Dataset Information

A KRAS-Associated Signature for Prognostic, Immune and Chemical Anti-Cancer Drug-Response Prediction in Colon Cancer.

ABSTRACT: Background: KRAS mutation, one of the most important biological processes in colorectal cancer, leads to poor prognosis in patients. Although studies on KRAS have concentrated for a long time, there are currently no ideal drugs against KRAS mutations. Methods: Different expression analysis and weighted gene coexpression network analysis was conducted to select candidate genes. Log-rank tests and Cox regression picked out the prognostic genes to build a KRAS-related gene prognostic score (KRGPS). A nomogram based on KRGPS was built to predict survival of clinical patients. Comprehensive analysis showed the prognosis, immune microenvironment and response to immune therapy and chemotherapy in KRGPS subgroups. Results: We collected a KRGPS from the set of two genes GJB6 and NTNG1, with low-KRGSP patients having better progression-free survival (PFS). Low KRGPS is correlated with high infiltration of activated NK cells, plasma cells and activated memory CD4 T cells and that these cells benefit more from immune checkpoint inhibitor therapy. However, high KRGPS is associated with high infiltration of activated mast cells, pathways of immune dysregulation and a high ratio of TP53 and KRAS mutations. KRGPS subgroups are also sensitive to chemotherapy differently. A nomogram, established based on the KRGPS and pathological stage, predict 3- and 5-years PFS well. Conclusions: The KRAS-associated score acts as a promising signature to distinguish prognosis, molecular and immune characteristics, and benefits from immune and chemical therapy. These KRAS-associated genes could be promising targets for drug design.

SUBMITTER: Luo K

PROVIDER: S-EPMC9237412 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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A KRAS-Associated Signature for Prognostic, Immune and Chemical Anti-Cancer Drug-Response Prediction in Colon Cancer.

Luo Kangjia K Song Yanni Y Guan Zilong Z Ou Suwen S Ye Jinhua J Ran Songlin S Wang Hufei H Tao Yangbao Y Gong Zijian Z Ma Tianyi T Jin Yinghu Y Huang Rui R Gao Feng F Yu Shan S

Frontiers in pharmacology 20220614

<b>Background:</b> KRAS mutation, one of the most important biological processes in colorectal cancer, leads to poor prognosis in patients. Although studies on KRAS have concentrated for a long time, there are currently no ideal drugs against KRAS mutations. <b>Methods:</b> Different expression analysis and weighted gene coexpression network analysis was conducted to select candidate genes. Log-rank tests and Cox regression picked out the prognostic genes to build a KRAS-related gene prognostic ...[more]

PMID: 35774610

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Project description:Colon cancer is a kind of common intestinal disease, and early diagnosis of colon cancer is crucial for patient's prognosis. RNA alternative splicing (AS) is an RNA modification that affects cancer occurrence. RNA AS detection is promising to improve the in-depth understanding of the pathological mechanisms in colon cancer. In this study, differential analysis was performed to determine colon cancer-related AS events and the corresponding parental genes. Subsequently, GO functional annotation analysis was carried out on the parental genes, which revealed that these AS events might affect cell adhesion and cell growth. Besides, protein-protein interaction (PPI) network was established with the parental genes, in which MCODE was utilized to identify major functional modules. Enrichment analysis for the major functional module was implemented again, which demonstrated that these genes were mainly concentrated in the ribosome, protein ubiquitination, cell adhesion molecule binding, and other relevant biological functions. Next, differentially expressed genes (DEGs) were screened from colon cancer and normal tissues and overlapped with the parental genes, by which 55 gene expression-associated AS and the corresponding 45 genes were obtained. Moreover, a correlation analysis between splicing factors (SFs) and AS was done to identify interactions. On this basis, an SF-AS network was constructed. The univariate Cox regression analysis was employed to screen prognostic AS signature and establish a risk model. To assess the model, K-M and ROC analyses were done for model assessment, indicating the effective prediction performance. Combined with common clinicopathological features, the multivariate Cox regression analysis was conducted to confirm whether the risk model could be considered as an independent prognostic indicator. Finally, the expression status of the parental genes for the prognostic AS was evaluated between normal and colon cancer cells using qRT-PCR. In summary, TCGA SpliceSeq data were comprehensively analyzed, and a 5-AS prognostic model was constructed for colon cancer.

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Dataset Information

A KRAS-Associated Signature for Prognostic, Immune and Chemical Anti-Cancer Drug-Response Prediction in Colon Cancer.

Publications

A KRAS-Associated Signature for Prognostic, Immune and Chemical Anti-Cancer Drug-Response Prediction in Colon Cancer.

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