP1 and / or SUPT16H gene in the preparation of a pharmaceutical composition for treating cancer.

[0007] The second purpose is to provide an inhibitor that inhibits the function of the FACT complex and its use in the preparation of anti-tumor drugs.

[0008] Preferably, the inhibitor is FK688 and / or NPK76-II-72-1.

[0009] The third object is to provide an application in preparing a reagent for assessing cancer prognosis based on detecting SSRP1 and / or SUPT16H gene expression levels.

[0010] The fourth object is to provide a method for preparing a drug for treating hepatocellular carcinoma by using an agent for inhibiting the expression of SSRP1 and / or SUPT16H genes.

[0011] The fifth object is to provide a drug for treating patients with hepatocellular carcinoma and / or patients with lung metastasis of hepatocellular carcinoma, wherein the drug comprises an agent that inhibits the expression of SSRP1 and / or SUPT16H genes and a pharmaceutically acceptable excipient.

[0012] The sixth object is to provide a reagent for detecting the expression level of FACT complex subunit gene SSRP1 and / or SUPT16H gene in the preparation of a cancer diagnosis or prognosis evaluation kit.

[0013] Preferably, the cancer is breast cancer, lung adenocarcinoma, renal clear cell carcinoma, or hepatocellular carcinoma.

[0014] Preferably, the reagent includes a primer pair for specifically amplifying SSRP1 and / or SUPT16H; the SSRP1 primer pair includes an upstream primer sequence as shown in SEQ ID NO.1, and a downstream primer sequence as shown in SEQ ID NO.2; the SUPT16H primer pair includes an upstream primer sequence as shown in SEQ ID NO.3, and a downstream primer sequence as shown in SEQ ID NO.4.

[0015] Preferably, the reagent further comprises an internal reference gene; the internal reference gene is ACTB; the ACTB comprises an upstream primer sequence as shown in SEQ ID NO.5, and a downstream primer sequence as shown in SEQ ID NO.6.

[0016] Compared with the prior art, the present invention has the following beneficial effects:

[0017] (1) The FACT complex is mainly enriched in hepatocytes in the hepatocellular carcinoma microenvironment. It not only accelerates tumor progression by promoting cell survival, but also plays an important role in immunosuppression.

[0018] (2) FK688 and / or NPK76-II-72-1 are potential therapeutic drugs targeting the FACT complex, providing a new approach for treating tumors with high FACT expression. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1 Schematic diagram of SSRP1 / SUPT16H pan-cancer expression and prognostic analysis. Figure A shows the differentially expressed genes (DEGs) analysis of SSRP1 and SUPT16H in pan-cancer. Red represents upregulation, blue represents downregulation, the size of the circle corresponds to the FDR value, and the larger the circle, the stronger the statistical significance; Figure B shows the FACT score analysis of SSRP1 and SUPT16H. Red represents tumor samples, and blue represents normal samples. The FACT score reflects the overall expression level of the two gene sets in pan-cancer; Figure C shows the expression analysis of SSRP1 and SUPT16H in cancer subtypes. The color gradient represents the -log10 (FDR) value, and the circle size is proportional to the significance of the FDR value; Figure D shows the prognostic analysis of SSRP1 and SUPT16H in pan-cancer, the circle color represents the hazard ratio (HR) of overall survival (OS), and the circle size corresponds to the -log of the Cox proportional hazard regression analysis. 10 (FDR) value. The survival effect of each cancer type is highlighted by the size of the circle, with larger circles indicating more significant associations.

[0021] Figure 2Figure 1 shows a pan-cancer analysis of genetic variation and DNA methylation differences in SRP1 and SUPT16H. Panel A shows a pan-cancer heatmap of SNV frequencies for SSRP1 and SUPT16H. Darker colors indicate higher mutation frequencies, and the cell values represent the percentage of mutations in each cancer type. Panel B shows a correlation analysis between CNVs and mRNA expression levels for SSRP1 and SUPT16H. Red indicates a positive correlation, blue indicates a negative correlation, and dot size reflects statistical significance (larger when FDR < 0.05). Panel C shows a survival analysis of mutant and wild-type SSRP1 and SUPT16H. Darker colors indicate higher hazard ratios (HRs) for overall survival (OS), and dot size corresponds to association significance (larger when Cox P values < 0.05). Panel D shows pan-cancer DNA methylation differences between SSRP1 and SUPT16H. Red indicates increased methylation levels, blue indicates decreased methylation levels, and dot size reflects difference significance (larger when FDR < 0.05).

