Identification of a novel cellular senescence-related signature for the prediction of prognosis and immunotherapy response in colon cancer

Rho GTPases are known to promote colon cancer cell invasion and metastasis by modulating cell motility and adhesion. However, the clinical implications of Rho GTPase-related genes in prognosis and treatment response for colon cancer remain underexplored.We identified Rho GTPase-related prognostic genes using univariate Cox regression and applied Least Absolute Shrinkage and Selection Operator (LASSO) regression to refine these genes and develop a prognostic model. The Rho GTPase-related gene signature was analyzed for associations with clinical outcomes, immune status, immunotherapy, and chemotherapy response in colon cancer patients.A Rho GTPase-related 12-gene signature was established to predict prognosis across training and validation cohorts. Both univariate and multivariate analyses confirmed the Rho GTPase risk score as an independent prognostic factor. The model's area under the curve (AUC) and decision curve analysis (DCA) outperformed traditional TNM staging and several existing models. Immune cell analysis showed high Rho GTPase risk scores correlated with increased macrophage/monocyte and cancer-associated fibroblast infiltration. Tumor Immune Dysfunction and Exclusion (TIDE) analysis revealed an association between high Rho GTPase risk scores and immune dysfunction, suggesting potential resistance to immune checkpoint inhibitors in high-risk patients. Additionally, differential sensitivity to 59 chemotherapeutics was observed: high-risk patients showed greater sensitivity to CCT007093, CGP.082996, and AS601245, while low-risk patients were more sensitive to BMS.708163, NSC.87877, and Cisplatin, informing potential treatment choices.The Rho GTPase-related 12-gene signature offers a valuable tool for predicting prognosis and therapeutic response in colon cancer, supporting personalized treatment strategies and improved patient outcomes.

The study aimed to determine whether ULBP2 was associated with prognosis and immune infiltration in colon cancer (CC) and provided important molecular basis inorderto early non-invasive diagnosis and immunotherapy of CC. Using The Cancer Genome Atlas database (TCGA) and ImmPort database, we extracted messenger RNA (mRNA) data of CC and immune-related genes, then we used "limma" package, "survival" package, and Venn overlap analysis to obtain the differentially expressed mRNA (DEmRNA) associated with prognosis and immunity of CC patients. "pROC" package was used to analyze receiver operating characteristics (ROC) of target gene. We used chi-square test and two-class logistics model to identify clinicopathological parameters that correlated with target gene expression. In order to determine the effects of target gene expression and clinicopathological parameters on survival, univariate and multivariate cox regression analyses were performed. We analyzed the related functions and signaling pathways of target gene by enrichment analysis. Finally, the correlation between target gene and tumor immune infiltrating was explored by ssGSEA and spearman correlation analysis. Results showed that ULBP2 was a target gene associated with immunity and prognosis in CC patients. CC patients with higher ULBP2 expression had poor outcomes. In terms of ROC, ULBP2 had an area under the curve (AUC) of 0.984. ULBP2 was associatedwith T stage, N stage, and pathologic stage of CC patients, and served as an independent predictor of overall survival in CC patients. Functional enrichment analysis revealed ULBP2 was obviously enriched in pathways connected with carcinogenesis and immunosuppression. The expression of ULBP2 was significantly associated with tumor immune cells and immune checkpoints according to ssGSEA and spearman correlation analysis. To conclude, our study suggested that ULBP2 was associated with tumor immunity, and might be a biomarker associated with the diagnosis and prognosis of CC patients, and a potential target of CC immunotherapy.

