Evidence map›Paper›PMID 37969374›Full record

ArticleTranslational cancer research2023

Analysis of IGFBP7 expression characteristics in pan-cancer and its clinical relevance to stomach adenocarcinoma.

Hui-Wen Xu, Mei-Qian Wang, Sen-Lin Zhu

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Article in Translational cancer research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.3field-weighted citation impact, top 17% of its field
1 · What the graph read from it

What it found

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3 · Its place in the literature

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4 citing papers in PubMed, 5 citations in OpenAlex.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Hui-Wen Xu *Department of Gastroenterology and Hepatology, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
Mei-Qian Wang *Department of Gastroenterology and Hepatology, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
Sen-Lin ZhuDepartment of Gastroenterology and Hepatology, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
The First Affiliated Hospital, Sun Yat-sen University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Insulin-like growth factor (IGF) binding proteins (IGFBPs) are involved in tumorigenesis and cancer progression. IGFBP7 has been shown to act as either a tumor suppressive gene or an oncogene in many tumors, including stomach adenocarcinoma (STAD). To provide a more systematic and comprehensive understanding of IGFBP7 gene, we performed an integrative pan-cancer analysis and explored further with the case of STAD. Methods: We compared the expression data of IGFBP7 in various cancer and normal tissues obtained from The Cancer Genome Atlas (TCGA) database and the Genotype-Tissue Expression (GTEx) database. The TISIDB web portal was used to analyze the associations of IGFBP7 with cancer molecular subtypes and immune subtypes. We also analyzed the predictive ability and prognostic values of IGFBP7 in pan-cancer, as well as explored its targeted binding proteins and their biological functions. Additionally, we examined the relationship between IGFBP7 and the clinical characteristics of STAD, investigated the co-expression genes and biological functions of differentially expressed genes (DEGs), and validated the mRNA and protein expression levels of IGFBP7 using gastric cancer (GC) and adjacent normal tissues in a small self-case-control study. Results: IGFBP7 was found to be overexpressed in STAD and downregulated in many other cancers. The mRNA and protein expression levels of IGFBP7 were also significantly higher in the collected GC tissues compared with adjacent tissues. Expression of IGFBP7 varied significantly across molecular subtypes of nine different cancer types and immune subtypes of eight types, with the highest expression observed in the genomically stable molecular subtype and C3 inflammatory immune subtype in STAD. IGFBP7 demonstrated an area under the curve (AUC) >0.7 for predicting 16 cancer types, and an AUC >0.9 for seven types. Patients in the higher IGFBP7 expression group showed a poorer prognosis for adrenal cortical carcinoma (ACC) and low-grade glioma (LGG), while demonstrating a more favorable prognosis for kidney renal clear cell carcinoma (KIRC). IGFBP7 expression in STAD was significantly associated with T stage, pathological stage, histologic grade, and Conclusions: IGFBP7 showed promise as a biomarker for prediction and prognosis in pan-cancer. IGFBP7 was found to be overexpressed in STAD, and its expression was closely associated with the clinical characteristics of STAD.

Indexed as

IGFBP7pan-cancer analysisstomach adenocarcinoma (STAD)

Identifiers

PMID37969374
PMCPMC10643967
OpenAlexW4388195381

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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.