[0022] Figure 3 Schematic diagram of GSEA-based pathway enrichment analysis of SSRP1 and SUPT16H in pan-cancer. The colors represent the normalized enrichment scores (NES), red indicates up-regulated pathways, and blue indicates down-regulated pathways. The size of the circles corresponds to the statistical significance of each pathway. The larger the circle, the more significant the result (i.e., -log 10 The higher the FDR value).

[0023] Figure 4 Figure 2 shows the correlation analysis of SSRP1 and SUPT16H with tumor microenvironment scores and immune cell infiltration in various cancer types. Figure A shows the correlation between tumor microenvironment scores (stromal score, ESTIMATE score, immune score, and tumor purity) in different cancer types. The color gradient indicates the strength of the correlation, with red representing a positive correlation and blue representing a negative correlation. The asterisk (*) indicates the level of statistical significance. Figure B shows a heat map of immune cell infiltration scores in different cancer types. The color indicates the strength of the correlation between immune cell infiltration (such as NK cells, CD8 T cells, macrophages, etc.) and FACT expression in each cancer type. The symbols (*, #) indicate statistically significant associations, and the color depth reflects the strength of the correlation.

[0024] Figure 5Figure 3 is a schematic diagram of the enrichment of SSRP1 and SUPT16H and their correlation with hepatocytes. Figure A represents the UMAP and t-SNE visualization display of the distribution of different cell types in liver cancer tissues; Figure B represents the difference in the proportion of each cell type in tumors and adjacent normal tissues; Figure C represents the difference in the enrichment level of the FACT complex; Figure D represents the expression level of specific marker genes for each cell type; Figure E represents the differential expression of SSRP1 and SUPT16H in tumors and normal liver tissues; Figure F represents the expression of SSRP1 and SUPT16H in different cell types.

[0025] Figure 6 Schematic diagram of the dynamic expression of the FACT complex in hepatocytes and its role in tumor progression. Panel A represents a pseudo-chronic reconstruction of LIHC hepatocytes, with each data point representing a single cell. Lighter colors indicate cells in later pseudo-chronic stages, reflecting their differentiation process. Panel B represents the expression patterns of SSRP1 and SUPT16H in hepatocytes at different pseudo-chronic stages, with darker colors indicating relative gene expression levels. Panel C shows the expression changes of SSRP1 and SUPT16H genes over the pseudo-chronic progression, demonstrating their dynamic fluctuations. Panels D and D represent hepatocytes with high and low FACT complex enrichment acting as signal source cells, sending signals to other cells at a specific frequency. Panels F and G represent hepatocytes with high and low FACT complex enrichment acting as target cells, receiving signals from other cells at a specific frequency.

[0026] Figure 7 Figure 1 shows a spatial transcriptomic analysis of the LIHC tumor microenvironment. Panel A represents the structural organization of a liver cancer tumor section; Panel B represents Louvain cluster analysis of the tumor section; Panels C and D represent the expression levels of SSRP1 and SUPT16H genes in different regions of the tumor section, with darker colors indicating expression intensity; Panel E represents the spatial distribution and trajectory analysis of cell subpopulations. The hexagonal grid represents different cell populations, color-coded by cell population characteristics (0: blue, 2: green, 3: red). White arrows indicate inferred developmental or cell differentiation trajectories across tissue regions; Panel F represents trajectory analysis depicting the potential differentiation or spatial migration of tumor cells within subclusters.

