Comprehensive genomics analysis of aging related gene signature to predict the prognosis and drug resistance of colon adenocarcinoma

Abstract

Background: Colon adenocarcinoma (COAD) is a heterogeneous tumor and senescence is crucial in the occurrence of cancer. This study aimed to identify senescence-based subtypes and construct a prognostic signature to predict the prognosis and guide immunotherapy or chemotherapy decisions for COAD patients.Methods: Based on the single-cell RNA sequencing (scRNA-seq) data of 13 samples from the Gene Expression Omnibus (GEO) database, we assessed cellular senescence characteristics. Transcriptome data, copy number variations (CNVs) and single nucleotide variations (SNVs) data were obtained from The Cancer Genome Atlas (TCGA) database. GSE39582 and GSE17537 were used for validation. Senescence subtypes were identified using unsupervised consensus clustering analysis, and a prognostic signature was developed using univariate Cox analysis and least absolute shrinkage and selection operator (LASSO). Response of risk groups to chemotherapy was predicted using the half-maximal inhibitory concentration (IC50) values. We further analyzed the relationship between risk gene expression and methylation level. The prediction performance was assessed by nomogram.Results: Senescence-related pathways were highly enriched in malignant cells and bulk RNA-seq verified cellular senescence. Three senescence subtypes were identified, in which patients in clust3 had poorest prognosis and higher T stage, accompanied with higher tumor mutation burden (TMB) and mutations, activated inflammatory response, more immune cell infiltration, and higher immune escape tendency. A senescence-based signature using 11 genes (MFNG, GPRC5B, TNNT1, CCL22, NOXA1, PABPC1L, PCOLCE2, MID2, CPA3, HSPA1A, and CALB1) was established, and accurately predicted a lower prognosis in high risk patients. Its robustness was validated by external cohort. Low risk patients were more sensitive to small molecule drugs including Erlotinib, Sunitinib, MG-132, CGP-082996, AZ628, Sorafenib, VX-680, and Z-LLNle-CHO. Risk score was an independent prognostic factor and nomogram confirmed its reliability. Four risk genes (CALB1, CPA3, NOXA1, and TNNT1) had significant positive correlation with their methylation level, while six genes (CCL22, GPRC5B, HSPA1A, MFNG, PABPC1L, and PCOLCE2) were negatively correlated with their methylation level.Conclusion: This study provides novel understanding of heterogeneity in COAD from the perspective of senescence, and develops signatures for prognosis prediction in COAD.

Integrative analysis reveals a four-gene signature for predicting survival and immunotherapy response in colon cancer patients using bulk and single-cell RNA-seq data

“…scRNA-seq, an innovative methodology, allows us to precisely distinguish different cell types within tissues, providing a powerful tool to understand the complex mix of cell populations when comparing, for instance, tumor and normal tissues (18,20). Additionally, scRNA-seq has the potential to uncover crucial hub genes related to tumor initiation and cancer advancement, which could be instrumental in shaping personalized therapies for CC patients (21)(22)(23)(24)(25)(26)36).…”

“…Recent advances in scRNA-seq help categorize colorectal cancer cells, explore gene differences, and distinguish between primary tumors and metastases (18)(19)(20). A few studies have effectively combined scRNA-seq and bulkRNA-seq data to establish and validate prognostic signatures in CC (21)(22)(23)(24)(25)(26), such as identifying genes related to membrane tension and aging-related or autophagy-related genes (21)(22)(23).…”

Integrated proteogenomic and metabolomic characterization of papillary thyroid cancer with different recurrence risks

“…6a). HSP6A was found to be a potential biomarker to predict the prognosis of TC [27]. ECM1 is associated with tumor invasiveness and poor prognosis in various cancer types [28].…”