Evidence map›Paper›PMID 39501306›Full record

ArticleCancer cell international2024

Systematic multiomics analysis and in vitro experiments suggest that ITGA5 could serve as a promising therapeutic target for ccRCC.

Xiangxian Che, Xi Tian, Zhenda Wang, Shuxuan Zhu, Shiqi Ye, Yue Wang, Yihan Chen, Yiyun Huang, Aihetaimujiang Anwaier, Peifeng Yao and 6 more

Abstract read
In one paragraph

Article in Cancer cell international, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

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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

16 authors.

Xiangxian Che *Department of Urology, Fudan University Shanghai Cancer Center, No. 270 Dong'an Road, Shanghai, 200032, People's Republic of China.
Xi Tian *Department of Urology, Fudan University Shanghai Cancer Center, No. 270 Dong'an Road, Shanghai, 200032, People's Republic of China.
Zhenda Wang *Department of Urology, Fudan University Shanghai Cancer Center, No. 270 Dong'an Road, Shanghai, 200032, People's Republic of China.
Shuxuan ZhuDepartment of Urology, Fudan University Shanghai Cancer Center, No. 270 Dong'an Road, Shanghai, 200032, People's Republic of China.
Shiqi YeDepartment of Urology, Fudan University Shanghai Cancer Center, No. 270 Dong'an Road, Shanghai, 200032, People's Republic of China.
Yue WangDepartment of Urology, Fudan University Shanghai Cancer Center, No. 270 Dong'an Road, Shanghai, 200032, People's Republic of China.
Yihan ChenDepartment of Oncology, Fudan University Shanghai Medical College, Shanghai, 200032, People's Republic of China.
Yiyun HuangDepartment of Oncology, Fudan University Shanghai Medical College, Shanghai, 200032, People's Republic of China.
Aihetaimujiang AnwaierDepartment of Urology, Fudan University Shanghai Cancer Center, No. 270 Dong'an Road, Shanghai, 200032, People's Republic of China.
Peifeng YaoSchool of Informatics, Xiamen University, Xiamen, 361102, People's Republic of China.
Yijia ChenDepartment of Oncology, Fudan University Shanghai Medical College, Shanghai, 200032, People's Republic of China.
Keting WuDepartment of Oncology, Fudan University Shanghai Medical College, Shanghai, 200032, People's Republic of China.
Yifei LiuDepartment of Oncology, Fudan University Shanghai Medical College, Shanghai, 200032, People's Republic of China.
Wenhao XuDepartment of Urology, Fudan University Shanghai Cancer Center, No. 270 Dong'an Road, Shanghai, 200032, People's Republic of China. xwhao0407@163.com.
Hailiang ZhangDepartment of Urology, Fudan University Shanghai Cancer Center, No. 270 Dong'an Road, Shanghai, 200032, People's Republic of China. zhanghl918@163.com.
Dingwei YeDepartment of Urology, Fudan University Shanghai Cancer Center, No. 270 Dong'an Road, Shanghai, 200032, People's Republic of China. dwyelie@163.com.

Funding

Beijing Xisike Clinical Oncology Research Foundation Y-HR2020MS-0948FDUROP (Fudan Undergraduate Research Opportunities Program) 22401Shanghai Anti-Cancer Association SACA-CY21A06,SACA-CY21B01,SACA-CY23A02,SACA-CY23C04Shanghai Municipal Health Bureau 2020CXJQ03Shanghai Undergraduate Training Program on Innovation and Entrepreneurship (SUTPLE) grant S202310246246
6 · The paper itself

Abstract

backgroundIntegrin alpha 5 (ITGA5) was previously confirmed to be related to prognosis in several cancer types; however, its function in clear cell renal cell carcinoma (ccRCC) and how this molecule regulates tumor progression and the tumor microenvironment (TME) remain to be elucidated.

methodsWe investigated the prognostic implications of ITGA5 with a machine learning model and evaluated biological behaviors of different levels of ITGA5 expression in vitro. Bioinformatic analysis was performed to explain the comprehensive effect of ITGA5 on the TME and drug sensitivity.

resultsWe constructed a machine learning model to elaborate the prognostic implication of ITGA5. As tumorigenesis of ccRCC was tightly relevant with several mutant genes, we investigated the correlation between ITGA5 expression and frequent mutations and found ITGA5 upregulation in VHL mutant ccRCC (P = 0.016). Through overexpressing, silencing, and blocking ITGA5, we verified the role of ITGA5 in promoting ccRCC adverse biological activities; and the potential functions of ITGA5 in ccRCC were bioinformatically demonstrated, summarizing as cell proliferation, migration, and angiogenesis. The localization of ITGA5 primarily in endothelia and macrophages further verified its magnitude in angiogenesis and aroused our excavation in ITGA5 regulation of immune infiltration landscape. Generally, ITGA5-high ccRCC presented an immunosuppressive TME by inducing a lower level of CD8 + T cell infiltration. For the last part we predicted drug sensitivity relevant to ITGA5 and concluded that a joint medication of ITGA5 inhibitors and VEGFR-target drugs (including sunitinib, axitinib, pazopanib, and motesanib) might be a promising therapeutic strategy.

conclusionOur findings clarified the adverse outcome induced by high expression of ITGA5 in ccRCC patients. In vitro experiments and bioinformatical analysis identified ITGA5 function as predominantly cell proliferation, migration, angiogenesis, and macrophage recruitment. Further, we predicted immune infiltration and medication sensitivity regulation by ITGA5 and proposed a joint use of ITGA5 inhibitors and anti-angiogenetic drugs as a potential potent therapeutic strategy.

Indexed as

BiomarkerClear cell renal cell carcinomaITGA5Machine learningTumor microenvironment

Identifiers

PMID39501306
PMCPMC11539770

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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.