Integrative analysis illustrates the role of PCDH7 in lung cancer development, cisplatin resistance, and immunotherapy resistance: an underlying target.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37538171", "kind": "science-record", "revision": 3, "content_hash": "sha256:2bd9fdf31879c998280777cc4774373de00de2754d3a4d4bc8e752d56d65a238", "created_at": "2026-11-01T19:02:19Z", "updated_at": "2026-11-01T21:31:17Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integrative analysis illustrates the role of PCDH7 in lung cancer development, cisplatin resistance, and immunotherapy resistance: an underlying target.", "doi": "10.3389/fphar.2023.1217213", "pmid": "37538171", "authors": ["Huakang Li", "Haoran Xu", "Hong Guo", "Kangming Du", "Diang Chen"], "journal": "Frontiers in pharmacology", "year": 2023, "abstract": "Background: Cisplatin resistance is a common clinical problem in lung cancer. However, the underlying mechanisms have not yet been fully elucidated, highlighting the importance of searching for biological targets. Methods: Bioinformatics analysis is completed through downloaded public data (GSE21656, GSE108214, and TCGA) and specific R packages. The evaluation of cell proliferation ability is completed through CCK8 assay, colony formation, and EdU assay. The evaluation of cell invasion and migration ability is completed through transwell and wound-healing assays. In addition, we evaluated cell cisplatin sensitivity by calculating IC50. Results: Here, we found that PCDH7 may be involved in cisplatin resistance in lung cancer through public database analysis (GSE21656 and GSE108214). Then, a series of in vitro experiments was performed, which verified the cancer-promoting role of PCDH7 in NSCLC. Moreover, the results of IC50 detection showed that PCDH7 might be associated with cisplatin resistance of NSCLC. Next, we investigated the single-cell pattern, biological function, and immune analysis of PCDH7. Importantly, we noticed PCDH7 may regulate epithelial-mesenchymal transition activity, and the local infiltration of CD8+ T and activated NK cells. Furthermore, we noticed that patients with high PCDH7 expression might be more sensitive to bortezomib, docetaxel, and gemcitabine, and resistant to immunotherapy. Finally, a prognosis model based on three PCDH7-derived genes (GPX8, BCAR3, and TNS4) was constructed through a machine learning algorithm, which has good prediction ability on NSCLC patients' survival. Conclusion: Our research has improved the regulatory framework for cisplatin resistance in NSCLC and can provide direction for subsequent related research, especially regarding PCDH7.", "claims": [{"id": "claim-method", "statement": "The machine learning algorithm LASSO regression was utilized to reduce the data dimension (McEligot et al., 2020). The prognosis prediction model was identified using the multivariate Cox regression analysis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37538171", "locator": "sections[4] \"Bioinformatics analysis\" paragraph 2", "quote": "The machine learning algorithm LASSO regression was utilized to reduce the data dimension (McEligot et al., 2020). The prognosis prediction model was identified using the multivariate Cox regression analysis."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/Supplementary Materials; further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37538171", "locator": "sections[22] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/Supplementary Materials; further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A machine-learning prognosis model based on GPX8, BCAR3, and TNS4 showed good predictive ability for NSCLC survival, with training-cohort AUC values of 0.750, 0.745, and 0.688 for 1-, 3-, and 5-year survival.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37538171", "locator": "sections[20] \"Construction of a prognosis model derived from PCDH7 using machine learning algorithms\" paragraph 2", "quote": "The AUC values of 1-, 3-, and 5-year ROC curves were 0.750, 0.745, and 0.688, respectively, indicating a good prediction ability of our prognosis model (Figures 10B–D)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A machine-learning prognosis model based on GPX8, BCAR3, and TNS4 showed good predictive ability for NSCLC survival, with training-cohort AUC values of 0.750, 0.745, and 0.688 for 1-, 3-, and 5-year survival.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:37538171"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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The"], "order_basis": "paper_text_order:522", "id": "assay:1"}, {"role": "data_generation", "definition_id": "cell_culture", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["Methods Cell culture The used BEAS-2B, A549, H1299, H522, H460 cells, and A549-Res cell", "BEAS-2B, A549, H1299, H522, H460 cells, and A549-Res cell lines (A549 cell line that is resis", ", H522, H460 cells, and A549-Res cell lines (A549 cell line that is resistant to cisplatin) we", ") were routinely stored in laboratories. The A549-Res (resistant to cisplatin) cell line was p"], "order_basis": "paper_text_order:1971", "id": "assay:2"}, {"role": "data_generation", "definition_id": "transfection", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.85, "evidence": ["n normal and lung cancer cell lines; (B, C): knockdown efficiency of PCDH7 in A549 and H1299 cells."], "order_basis": "paper_text_order:2772", "id": "assay:3"}, {"role": "data_generation", "definition_id": "ihc", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["bcellular localization of PCDH7. Notes: (A): IHC of PCDH7 in normal lung tissue; (B): IHC of", "A): IHC of PCDH7 in normal lung tissue; (B): IHC of PCDH7 in lung cancer tissue; (C): subcell"], "order_basis": "paper_text_order:2905", "id": "assay:4"}, {"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["cox_ph"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["The machine learning algorithm LASSO regression was utilized to reduce the data dimension (McEligot et al., 2020). The prognosis prediction model was identified using the multivariate Cox regression analysis."], "order_basis": "consumes_earlier_output", "id": "assay:5"}]}