[0027] Figure 8The figures are the expression validation and immunophenotypic score analysis of the FACT complex. Figure A represents the expression of the FACT complex in the GSE87630 and GSE101685 datasets, with red representing tumor samples and blue representing normal samples; Figure B represents the correlation between the immunophenotypic score (IPS) and FACT score of patients with different PD-1 and CTLA-4 status; Figure C represents the qRT-PCR analysis of FACT complex expression in nine different cell types, *P<0.05, **P<0.01, ***P<0.001, ns: not statistically significant.

[0028] Figure 9 Schematic diagram of drug sensitivity analysis and experimental validation. Panel A shows the pan-cancer correlation between SSRP1 and SUPT16H mRNA expression levels and GDSC drug IC50 values (top 30). Red indicates a positive correlation, and blue indicates a negative correlation. Black-bordered circles indicate statistically significant associations with an FDR ≤ 0.05; circle size is inversely proportional to the FDR value. Panel B shows the cytotoxic effects of different doses of FK866 on HepG2 cells as assessed by the CCK-8 assay. Panel C shows the effect of FK866 (10 μM) on HepG2 cell colony formation (DMSO as a control). **P < 0.01, ***P < 0.001, ns: not statistically significant. DETAILED DESCRIPTION

[0029] In order to further illustrate the present invention, the technical solution provided by the present invention is described in detail below with reference to the accompanying drawings and embodiments, but they should not be construed as limiting the scope of protection of the present invention.

[0030] Unless otherwise specified, the production processes, experimental methods or detection methods involved in the embodiments of the present invention are all conventional methods in the prior art, and their names and / or abbreviations are conventional names in the field and are very clear and unambiguous in the relevant fields of use. Those skilled in the art can understand the conventional process steps based on the names and apply the corresponding equipment to implement them according to conventional conditions or the conditions recommended by the manufacturer.

[0031] The various instruments, equipment, raw materials or reagents used in the embodiments of the present invention are not particularly limited in their sources and are all conventional products that can be purchased through regular commercial channels or prepared according to conventional methods well known to those skilled in the art.

[0032] Data source: The data studied in this article are derived from The Cancer Genome Atlas (TCGA) (https: / / cancergenome.nih.gov / ) and Gene Set Cancer Analysis (GSCA) (http: / / bioinfo.life.hust.edu.cn / GSCA).

[0033] The accession number of SSRP1 is NM_003146.3

[0034] The accession number of SUPT16H is NM_007192.4

[0035] Example 1

[0036] To investigate the differential expression characteristics of SSRP1 and SUPT16H, core components of the FACT complex, between tumors and normal tissues

[0037] The present invention systematically performed a pan-cancer analysis of SSRP1 and SUPT16H in 33 cancer types, using differential expression analysis to reflect the pan-cancer expression levels of SSRP1 and SUPT16H in tumor tissues compared with adjacent adjacent tissues. Figure 1 As shown in Figure 2, differential expression analysis showed that SSRP1 and SUPT16H showed a significant upregulation trend in pan-cancer and were consistently overexpressed in most of the cancers tested. Figure 1 As shown in A, SSRP1 and SUPT16H are significantly overexpressed in thyroid cancer (THCA), breast cancer (BRCA), head and neck squamous cell carcinoma (HNSC), renal clear cell carcinoma (KIRC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), gastric adenocarcinoma (STAD), esophageal cancer (ESCA) and prostate adenocarcinoma (PRAD).

[0038] Gene Set Variation Analysis (GSVA) was used to evaluate the overall expression dynamics of the two genes in pan-cancer and calculate the comprehensive score (i.e., FACT score). Figure 1 B analysis showed that the FACT scores of most cancer types showed an upward trend, among which the FACT scores of BRCA, LIHC, LUAD, STAD, bladder urothelial carcinoma (BLCA), colon adenocarcinoma (COAD), head and neck squamous cell carcinoma (HNSC), renal chromophobe cell carcinoma (KICH) and LUSC were significantly increased. In contrast, the FACT scores of thyroid cancer (THCA) and renal clear cell carcinoma (KIRC) showed a downward trend.

