A cellular senescence-related classifier based on a tumorigenesis- and immune infiltration-guided strategy can predict prognosis, immunotherapy response, and candidate drugs in hepatocellular carcinoma

Abstract

BackgroundCellular senescence plays an irreplaceable role in tumorigenesis, progression, and tumor microenvironment (TME) remodeling. However, to date, there is limited research delineating the landscape of cellular senescence in hepatocellular carcinoma (HCC), and an improved understanding on the interaction of tumor-associated cellular senescence with HCC prognosis, TME, and response to immunotherapy is warrant.MethodsTumorigenic and immune infiltration-associated senescence genes were determined by weighted gene co-expression network analysis (WGCNA) and the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm, and subsequently, a prognostic scoring model (named TIS) was constructed using multiple survival analysis algorithms to classify the senescence-related subtypes of HCC. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were conducted to identify the distinct hallmark pathways between high- and low-risk subtypes. Additionally, we carried out correlation analyses for TIS and clinical traits, senescence-associated secretory phenotype (SASP), immune infiltration and evasion, immune checkpoint factors, drug response, and immunotherapeutic efficacy. External experimental validation was conducted to delineate the association of CPEP3 (a TIS gene) with HCC phenotypes through assays of proliferation, colony formation, and invasion.ResultsA five-gene TIS, composed of NET1, ATP6V0B, MMP1, GTDC1, and CPEB3, was constructed and validated using TCGA and ICGC datasets, respectively, and showed a highly robust and plausible signature for overall survival (OS) prediction of HCC in both training and validation cohorts. Patients in the TIS-high group were accompanied by worse OS, activation of carcinogenetic pathways, infiltration of immunosuppressive cells, exclusion of effector killing cells, overexpression of immunomodulatory genes and SASP, and unsatisfied response to immunotherapy. In response to anticancer drugs, patients in the TIS-high group exhibited enhanced susceptibility to several conventional chemotherapeutic agents (5-fluorouracil, docetaxel, doxorubicin, gemcitabine, and etoposide), as well as several inhibitors of pathways involved in cellular senescence (cell-cycle inhibitors, bromodomain and extraterminal domain family (BET) inhibitors, PI3K-AKT pathway inhibitors, and multikinase inhibitors). Additionally, four putative drugs (palbociclib, JAK3 inhibitor VI, floxuridine, and lestaurtinib) were identified as potential compounds for patients in the TIS-high group. Notably, in vitro functional validation showed that CPEB3 knockdown boosted the phenotypes of proliferation, clonogenicity, and invasion in HCC cells, whereas CPEB3 overexpression attenuated these phenotypes.ConclusionsOur study provides comprehensive clues demonstrating the role of novel TIS in predicting HCC prognosis, immunotherapeutic response, and candidate drugs. This work highlights the significance of tumorigenesis- and immune infiltration-related cellular senescence in cancer therapy.

Chemokine CCL21 determines immunotherapy response in hepatocellular carcinoma by affecting neutrophil polarization

“…ROC curve analysis and DCA showed better discrimination and more net benefit of the nomogram compared to those independent predictors. As for other predictive models in HCC, Luo, et al [ 25 ] constructed a 5-gene (NET1, ATP6V0B, MMP1, GTDC1, and CPEB3) risk model through LASSO analyses for predicting immunotherapy response. Feng, et al [ 26 ] identified 17 immune-related gene pairs to explore its predictiveness to ICIs via the LASSO algorithm.…”

A senescence-associated signature refines the classification of different modification patterns and characterization of tumor immune microenvironment infiltration in triple-negative breast cancer

“…Wang et al (Wang et al, 2022) used the senescence-related gene set to define a cellular senescence score in pancancer and demonstrated that this score can represent the degree of immune activation in the tumor microenvironment and can identify groups with better prognosis. In specific cancers, researchers have also constructed classification models and prognostic prediction models based on senescence gene sets, including clear cell renal cell carcinoma (Lu et al, 2022), hepatocellular carcinoma (Luo et al, 2022), glioblastoma (Tan et al, 2022), etc., and explored the relationship between senescencerelated genes and clinical characteristics, tumor microenvironment, immunotherapy, etc. These studies demonstrate the importance of senescence genes in cancer therapy.…”

Prognosis signature for predicting the survival and immunotherapy response in esophageal carcinoma based on cellular senescence-related genes

“…By integrating these and other datasets developers performed a systems biology analysis of cell senescence. At present, there are few studies on CSRGs in ECa, but a number of studies have shown that CSRGs signature can play a prognostic role in hepatocellular carcinoma, gastric cancer, bladder cancer, renal cell carcinoma, and colon cancer (17)(18)(19)(20)(21). However, the expression characteristics and prognostic significance of CSRGs in ECa remain unclear.…”