The role of metabolic reprogramming of the tumor immune microenvironment in cancer development and immune escape has increasingly attracted attention. However, the predictive value of differences in metabolism-immune microenvironment on the prognosis of colon cancer (CC) and the response to immunotherapy have not been elucidated. The aim of this study was to investigate changes in metabolism and immune profile of CC and to identify a reliable signature for predicting prognosis and therapeutic response. The metabolism and immune-related differential genes in CC were screened out by differential gene expression analysis. A metabolism and immune related prognostic signature was established by the least absolute shrinkage and selection operator (LASSO) Cox algorithm. The training cohort with 417 patients from The Cancer Genome Atlas (TCGA) database and the validation cohort of 232 patients from GSE17538 were used to confirm the robustness of the prognostic signature. Immunohistochemical staining scores were used to assess gene expression levels in our clinical samples. Gene ontology (GO) analysis, gene set enrichment analysis (GSEA), single nucleotide variation (SNV) analysis, immune infiltration and immune factors analysis were used to explore the characteristics of patients with different subtypes. Multiple cancer immunotherapy datasets were used to assess the response of patients with different subtypes to immune checkpoint inhibitors. We established the Metabolism and Immune-Related Prognostic Score (MIRPS) based on six genes (CD36, PCOLCE2, SCG2, CALB2, STC2, CLDN23) to predict the prognosis of CC patients. We found a correlation between MIRPS and the malignant phenotype, microsatellite subtype, mutation load, and immune escape in CC. Tumors with high MIRPS presented a higher tumor mutation load and a more prominent immunosuppressive microenvironment. This subset of patients may potentially respond well to immune checkpoint inhibitor therapy. MIRPS may be used as a novel prognostic tool for CC and have potential value for immunotherapy response prediction.

Construction of a prognostic model for colon cancer by combining endoplasmic reticulum stress responsive genes

Activated Cdc42-associated kinase 1 (ACK1), a kind of tyrosine kinase, is considered to be an oncogene in many cancers, and it is likely to become a potential target for cancer treatment. We found that the expression of the ACK1 gene in colon cancer was higher than that in normal tissues adjacent to cancer, and high expression of the ACK1 gene was associated with poor prognosis of patients. We assessed the prognosis of colon cancer based on ACK1-related genes and constructed a model that can predict the prognosis of colon cancer patients in colon cancer data from The Cancer Genome Atlas (TCGA) database. We then explored the relationship between ACK1 and the immune microenvironment of colon cancer. The overexpression of ACK1 might hinder the function of antigen-presenting cells. The colon cancer prognosis prediction model we constructed has certain significance for clinicians to judge the prognosis of patients with colon cancer. The expression of the ACK1 gene might affect the infiltration level of a variety of immune cells and immunomodulators in the immune microenvironment.

Identification of lactylation-associated fibroblast subclusters predicting prognosis and cancer immunotherapy response in colon cancer.

Polyamine metabolism is closely associated with tumorigenesis, progression, and the tumor microenvironment (TME). This study aimed to determine whether polyamine metabolism-related genes (PMRGs) could predict prognosis and immunotherapy efficacy in Breast Cancer (BC). We conducted a comprehensive multi-omics analysis of PMRG expression profiles in BC. Consensus cluster analysis was used to identify PMRG expression subtypes in the METABRIC cohort. Univariate and multivariate Cox regression analyses were performed to identify independent prognostic genes, which were subsequently used to construct a predictive model for BC, along with a novel nomogram based on PMRGs. The model was validated using an independent cohort (GSE86166). Independent prognostic genes were further verified in BC tissues using quantitative real-time PCR (qRT-PCR), Semi-quantitative Western blot, and immunohistochemistry. Additionally, we analyzed the immune microenvironment and enriched pathways across different subtypes using multiple algorithms. Finally, the "oncoPredict" R package was used to assess potential drug sensitivities in high-risk and low-risk groups. Seventeen polyamine metabolism genes were identified. PMRGs were abundantly expressed in tumor cells, with 12 survival-related genes being selected. In the METABRIC cohort, two PMRG expression subtypes were identified, with cancer- and immune-related pathways being more active in cluster B, which was associated with a worse prognosis. Six genes were used to construct a prognostic model through univariate and multivariate Cox regression analyses. The predictive performance of the polyamine metabolism model was validated by ROC curve analysis (training cohort: METABRIC, AUC3years=0.684; validation cohort: GSE86166, AUC3years=0.682). A nomogram combining risk scores and clinicopathological features was constructed. Decision Curve Analysis (DCA) demonstrated that the model could guide clinical treatment strategies. Four high-risk independent prognostic factors (OAZ1, SRM, SMOX, and SMS) were validated as being upregulated in breast cancer tissues. The model successfully stratified BC patients into high-risk and low-risk groups, with the high-risk group exhibiting poorer clinical outcomes. Functional analysis revealed significant differences in immune status and drug sensitivity between high-risk and low-risk groups. This study elucidated the biological characteristics of PMRG expression subtypes in BC, identifying a polyamine-related prognostic signature and four novel biomarkers to accurately predict prognosis and immunotherapy response in BC patients.