[0039] like Figure 1The molecular subtype analysis shown in C showed that there was significant heterogeneity in the expression of SSRP1 / SUPT16H among different subtypes of BRCA, KIRC, etc. There were significant differences in the expression of SSRP1 and SUPT16H among different subtypes of BRCA, KIRC, LUAD and STAD. Figure 1 As shown in Figure D, high expression of SSRP1 and SUPT16H was significantly associated with shortened overall survival (OS) in patients with adrenocortical carcinoma (ACC) and LIHC, indicating that the FACT complex may be a key driver of tumor progression and an important biomarker for patient prognosis.

[0040] Example 2 Somatic mutation analysis and its impact on patient survival

[0041] Based on data from the GSCA database, we performed somatic mutation analysis to assess the frequency of single nucleotide variants (SNVs) in the SSRP1 and SUPT16H genes across 33 cancer types. Spearman rank correlation analysis was used to assess the correlation between copy number variation (CNV) and SSRP1 and SUPT16H expression levels. P values were adjusted for FDR, with a significance threshold of FDR < 0.05. In addition, survival analyses were performed to assess the impact of SSRP1 and SUPT16H mutations on patient prognosis.

[0042] Result analysis: Figure 2 As shown in Figure A, the SUPT16H gene is frequently mutated in uterine corpus endometrial carcinoma (UCEC), skin melanoma (SKCM), colon adenocarcinoma (COAD), and bladder urothelial carcinoma (BLCA), at 39%, 25%, 19%, and 18%, respectively. SSRP1 is also significantly mutated in UCEC, SKCM, COAD, and BLCA, at 25%, 12%, 9%, and 8%, respectively. These results suggest that SSRP1 and SUPT16H are frequently mutated in various cancers and jointly drive tumorigenesis.

[0043] like Figure 2 As shown in Figure B, CNV analysis revealed that SSRP1 and SUPT16H mRNA expression levels were strongly positively correlated with CNV status in most cancer types. This correlation was particularly pronounced in invasive breast cancer (BRCA), lung squamous cell carcinoma (LUSC), head and neck squamous cell carcinoma (HNSC), and lung adenocarcinoma (LUAD).

[0044] For prognostic analysis, such as Figure 2As shown in Figure C, CNV amplification of SSRP1 and SUPT16H was associated with significantly improved survival outcomes in patients with prostate adenocarcinoma (PRAD) and hepatocellular carcinoma (LIHC). In contrast, DNA methylation analysis showed that in LIHC, PRAD, COAD, and LUAD, the methylation levels of SSRP1 and SUPT16H in tumor tissues were significantly lower than those in normal tissues. This suggests that epigenetic mechanisms may regulate the expression of the two genes in these cancer types. In contrast, Figure 2 As shown in D, DNA methylation levels were significantly upregulated in head and neck squamous cell carcinoma (HNSC), renal clear cell carcinoma (KIRC), lung squamous cell carcinoma (LUSC), and thyroid carcinoma (THCA).

[0045] Example 3: Exploring the enrichment characteristics of FACT complex-related pathways in pan-cancer

[0046] Gene Set Enrichment Analysis (GSEA) was used to explore pan-cancer enrichment of pathways associated with the FACT complex using 50 signature gene sets from the Molecular Signatures Database (MSigDB; https: / / www.gsea-msigdb.org / gsea / msigdb / index.jsp). Samples from each cancer type were divided into high and low FACT enrichment groups based on a FACT score threshold of 30%. The "GSVA" R package was used to analyze pathway enrichment differences between the two groups across 33 cancer types. The enrichment of pathway gene sets was quantified using the Normalized Enrichment Score (NES): NES > 0 indicates pathway activation in the high FACT group, while NES < 0 indicates pathway inhibition in the low FACT group. Results were visualized using bubble plots to highlight pathways with significant differences between the groups.

