Inhibition of neuroactive ligand–receptor interaction pathway can enhance immunotherapy response in colon cancer: an in silico study

Yang, Yun; Li, Jun; Jing, Chao; Zhai, Yuhao; Bai, Zhigang; Yang, Yingchi; Deng, Wuguo

doi:10.1080/14737140.2023.2245567

Cited by 10 publications

(2 citation statements)

References 42 publications

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“…It encompasses interactions between the surface receptors of nerve cells and neurotransmitters, hormones, or other neuroactive substances(Y. Yang et al, 2023). This signaling pathway is critical in numerous physiological processes, including nerve conduction, neural development, and the regulation of emotions.…”

Section: Discussionmentioning

confidence: 99%

The role of immune inflammatory response and inflammatory infiltrates in major depression disorder and atopic dermatitis: new insights from machine learning

Jiang,

Gong,

Yao

et al. 2023

Preprint

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Background: Major depression disorder (MDD) and atopic dermatitis (AD) are distinct disorders involving immune inflammatory responses. This study aimed to investigate the comorbid relationship between AD and MDD and to identify possible common mechanisms. Methods: We obtained AD and MDD data from the Gene Expression Omnibus (GEO) database. Differential expression analysis and the Genecard database were employed to identify shared genes associated with inflammatory diseases. These shared genes were then subjected to gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Hub genes were selected based on the protein-protein interactions using CytoHubba, and key regulatory genes were identified through enrichment analysis. Subsequently, we conducted immune infiltration and correlation analyses of the shared genes in AD. Finally, we employed three machine learning models to predict the significance of shared genes. Results: A total of 17 shared genes were identified in the AD_Inflammatory_MDD dataset (S100A9, PTGER2, PI3, SNCA, DAB2, PDGFA, FSTL1, CALD1, XK, UTS2, DHRS9, PARD3, NFIB, TMEM158, LIPH, RAB27B, and SH3BRL2). These genes were associated with biological processes such as the regulation of mesenchymal cell proliferation, mesenchymal cell proliferation, and glial cell differentiation. The neuroactive ligand-receptor interaction, IL-17 signaling, and Rap1 signaling pathways were significantly enriched in KEGG analysis. SNCA, S100A9, SH3BGRL2, RAB27B, TMEM158, DAB2, FSTL1, CALD1, and XK were identified as hub genes contributing to comorbid AD and MDD development. The three machine learning models consistently identified SNCA and PARD3 as important biomarkers. Conclusion: SNCA, S100A9, SH3BGRL2, RAB27B, TMEM158, DAB2, FSTL1, CALD1, and XK were identified as significant genes contributing to the development of AD and MDD comorbidities. Immune infiltration analysis showed a notable increase in the infiltration of various subtypes of CD4+ T cells, suggesting a potential association between the development of skin inflammation and the immune response. Across different machine learning models, SNCA and PARD3 consistently emerged as important biomarkers.

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Section: Discussionmentioning

confidence: 99%

The role of immune inflammatory response and inflammatory infiltrates in major depression disorder and atopic dermatitis: new insights from machine learning

Jiang,

Gong,

Yao

et al. 2023

Preprint

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“…The fatty acid metabolism was thought to inhibit LYM pathways, such as CPT1A, a key enzyme in fatty acid b-oxidation, inhibits LYM under pathological conditions (25), which may provide new evidence for the mechanism of LYM in LUAD with low-LYM-index. In addition, cell adhesion molecules and neuroative ligand-receptor interactions were involved in tumor progression (26)(27)(28), as indicated by the fact that CEACAM1, a cell adhesion molecule, was associated with tumor angiogenesis and LYM (29). Therefore, these pathways may reveal the potential mechanism involved in the difference between the high-and low-LYM-index groups.…”

Section: Gsea Analysis Of Lym Indexmentioning

confidence: 99%

Tumor lymphangiogenesis index reveals the immune landscape and immunotherapy response in lung adenocarcinoma

Yang,

Wu,

Cai

et al. 2024

Front. Immunol.

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BackgroundLymphangiogenesis (LYM) has an important role in tumor progression and is strongly associated with tumor metastasis. However, the clinical application of LYM has not progressed as expected. The potential value of LYM needs to be further developed in lung adenocarcinoma (LUAD) patients.MethodsThe Sequencing data and clinical characteristics of LUAD patients were downloaded from The Cancer Genome Atlas and GEO databases. Multiple machine learning algorithms were used to screen feature genes and develop the LYM index. Immune cell infiltration, immune checkpoint expression, Tumor Immune Dysfunction and Exclusion (TIDE) algorithm and drug sensitivity analysis were used to explore the correlation of LYM index with immune profile and anti-tumor therapy.ResultsWe screened four lymphangiogenic feature genes (PECAM1, TIMP1, CXCL5 and PDGFB) to construct LYM index based on multiple machine learning algorithms. We divided LUAD patients into the high LYM index group and the low LYM index group based on the median LYM index. LYM index is a risk factor for the prognosis of LUAD patients. In addition, there was a significant difference in immune profile between high LYM index and low LYM index groups. LUAD patients in the low LYM index group seemed to benefit more from immunotherapy based on the results of TIDE algorithm.ConclusionOverall, we confirmed that the LYM index is a prognostic risk factor and a valuable predictor of immunotherapy response in LUAD patients, which provides new evidence for the potential application of LYM.

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Predicting postoperative prognosis of pancreatic cancer using a computed tomography-based radio-clinical model: exploring biologic functions

Yu,

Ruan,

et al. 2024

Journal of Gastrointestinal Surgery

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Inhibition of neuroactive ligand–receptor interaction pathway can enhance immunotherapy response in colon cancer: an in silico study

Cited by 10 publications

References 42 publications

The role of immune inflammatory response and inflammatory infiltrates in major depression disorder and atopic dermatitis: new insights from machine learning

The role of immune inflammatory response and inflammatory infiltrates in major depression disorder and atopic dermatitis: new insights from machine learning

Tumor lymphangiogenesis index reveals the immune landscape and immunotherapy response in lung adenocarcinoma

Predicting postoperative prognosis of pancreatic cancer using a computed tomography-based radio-clinical model: exploring biologic functions

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