Evidence map›Paper›PMID 41842707›Full record

ArticleThe clinical respiratory journal2026

Therapeutic Targets for Lung Squamous Cell Carcinoma: Proteome-Wide Mendelian Randomization and Potential Drug Prediction.

Tao Xiang, Tingting Hu, Jiantong Sun, Qikun Geng, Jiazun Yang, Zhongyu Jian

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Article in The clinical respiratory journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Tao XiangWest China Hospital, Sichuan University, Chengdu, China.
Tingting HuWest China Hospital, Sichuan University, Chengdu, China.
Jiantong SunInstitute of Thoracic Oncology and Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, China.ORCID https://orcid.org/0009-0000-4868-0330
Qikun GengWest China Hospital, Sichuan University, Chengdu, China.
Jiazun YangWest China Hospital, Sichuan University, Chengdu, China.
Zhongyu JianDepartment of Urology, Institute of Urology (Laboratory of Reconstructive Urology), West China Hospital, Sichuan University, Chengdu, China.ORCID https://orcid.org/0000-0002-8532-9336

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLung squamous cell carcinoma (LUSC) is one of the most prevalent subtypes of lung cancer, accounting for approximately 25%. Mendelian randomization (MR) analysis of plasma proteins can identify potential drug targets for LUSC.

methodsWe obtained genetic instruments for plasma proteins from a GWAS by Sun BB et al. conducted on 3301 healthy adults. GWAS summary statistics of LUSC were from ILCCO. We conducted a two-sample MR analysis to investigate the causal relationships between plasma proteins and the risk to LUSC. Colocalization analysis was used to test whether two traits share the same causal variables. Survival analysis based on potential proteins was conducted using transcriptomic data from TCGA database to determine whether gene expression levels are associated with the prognosis of LUSC patients. Moreover, predictions of potential drug ligands and molecular dockings were performed to evaluate the pharmaceutical properties of the target genes.

resultsProteome-wide MR analysis identified three proteins (MICB, SPINK2, TXNDC11) that showed a strong association with decreased risk of LUSC after FDR correction. Increased levels of MICB [OR(95% CI) = 0.72(0.63, 0.83); p = 3.90E-06], SPINK2 [OR (95% CI) = 0.74 (0.66, 0.84); p = 1.25E-06], TXNDC11 [OR (95% CI) = 0.63(0.51, 0.78); p = 2.69E-05] per SD decreased the risk of LUSC specifically. Sensitivity analysis revealed that there was no significant heterogeneity, pleiotropy, or reverse causality between these proteins and LUSC. Colocalization analysis indicated that three proteins shared the same variants with LUSC. Survival analysis showed that upregulation of SPINK2 was associated with a favorable prognosis in LUSC patients, while MICB and TXNDC11 were not. The expression of MICB, SPINK2, TXNDC11 was found to be potentially affected by specific substances, and our molecular docking showed a stable binding between SPINK2 and danazol, suggesting its potential as a drug target.

conclusionThis study identified SPINK2 causally associated with the risk and prognosis of LUSC, which is a promising target for LUSC. The drug prediction we performed illustrated the medicinal potential of SPINK2, and the high binding activity of molecular docking indicated its strong potential as a drug target.

Indexed as

Carcinoma, Squamous CellLung NeoplasmsMendelian Randomization AnalysisProteomeGenome-Wide Association StudyHumansPrognosisTrypsin Inhibitor, Kazal PancreaticProteomeTrypsin Inhibitor, Kazal Pancreaticlung squamous carcinomaMendelian randomizationplasma proteinsurvival analysis

Identifiers

PMID41842707
PMCPMC13093309

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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.