[0047] Result analysis: Figure 3As shown, GSEA showed that multiple key pathways were significantly enriched in high-FACT score tumors across 33 cancer types: the PI3K / AKT / mTOR signaling pathway, epithelial-mesenchymal transition (EMT), G2 / M checkpoint, and mitotic spindle pathways were significantly upregulated in FACT-high expression tumors. Because these pathways are crucial for cell proliferation, survival, and mitosis, SSRP1 and SUPT16H may drive tumor progression by regulating these processes. In contrast, tumors in the low FACT score group were significantly enriched for pathways related to cellular stress response and immune regulation, including oxidative phosphorylation, myogenesis, hypoxia, IL-2 / Stat5 signaling, IL-6 / Jak-Stat3 signaling, inflammatory response, interferon α / γ response, coagulation, complement activation, and adipogenesis.

[0048] Example 4 Regulatory Role of SSRP1 and SUPT16H in Tumor Immune Microenvironment

[0049] To assess stromal and immune cell infiltration in tumors, gene expression data were analyzed based on the ESTIMATE algorithm. Spearman correlation analysis was also performed to examine the relationship between the GSVA scores of SSRP1 and SUPT16H and the four ESTIMATE-derived scores.

[0050] Results: In most tumor types, FACT scores were significantly negatively correlated with stromal scores, ESTIMATE scores, and immune scores, indicating that SSRP1 and SUPT16H may mediate immunosuppression in the tumor microenvironment. Figure 4 As shown in A, the FACT score was positively correlated with tumor purity, indicating that high FACT expression was associated with enhanced homogeneity of the tumor cell population. Further analysis of immune cell infiltration showed that Figure 4 As shown in Figure B, the FACT score was significantly negatively correlated with the infiltration levels of 10 immune cells, including mucosal-associated invariant T cells (MAIT cells), cytotoxic T cells, natural killer (NK) cells, follicular helper T (Tfh) cells, and macrophages. On the other hand, the FACT score was significantly positively correlated with the infiltration levels of 9 immune cells, such as CD8 naive T cells and central memory T cells.

[0051] Example 5 Enrichment and functional role of FACT complex in hepatocytes in the tumor microenvironment

[0052] The hepatocellular carcinoma (LIHC) single-cell dataset GSE149614 was obtained from the Gene Expression Omnibus (GEO) database. Figure 5As shown in Figure A, a total of 63,102 cells were analyzed in liver cancer tissue, covering six major cell types: T / NK cells, myeloid cells, hepatocytes, endothelial cells, fibroblasts, and B cells. The present invention analyzed the expression patterns of SSRP1 and SUPT16H in different cell types by calculating the average expression levels of SSRP1 and SUPT16H in tumor and normal samples and the proportion of cells expressing these genes. Furthermore, the average expression level of each gene across all cell types in LIHC was calculated to identify cell populations enriched for the gene.

[0053] Result analysis: Figure 5 As shown in Figure B, compared with adjacent normal tissue, the proportions of macrophages, hepatocytes, B cells, and fibroblasts in tumor tissue increased significantly, while the proportions of T / NK cells and myeloid cells decreased significantly. These results indicate that immune and stromal cell populations in the tumor microenvironment undergo reprogramming.

[0054] Further analysis based on the weighted comprehensive score of the single-cell enrichment algorithm revealed that Figure 5 As shown in C, the FACT complex is most enriched in hepatocytes. This indicates the importance of hepatocytes as the main host of the FACT complex in the tumor microenvironment. Figure 5 As shown in D, the FACT complex enrichment score of tumor-associated hepatocytes was significantly higher than that of adjacent normal hepatocytes. In addition, the core components of the FACT complex, SSRP1 and SUPT16H, were significantly upregulated in tumor tissues (especially hepatocytes). Figure 5 EF, indicating its functional role in tumor progression.

[0055] Example 6 Pseudo-time analysis of FACT complex enrichment in hepatocytes and analysis of intercellular communication profiles

[0056] Pseudo-time analysis based on the differential expression of SSRP1 and SUPT16H was performed. Figure 6 As shown in A, Figure 6 As shown in Figures BC, SSRP1 expression decreased slightly in the middle of the evolutionary phase and then increased significantly in the later phase, while SUPT16H expression increased steadily during the pseudo-time. These results suggest that SSRP1 and SUPT16H play key roles in hepatocyte differentiation and tumor progression, affecting the core stages of cancer development.

