A novel prognostic and therapeutic target biomarker based on complement-related gene signature in gastric cancer.

Gastric cancer (GC) complement overall survival analysis (OS analysis) prognosis treatment

Journal

Translational cancer research
ISSN: 2219-6803
Titre abrégé: Transl Cancer Res
Pays: China
ID NLM: 101585958

Informations de publication

Date de publication:
31 Dec 2023
Historique:
received: 10 04 2023
accepted: 18 10 2023
medline: 9 1 2024
pubmed: 9 1 2024
entrez: 9 1 2024
Statut: ppublish

Résumé

Gastric cancer (GC) is one of the most prevalent cancer types that reduce human life expectancy. The current tumor-node-metastasis (TNM) staging system is inadequate in identifying higher or lower risk of GC patients because of tumor heterogeneity. Research shows that complement plays a dual role in the tumor development and progression of GC. We downloaded GC data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). A complement-related risk signature was constructed through bioinformatics analysis. Subsequently, the predictive ability of this signature was validated with GSE84437 dataset, and a nomogram integrating risk score and common clinical factors was established. Besides, we evaluated the association of risk score with the immune and stromal cell infiltration in TCGA. Furthermore, immunotherapy response prediction and drug susceptibility analysis were conducted to access the ability of the risk signature in predicting the therapeutic effect. A complement-related gene (CRG) signature, based on six genes ( The novel CRG signature may act as a reliable, efficient tool for prognostic prediction and treatment guidance in future clinical practice.

Sections du résumé

Background UNASSIGNED
Gastric cancer (GC) is one of the most prevalent cancer types that reduce human life expectancy. The current tumor-node-metastasis (TNM) staging system is inadequate in identifying higher or lower risk of GC patients because of tumor heterogeneity. Research shows that complement plays a dual role in the tumor development and progression of GC.
Methods UNASSIGNED
We downloaded GC data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). A complement-related risk signature was constructed through bioinformatics analysis. Subsequently, the predictive ability of this signature was validated with GSE84437 dataset, and a nomogram integrating risk score and common clinical factors was established. Besides, we evaluated the association of risk score with the immune and stromal cell infiltration in TCGA. Furthermore, immunotherapy response prediction and drug susceptibility analysis were conducted to access the ability of the risk signature in predicting the therapeutic effect.
Results UNASSIGNED
A complement-related gene (CRG) signature, based on six genes (
Conclusions UNASSIGNED
The novel CRG signature may act as a reliable, efficient tool for prognostic prediction and treatment guidance in future clinical practice.

Identifiants

pubmed: 38192986
doi: 10.21037/tcr-23-628
pii: tcr-12-12-3565
pmc: PMC10774048
doi:

Types de publication

Journal Article

Langues

eng

Pagination

3565-3580

Informations de copyright

2023 Translational Cancer Research. All rights reserved.

Déclaration de conflit d'intérêts

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-23-628/coif). The authors have no conflicts of interest to declare.

Auteurs

Zuming Liu (Z)

Digestive Department, Sinopharm Dongfeng General Hospital, Hubei University of Medicine, Shiyan, China.

Mingwei Yang (M)

Department of Radiation Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.

Hang Shu (H)

Department of Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.

Jianmei Zhou (J)

Digestive Department, Sinopharm Dongfeng General Hospital, Hubei University of Medicine, Shiyan, China.
Department of Infectious Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, China.

Classifications MeSH