ArticleAging2023
Construction of a T-cell exhaustion-related gene signature for predicting prognosis and immune response in hepatocellular carcinoma.
Article in Aging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 12 citations in OpenAlex.
- Analysis of PD1, LAG3, TIGIT, and TIM3 expression in human lung adenocarcinoma reveals a 25-gene signature predicting immunotherapy response.Cell reports. Medicine · 2024Trial
- Single-cell epigenetic landscape, microenvironment interactions, and gene regulatory modules of non-functioning pituitary adenomas.Cell systems · 2026Article
- Identification of heterogeneity and common characteristics in colorectal carcinoma located in distinct sites.Scientific reports · 2025Article
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- Construction and Validation of T-cell Exhaustion-related Gene Signature for Predicting Prognosis in Diffuse Large B-cell Lymphoma.Current pharmaceutical biotechnology · 2025Article
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- Ferritin light chain as a potential biomarker for the prognosis of liver hepatocellular carcinoma.Heliyon · 2024Article
- Identification and Validation of T-Cell Exhaustion Signature for Predicting Prognosis and Immune Response in Pancreatic Cancer by Integrated Analysis of Single-Cell and Bulk RNA Sequencing Data.Diagnostics (Basel, Switzerland) · 2024Article
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- The Characteristics of Transcription Factors Regulating T Cell Exhaustion Were Analyzed to Predict the Prognosis and Therapeutic Effect in Patients with HCC.International journal of general medicine · 2023Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHepatocellular carcinoma (HCC) is a heterogeneous malignancy with a rising prevalence worldwide. Immunotherapy has been shown to improve treatment outcomes for HCC. We aimed to construct a T-cell exhaustion-related gene prognostic model (TEXPM) for HCC and to elucidate the immunologic characteristics and advantages of immunotherapy in T-cell exhaustion-Related Gene-defined HCC groups.
methodsSingle-cell RNA sequencing data were used in conjunction with TCGA Differentially expressed genes (DEGs) to screen for T-cell exhaustion-Related Genes (TEXGs) for subsequent evaluation. Using univariate Cox regression analysis and LASSO regression analysis, five genes (FTL, GZMA, CD14, NPC2, and IER3) were subsequently selected for the construction of a TEXPM. Then, we evaluated the immunologic characteristics and advantages of immunotherapy in groups identified by TEXPM.
resultsThe TEXPM was formed with FTL, GZMA, CD14, NPC2, and IER3. The results of the training and validation team studies were consistent, with the low TEXPM group surviving longer than the high TEXPM group (P < 0.001). Multivariate Cox regression analysis demonstrated that TEXPM (HR: 2.347, 95%CI: 1.844-2.987; HR: 2.172, 95% CI: 1.689-2.793) was an independent prognostic variable for HCC patients. The low-TEXPM group was linked to active immunity, less aggressive phenotypes, strong infiltration of CD8+ T cells, CD4 + T cells, and M1 macrophages, and a better response to ICI treatment. A high TEXPM group, on the other hand, was associated with suppressive immunity, more aggressive phenotypes, a significant infiltration of B cells, M0 macrophages, and M2 macrophages, and a reduced response to ICI treatment. FTL is an independent prognostic variable in HCC patients and the knockdown of FTL can affect the biological behavior of hepatocellular carcinoma cells.
conclusionsTEXPM is a promising prognostic biomarker connected to the immune system. Differentiating immunological and molecular features and predicting patient outcomes may be facilitated by TEXPM grouping. Furthermore, the expression of FTL was found to be an independent prognostic factor for HCC. Knockdown of FTL significantly inhibited proliferation, migration, and invasive activity in liver cancer cells.
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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 Socratic graph.