[0057] In addition, cell communication analysis showed that Figure 6 As shown in Figures D and E, hepatocytes with high FACT complex enrichment in LIHC send signals to neighboring cells at a similar frequency as those with low FACT enrichment. However, hepatocytes with high FACT enrichment receive signals from other cell types at a higher frequency, suggesting that FACT may play a central regulatory role in shaping the tumor microenvironment.

[0058] Example 7 Spatial expression and dynamic trajectory of the FACT complex during liver cancer tumor progression

[0059] By Louvain cluster analysis, Figure 7 As shown in AB, LIHC tissue sections were divided into 7 main clusters and their corresponding 31 subclusters, each cluster reflecting a different state of the tumor microenvironment, indicating the spatial heterogeneity of the tumor. Figure 7 As shown in CD, gene expression heat map analysis showed that SSRP1 and SUPT16H were expressed in clusters 2, 3, and 4, and the expression level of cluster 3 was particularly significant. Spatial trajectory reconstruction based on the PSTS method showed that Figure 7 As shown in E, the progression path from the tumor core area (cluster 0) to clusters 2 and 3 suggests the metastatic process of tumor cells in the microenvironment.

[0060] like Figure 7 Figure F further reveals a branching pattern within subclusters, with tumor cells from subcluster 10 (originating from subcluster 0) migrating through subcluster 2 (originating from subcluster 2) to subcluster 1 (originating from subcluster 3). Gene trajectory analysis showed that SSRP1 and SUPT16H expression increased along the tumor progression pathway (PTST score > 0 and P < 0.05), confirming their key role in tumor development (Table 1).

[0061] Table 1 Expression trends of SSRP1 and SUPT16H genes during tumor progression

[0062]

[0063] Example 8 Validation of the FACT complex in hepatocellular carcinoma and immunophenotypic scoring analysis

[0064] The present invention obtained the public datasets GSE87630 and GSE101685 from the GEO database to verify the expression of SSRP1 and SUPT16H in hepatocellular carcinoma (LIHC). By analyzing tumor samples and normal samples, it was found that Figure 8 As shown in A, both genes were significantly overexpressed in tumor tissues, further demonstrating their key roles in the progression of HCC. In addition, immunophenotypic score (IPS) analysis showed that Figure 8 As shown in Figure B, patients with low FACT scores had higher IPS values, which were independent of PD-1 and CTLA-4 pathway status. This suggests that lower FACT expression is associated with a more favorable immune response, thereby improving the efficacy of immunotherapy for LIHC.

[0065] Example 9 Experimental Verification of FACT Complex Expression

[0066] To experimentally validate the SSRP1 and SUPT16H expression patterns observed in computational analysis, nine cell lines representing three cancer types (hepatocellular carcinoma (LIHC), clear cell renal cell carcinoma (KIRC), and breast cancer (BRCA)) were selected. These cancer cell lines included 786-O and Caki-1 cells from KIRC, MDA-MB-468 and MDA-MB-231 cells from BRCA, and MHCC-97H and HepG2 cells from LIHC. The corresponding normal cell lines were HK-2 (kidney), MCF-10A (breast epithelial), and L-02 (liver). Cells were seeded at a density of 50,000 cells per well in 6-well plates and cultured to confluence. Table 2 lists gene-specific primers for SSRP1, SUPT16H, and the internal control gene beta-actin (ACTB). Gene expression levels were normalized using ACTB as an internal control, and relative expression was calculated using the 2^(-ΔΔCT) method. Results are expressed as fold change relative to the corresponding normal cell line for each cancer type.

