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Exploring the Relationship Between Senescence and Colorectal Cancer in Prognosis, Immunity, and Treatment.

ABSTRACT: Background: Senescence, as an effective barrier against tumorigenesis, plays a critical role in cancer therapy. However, the role of senescence in colorectal cancer (CRC) has not yet been reported. This study aimed to build a prognostic signature for the prognosis of patients with CRC based on senescence-related genes. Methods: A prognostic signature was built from TCGA based on differentially expressed senescence-related genes by the least absolute shrinkage and selection operator (LASSO) and Cox regression analyses, which were further validated using two Gene Expression Omnibus (GEO) cohorts. The CIBERSORT and ssGSEA algorithms were utilized to analyze the infiltrating abundance of immune cells. The relationship of signature with the immune therapy and the sensitivity of different therapies was explored. Results: We found 93 genes associated with senescence that were differentially expressed. Based on expression and clinical parameters, we developed a senescence-related prognostic signature and its effectiveness was verified using two external validation cohorts. Overall survival was predicted using a prognostic nomogram that incorporated the predictive values of the risk score and clinical traits. Additionally, the risk score was significantly correlated with immune cells infiltration, tumor immune microenvironment (TME) score, immune checkpoints, immunotherapeutic efficacy, and chemotherapy sensitivity. Conclusion: The senescence-related prognostic model can well predict the prognosis, immunotherapeutic response, and identify potential drug targets, which can help guide individualized treatment.

SUBMITTER: Dong K

PROVIDER: S-EPMC9240351 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Introduction: Metabolic dysregulation is a widely acknowledged contributor for the development and tumorigenesis of colorectal cancer (CRC), highlighting the need for reliable prognostic biomarkers in this malignancy. Methods: Herein, we identified key genes relevant to CRC metabolism through a comprehensive analysis of lactate metabolism-related genes from GSEA MsigDB, employing univariate Cox regression analysis and random forest algorithms. Clinical prognostic analysis was performed following identification of three key genes, and consistent clustering enabled the classification of public datasets into three patterns with significant prognostic differences. The molecular pathways and tumor microenvironment (TME) of these patterns were then investigated through correlation analyses. Quantitative PCR was employed to quantify the mRNA expression levels of the three pivotal genes in CRC tissue. Single-cell RNA sequencing data and fluorescent multiplex immunohistochemistry were utilized to analyze relevant T cells and validate the correlation between key genes and CD4+ T cells. Results: Our analysis revealed that MPC1, COQ2, and ADAMTS13 significantly stratify the cohort into three patterns with distinct prognoses. Additionally, the immune infiltration and molecular pathways were significantly different for each pattern. Among the key genes, MPC1 and COQ2 were positively associated with good prognosis, whereas ADAMTS13 was negatively associated with good prognosis. Single-cell RNA sequencing (scRNA-seq) data illustrated that the relationship between three key genes and T cells, which was further confirmed by the results of fluorescent multiplex immunohistochemistry demonstrating a positive correlation between MPC1 and COQ2 with CD4+ T cells and a negative correlation between ADAMTS13 and CD4+ T cells. Discussion: These findings suggest that the three key lactate metabolism genes, MPC1, COQ2, and ADAMTS13, may serve as effective prognostic biomarkers and support the link between lactate metabolism and the immune microenvironment in CRC.