Identification of immune infiltration-related LncRNA FAM83C-AS1 for predicting prognosis and immunotherapy response in colon cancer

Rsf-1 (HBXAP) was recently reported to be overexpressed in various cancers and associated with the malignant behavior of cancer cells. However, the expression of Rsf-1 and its biological roles in colon cancer have not been reported. The molecular mechanism of Rsf-1 in cancer aggressiveness remains ambiguous. In the present study, we analyzed the expression pattern of Rsf-1 in colon cancer tissues and found that Rsf-1 was overexpressed in 50.4 % of colon cancer specimens. There was a significant association between Rsf-1 overexpression and TNM stage (p = 0.0205), lymph node metastasis (p = 0.0025), and poor differentiation (p = 0.0235). Furthermore, Rsf-1 overexpression correlated with a poor prognosis in colon cancer patients (p = 0.0011). In addition, knockdown of Rsf-1 expression in HT29 and HCT116 cells with high endogenous Rsf-1 expression decrease cell proliferation and colony formation ability. Further analysis showed that Rsf-1 knockdown decreased cyclin E expression and phospho-Rb level. In conclusion, Rsf-1 is overexpressed in colon cancers and contributes to malignant cell growth by cyclin E and phospho-Rb modulation, which makes Rsf-1 a candidate therapeutic target in colon cancer.

Inflammatory response signature score model for predicting immunotherapy response and pan-cancer prognosis

Lactylation is a type of chemical modification involving the introduction of lactyl groups to a molecule which can affect the interactions between tumor cells and their microenvironment. This study aims to evaluate the possible role of lactylation-related gene signature in the prediction of both prognosis and immunotherapy response in bladder cancer (BLCA). Lactylation-related genes were obtained from the published work and two subtypes (cluster A and B) were identified through unsupervised clustering. The differences including clinical features, differentially expressed genes (DEGs), pathways, and immune cell infiltration between these two clusters were thoroughly examined. By utilizing the DEGs between the two clusters, a lactylation score was identified to predict the overall survival status and the response of BLCA patients receiving immunotherapy. Our results demonstrated that patients with a high lactylation score tended to have a worse survival period and increased immune cell infiltration level. Further analysis showed that high lactylation score may be associated with higher sensitivity to immune checkpoint inhibitor (ICI) treatment which is crucial in the identification of the suitable candidates for ICI therapy. Our results emphasize the possible predictive role of lactylation-related gene signature both in the survival rates of BLCA and its implications for treatment strategies.