[0067] Table 2 qRT-PCR primer sequences used in the present invention

[0068]

[0069] Result analysis: Figure 8 As shown in C, in LIHC, the expression levels of SSRP1 and SUPT16H in MHCC-97H and HepG2 cell lines were significantly higher than those in normal liver cells L-02, verifying the results of computational biology analysis; in renal clear cell carcinoma (KIRC), the expression levels of SSRP1 and SUPT16H in 786-O cell line did not change significantly compared with normal kidney cells HK-2, but SSRP1 expression was upregulated and SUPT16H was downregulated in Caki-1 cells, further supporting the computational biology findings; in breast cancer (BRCA), the expression levels of SSRP1 and SUPT16H in MDA-MB-231 and MDA-MB-468 cell lines were significantly increased compared with normal breast epithelial cells MCF-10A, verifying the initial prediction.

[0070] Example 10 Drug sensitivity analysis

[0071] The present invention investigates the relationship between cancer drug sensitivity (GDSC) and SSRP1 and SUPT16H mRNA expression levels.

[0072] Result analysis: Figure 9As shown in Figure A, high expression of these two genes is associated with increased resistance to eight drugs (including 17-AAG, CHIR-99021, dasatinib, PD-0325901, REDA119, selumetinib, TGX221, and trametinib). Conversely, high expression of SSRP1 and SUPT16H is associated with increased sensitivity to 22 drugs (such as FK866 and NPK76-II-72-1). This inverse relationship suggests that the expression levels of these FACT complex genes affect drug efficacy, suggesting the potential for personalized drug screening based on gene expression profiling.

[0073] To further verify the research results, the present invention focused on the commercially available drug FK866 and evaluated its effect using CCK-8 and colony formation assays. Figure 9 B CCK-8 experiments showed that a dose of 0.1 μM could trigger the cytotoxicity of FK866; Figure 9 As shown in C, the colony formation assay showed that a dose of 10 μM significantly inhibited the growth of cell colonies.

[0074] Although the above embodiment provides a detailed description of the present invention, it is only a part of the embodiments of the present invention, not all of the embodiments. People can also obtain other embodiments based on this embodiment without creativity, and these embodiments all fall within the scope of protection of the present invention.

Claims

1. Use of inhibitors of SSRP1 and / or SUPT16H genes in the preparation of pharmaceutical compositions for treating cancer.

2. Use of an inhibitor that inhibits the function of the FACT complex in the preparation of anti-tumor drugs.

3. The use according to any one of claims 1-2, characterized in that The inhibitor is FK688 and / or NPK76-II-72-1.

4. An application in the preparation of a reagent for assessing cancer prognosis based on detecting SSRP1 and / or SUPT16H gene expression levels.

5. Use of an agent that inhibits SSRP1 and / or SUPT16H gene expression in the preparation of a drug for treating patients with hepatocellular carcinoma.

6. A drug for treating patients with hepatocellular carcinoma and / or patients with lung metastasis of hepatocellular carcinoma, characterized in that: The drug comprises an agent for inhibiting the expression of SSRP1 and / or SUPT16H genes and pharmaceutically acceptable excipients.

7. Use of a reagent for detecting the expression level of FACT complex subunit gene SSRP1 and / or SUPT16H gene in the preparation of a cancer diagnosis or prognosis assessment kit.

8. The use according to claim 1 or 7, characterized in that The cancer is breast cancer, lung adenocarcinoma, renal clear cell carcinoma, and hepatocellular carcinoma.

9. The use according to claim 4 or 7, characterized in that The reagent includes a primer pair for specifically amplifying SSRP1 and / or SUPT16H; the SSRP1 primer pair includes an upstream primer sequence as shown in SEQ ID NO.1 and a downstream primer sequence as shown in SEQ ID NO.2; the SUPT16H primer pair includes an upstream primer sequence as shown in SEQ ID NO.3 and a downstream primer sequence as shown in SEQ ID NO.

4.

10. The use according to claim 9, characterized in that The reagent further includes an internal reference gene; the internal reference gene is ACTB; the ACTB includes an upstream primer sequence as shown in SEQ ID NO.5 and a downstream primer sequence as shown in SEQ ID NO.6.

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