Evidence map›Paper›PMID 36458010›Full record

ArticleFrontiers in immunology2022

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.

Yi Luo, Hao Liu, Hong Fu, Guo-Shan Ding, Fei Teng

Open access · goldAbstract read
In one paragraph

Article in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
2.0field-weighted citation impact, top 12% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 13 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 2 institutions in 1 country.

Yi LuoDepartment of Liver Surgery and Organ Transplantation, Changzheng Hospital, Naval Medical University, Shanghai, China.
Hao LiuDepartment of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai, China.
Hong FuDepartment of Liver Surgery and Organ Transplantation, Changzheng Hospital, Naval Medical University, Shanghai, China.
Guo-Shan DingDepartment of Liver Surgery and Organ Transplantation, Changzheng Hospital, Naval Medical University, Shanghai, China.
Fei TengDepartment of Liver Surgery and Organ Transplantation, Changzheng Hospital, Naval Medical University, Shanghai, China.
Shanghai Changzheng Hospital · CNRuijin Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Cellular 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. Methods: Tumorigenic 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. Results: A 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, Conclusions: Our 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.

Indexed as

Carcinoma, HepatocellularLiver NeoplasmsCarcinogenesisCellular SenescenceGlycosyltransferasesHumansImmunologic FactorsImmunotherapyPhosphatidylinositol 3-KinasesPrognosisRNA-Binding ProteinsTumor MicroenvironmentCPEB3 protein, humanGlycosyltransferasesGTDC1 protein, humanImmunologic FactorsPhosphatidylinositol 3-KinasesRNA-Binding Proteinscellular senescencehepatocellular carcinomaimmunotherapyprognosistumor microenvironment

Identifiers

PMID36458010
PMCPMC9705748
OpenAlexW4309113884

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.