Evidence map›Paper›PMID 39185649›Full record

ArticleCurrent pharmaceutical design2024

Assessing TGF-β Prognostic Model Predictions for Chemotherapy Response and Oncogenic Role of FKBP1A in Liver Cancer.

Weimei Chen, Qinghe Que, Rongrong Zhong, Zhou Lin, Qiaolan Yi, Qingshui Wang

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Article in Current pharmaceutical design, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Weimei ChenDepartment of Blood Transfusion, Longyan First Affiliated Hospital of Fujian Medical University, Longyan City, Fujian Province, 364000, China.
Qinghe QueDepartment of Blood Transfusion, Longyan First Affiliated Hospital of Fujian Medical University, Longyan City, Fujian Province, 364000, China.
Rongrong ZhongDepartment of Emergency, Longyan First Affiliated Hospital of Fujian Medical University, Longyan City, Fujian Province, 364000, China.
Zhou LinDepartment of Burn Plastic Surgery and Wound Repair Surgery, Longyan First Affiliated Hospital of Fujian Medical University, Longyan City, Fujian Province, 364000, China.
Qiaolan YiDepartment of Clinical Laboratory, Longyan First Affiliated Hospital of Fujian Medical University, Longyan City, Fujian Province, 364000, China.
Qingshui WangSecond Affiliated Hospital of Fujian University of Traditional Chinese Medical University Medicine, Fujian-Macao Science and Technology Cooperation Base of Traditional Chinese Medicine-Oriented Chronic Disease Prevention and Treatment, Innovation and Transformation Center, Fujian University of Traditional Chinese Medicine, Fuzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe Transforming Growth Factor-Beta (TGF-β) signaling pathway plays a crucial role in the pathogenesis of diseases. This study aimed to identify differentially expressed TGF-β-related genes in liver cancer patients and to correlate these findings with clinical features and immune signatures.

methodsThe TCGA-STAD and LIRI-JP cohorts were utilized for a comprehensive analysis of TGF-β- related genes. Differential gene expression, functional enrichment, survival analysis, and machine learning techniques were employed to develop a prognostic model based on a TGF-β-related gene signature (TGFBRS).

resultsWe developed a prognostic model for liver cancer based on the expression levels of nine TGF-β- related genes. The model indicates that higher TGFBRS values are associated with poorer prognosis, higher tumor grades, more advanced pathological stages, and resistance to chemotherapy. Additionally, the TGFBRS-High subtype was characterized by elevated levels of immune-suppressive cells and increased expression of immune checkpoint molecules. Using a Gradient Boosting Decision Tree (GBDT) machine learning approach, the FKBP1A gene was identified as playing a significant role in liver cancer. Notably, knocking down FKBP1A significantly inhibited the proliferation and metastatic capabilities of liver cancer cells both in vitro and in vivo.

conclusionOur study highlights the potential of TGFBRS in predicting chemotherapy responses and in shaping the tumor immune microenvironment in liver cancer. The results identify FKBP1A as a promising molecular target for developing preventive and therapeutic strategies against liver cancer. Our findings could potentially guide personalized treatment strategies to improve the prognosis of liver cancer patients.

Indexed as

Liver NeoplasmsTacrolimus Binding ProteinsTransforming Growth Factor betaAnimalsAntineoplastic AgentsCell ProliferationHumansMicePrognosisAntineoplastic AgentsFKBP1A protein, humanTacrolimus Binding ProteinsTransforming Growth Factor betachemotherapyFKBP1Aimmune microenvironmentliver cancersignaling pathway.TGF-β

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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.