Background Colon cancer (CC) is a malignant tumor with a high incidence and poor prognosis. Accumulating evidence shows that the immune signature plays an important role in the tumorigenesis, progression, and prognosis of CC. Our study is aimed at establishing a novel robust immune-related gene pair signature for predicting the prognosis of CC. Methods Gene expression profiles and corresponding clinical information are obtained from two public data sets: The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO, GSE39582). We screened out immune-related gene pairs (IRGPs) associated with prognosis in the discovery cohort. Lasso-Cox proportional hazard regression was used to develop the best prognostic signature model. According to this, the patients in the validation cohort were divided into high immune-risk group and low immune-risk group, and the prediction ability of the signature model was verified by survival analysis and independent prognostic analysis. Results A total of 17 IRGPs composed of 26 IRGs were used to construct a prognostic-related risk scoring model. This model accurately predicted the prognosis of CC patients, and the patients in the high immune-risk group indicated poor prognosis in the discovery cohort and validation cohort. Besides, whether in univariate or multivariate analysis, the IRGP signature was an independent prognostic factor. T cell CD4 memory resting in the low-risk group was significantly higher than that in the high-risk group. Functional analysis showed that the biological processes of the low-risk group included “TCA cycle” and “RNA degradation,” while the high-risk group was enriched in the “CAMs” and “focal adhesion” pathways. Conclusion We have successfully established a signature model composed of 17 IRGPs, which provides a novel idea to predict the prognosis of CC patients.

Retinoblastoma binding protein 4 up-regulation is correlated with hepatic metastasis and poor prognosis in colon cancer patients

PurposeImprinted genes are often identified as key players in the etiology and prognosis of many tumors; however, the role they play in colon cancer remains unclear. Along with the development of big data analysis came the discovery of a wealth of genetic prognostic factors, like microsatellite instability for colon cancer, which need to be taken into consideration when evaluating new biomarkers for the disease.MethodsWe systematically mined public databases to find recurrence free survival (RFS)-related imprinted genes for colon cancer patients on the mRNA level by univariate and multivariate survival analyses. We then investigated the association of methylation status and microRNA expression of the targeted imprinted genes with survival rate of colon cancer patients. Lastly, in a clinical study we used qRT-PCR and immunohistochemistry to quantify mRNA and protein expression of the imprinted genes that related to RFS in our bioinformatics screening, respectively, in 20 tumor tissues compared to paired adjacent tissues.ResultsThe results show that paternally expressed gene 3 (PEG3) is the only imprinted gene related to colon cancer patient prognosis on the mRNA level in our datasets, and high mRNA expression of PEG3 is associated with a poor prognosis. Furthermore, the methylation beta value of cg13960339, as well as the expression of 4 microRNAs, negatively correlated with PEG3 mRNA level and were correlated with the prognosis of colon cancer patients. Moreover, the expression of PEG3 mRNA in colon cancer is significantly lower, but PEG3 protein expression is significantly higher compared to that in normal tissues.ConclusionPEG3 is likely associated with the progression and prognosis of colon cancer.

BackgroundCancer originates from dysregulated cell proliferation driven by driver gene mutations. Despite numerous algorithms developed to identify genomic mutational signatures, they often suffer from high computational complexity and limited clinical applicability.MethodsHere, we presented ProgModule, an advanced computational framework designed to identify mutation driver modules for cancer prognosis and immunotherapy response prediction. In ProgModule, we introduced the Prognosis-Related Mutually Exclusive Mutation (PRMEM) score, which optimizes the balance between exclusive mutation coverage and the incorporation of mutation combination mechanisms critical for cancer prognosis.ResultsApplying to BLCA and HNSC cohorts, ProgModule successfully identified driver modules that stratify patients into distinct prognostic subgroups, and the combination of these modules could serve as an effective prognostic biomarker. Extending our method to diverse cancers, ProgModule presented robust prognostic performance and stability across model parameters, including stopping criteria and network topology. Moreover, our analysis suggested that driver modules can predict immunotherapeutic benefit more effectively than existing signatures. Further analyses based on published CRISPR data indicated that genes within these modules may serve as potential therapeutic targets.ConclusionsAltogether, ProgModule emerges as a powerful tool for identifying mutation driver modules as prognostic and immunotherapy response biomarkers, and genes within these modules may be used as potential therapeutic targets for cancer, offering new insights into precision oncology.