Project description:BackgroundEMT is an important biological process in the mechanism of tumor invasion and metastasis. However, there are still many unknowns about the specific mechanism of EMT in tumor. At present, a comprehensive analysis of EMT-related genes in colorectal cancer (CRC) is still lacking.MethodsAll the data were downloaded from public databases including TCGA database (488 tumor samples and 52 normal samples) as the training set and the GEO database (GSE40967 including 566 tumor samples and 19 normal samples, GSE12945 including 62 tumor samples, GSE17536 including 177 tumor samples, GSE17537 including 55 tumor samples) as the validation sets. One hundred and sixty-six EMT-related genes (EMT-RDGs) were selected from the Molecular Signatures Database. Bioinformatics methods were used to analyze the correlation between EMT-RDGs and CRC prognosis, metastasis, drug efficacy, and immunity.ResultsWe finally obtained nine prognostic-related EMT-RDGs (FGF8, NOG, PHLDB2, SIX2, SNAI1, TBX5, TIAM1, TWIST1, TCF15) through differential expression analysis, Unicox and Lasso regression analysis, and then constructed a risk prognosis model. There were significant differences in clinical characteristics, 22 immune cells, and immune functions between the high-risk and low-risk groups and the different states of the nine prognostic-related EMT-RDGs. The methylation level and mutation status of nine prognostic-related EMT-RDGs all affect their regulation of EMT. The Cox proportional hazards regression model was also constructed by the methylation sites of nine prognostic-related EMT-RDGs. In addition, the expression of FGF8, PHLDB2, SIX2, and SNAIL was higher and the expression level of NOG and TWIST1 was lower in the non-metastasis CRC group. Nine prognostic-related EMT-RDGs also affected the drug treatment response of CRC.ConclusionsTargeting these nine prognostic-related EMT-RDGs can regulate CRC metastasis and immune, which is beneficial for the prognosis of CRC patients, improve drug sensitivity in CRC patients.

Project description:Tumor formation is closely associated with disulfidptosis, a new form of cell death induced by disulfide stress-induced. The exact mechanism of action of disulfidptosis in pancreatic cancer (PCa) is not clear. This study analyzed the impact of disulfidptosis-related genes (DRGs) on the prognosis of PCa and identified clusters of DRGs, and based on this, a risk score (RS) signature was developed to assess the impact of RS on the prognosis, immune and chemotherapeutic response of PCa patients. Based on transcriptomic data and clinical information from PCa tissue and normal pancreatic tissue samples obtained from the TCGA and GTEx databases, differentially expressed and differentially surviving DRGs in PCa were identified from among 15 DRGs. Two DRGs clusters were identified by consensus clustering by merging the PCa samples in the GSE183795 dataset. Analysis of DRGs clusters about the PCa tumor microenvironment and differential analysis to obtain differential genes between the two DRG clusters. Patients were then randomized into the training and testing sets, and a prognostic prediction signature associated with disulfidptosis was constructed in the training set. Then all samples were divided into high-disulfidptosis-risk (HDR) and low-disulfidptosis-risk (LDR) subgroups based on the RS calculated from the signature. The predictive efficacy of the signature was assessed by survival analysis, nomograms, correlation analysis of clinicopathological characteristics, and the receiver operating characteristic (ROC) curves. To assess differences between different risk subgroups in immune cell infiltration, expression of immune checkpoint molecules, somatic gene mutations, and effectiveness of immunotherapy and chemotherapy. The GSE57495 dataset was used as external validation, reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was used to detect the expression levels of DRGs. A total of 12 DRGs with differential expression and prognosis in PCa were identified, based on which a risk-prognosis signature containing five differentially expressed genes (DEGs) was developed. The signature was a good predictor and an independent risk factor. The nomogram and calibration curve shows the signature's excellent clinical applicability. Functional enrichment analysis showed that RS was associated with tumor and immune-related pathways. RS was strongly associated with the tumor microenvironment, and analysis of response to immunotherapy and chemotherapy suggests that the signature can be used to assess the sensitivity of treatments. External validation further demonstrated the model's efficacy in predicting the prognosis of PCa patients, with RT-qPCR and immunohistochemical maps visualizing the expression of each gene in PCa cell lines and the tissue. Our study is the first to apply the subtyping model of disulfidptosis to PCa and construct a signature based on the disulfidptosis subtype, which can provide an accurate assessment of prognosis, immunotherapy, and chemotherapy response in PCa patients, providing new targets and directions for the prognosis and treatment of PCa.

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Exploring the Relationship Between Senescence and Colorectal Cancer in Prognosis, Immunity, and Treatment.

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