A Cancer Associated Fibroblasts-Related Six-Gene Panel for Anti-PD-1 Therapy in Melanoma Driven by Weighted Correlation Network Analysis and Supervised Machine Learning.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35479936", "kind": "science-record", "revision": 4, "content_hash": "sha256:148dfff30cfb555cc0bc67f6ab39927d947e73eedaee6935b35e8cc24b6e6aea", "created_at": "2026-11-01T18:14:21Z", "updated_at": "2026-11-01T22:06:24Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A Cancer Associated Fibroblasts-Related Six-Gene Panel for Anti-PD-1 Therapy in Melanoma Driven by Weighted Correlation Network Analysis and Supervised Machine Learning.", "doi": "10.3389/fmed.2022.880326", "pmid": "35479936", "authors": ["Luyao Tian", "Fei Long", "Youjin Hao", "Bo Li", "Yinghong Li", "Ying Tang", "Jing Li", "Qi Zhao", "Juan Chen", "Mingwei Liu"], "journal": "Frontiers in medicine", "year": 2022, "abstract": "Background: Melanoma is a highly aggressive skin cancer with a poor prognosis and mortality. Immune checkpoint blockade (ICB) therapy (e.g., anti-PD-1 therapy) has opened a new horizon in melanoma treatment, but some patients present a non-responsive state. Cancer-associated fibroblasts (CAFs) make up the majority of stromal cells in the tumor microenvironment (TME) and have an important impact on the response to immunotherapy. There is still a lack of identification of CAFs-related predictors for anti-PD-1 therapy, although the establishment of immunotherapy biomarkers is well underway. This study aims to explore the potential CAFs-related gene panel for predicting the response to anti-PD-1 therapy in melanoma patients and elucidating their potential effect on TME.\nMethods: Three gene expression datasets from melanoma patients without anti-PD-1 treatment, in a total of 87 samples, were downloaded from Gene Expression Omnibus (GEO) as the discovery sets (GSE91061) and validation sets (GSE78220 and GSE122220). The CAFs-related module genes were identified from the discovery sets by weighted gene co-expression network analysis (WGCNA). Concurrently, we utilized differential gene analysis on the discovery set to obtain differentially expressed genes (DEGs). Then, CAFs-related key genes were screened with the intersection of CAFs-related module genes and DEGs, succeeded by supervised machine learning-based identification. As a consequence of expression analysis, gene set enrichment analysis, survival analysis, staging analysis, TME analysis, and correlation analysis, the multidimensional systematic characterizations of the key genes were uncovered. The diagnostic performance of the CAFs-related gene panel was assessed by receiver operating characteristic (ROC) curves in the validation sets. Eventually, the CAFs-related gene panel was verified by the expression from the single-cell analysis.\nResults: The six-gene panel associated with CAFs were finally identified for predicting the response to anti-PD-1 therapy, including CDK14, SYNPO2, TCF4, GJA1, CPXM1, and TFPI. The multigene panel demonstrated excellent combined diagnostic performance with the area under the curve of ROC reaching 90.5 and 75.4% ~100% in the discovery and validation sets, respectively.\nConclusion: Confirmed by clinical treatment outcomes, the identified CAFs-related genes can be used as a promising biomarker panel for prediction to anti-PD-1 therapy response, which may serve as new immunotherapeutic targets to improve survival outcomes of melanoma patients.", "claims": [{"id": "claim-method", "statement": "The intersection between module genes associated with CAFs and DEGs related to anti-PD-1 therapy response were extracted and defined as CAFs-related DEGs (CDEGs), and further LASSO and random forest analysis were adopted to identify key CDEGs (KCDEGs) using the glmnet package (nlambda = 1000, 10-fold cross-validation) and randomForest package.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35479936", "locator": "sections[7] \"Identification of Key Genes by LASSO Regression Analysis and Random Forest Algorithm\" paragraph 2", "quote": "The intersection between module genes associated with CAFs and DEGs related to anti-PD-1 therapy response were extracted and defined as CAFs-related DEGs (CDEGs), and further LASSO and random forest analysis were adopted to identify key CDEGs (KCDEGs) using the glmnet package (nlambda = 1000, 10-fold cross-validation) and randomForest package."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35479936", "locator": "sections[27] \"Data Availability Statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A six-gene CAFs-related panel comprising CDK14, SYNPO2, TCF4, GJA1, CPXM1, and TFPI was identified to predict anti-PD-1 response in melanoma, with combined AUCs of 90.5% in the discovery set and 75.4%–100% in the validation sets.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35479936", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The six-gene panel associated with CAFs were finally identified for predicting the response to anti-PD-1 therapy, including CDK14, SYNPO2, TCF4, GJA1, CPXM1, and TFPI."}, {"asset": "source:pmid:35479936", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The multigene panel demonstrated excellent combined diagnostic performance with the area under the curve of ROC reaching 90.5 and 75.4% ~100% in the discovery and validation sets, respectively."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "A six-gene CAFs-related panel comprising CDK14, SYNPO2, TCF4, GJA1, CPXM1, and TFPI was identified to predict anti-PD-1 response in melanoma, with combined AUCs of 90.5% in the discovery set and 75.4%–100% in the validation sets.", "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:35479936"], "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. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE122220"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "blocked", "blocked_on": "method:machine_learning_random_forest", "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE122220"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:machine_learning_random_forest"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35479936", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fmed.2022.880326", "sha256": "sha256:10ae75a41feb4ba95bacecd34d33cf04ec4f4fa1accffff10c9b3715dd669e64", "bytes": 70324, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fmed.2022.880326", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9035939", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE122220", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE122220", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE122220", "version": null, "note": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM3461245", "GSM3461246", "GSM3461247", "GSM3461248", "GSM3461249", "GSM3461250", "GSM3461251", "GSM3461252", "GSM3461253", "GSM3461254"], "sample_count": 10, "group_sizes": null, "group_sizes_basis": null, "platform": "10558", "taxon": "Homo sapiens", "title": "Gene expression profiles of melanoma tumor samples from patients treated with checkpoint inhibitors (human)", "resolution_checked_at": "2026-11-01T18:06:43Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE78220", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE78220", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE78220", "version": null, "note": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM2069823", "GSM2069824", "GSM2069825", "GSM2069826", "GSM2069827", "GSM2069828", "GSM2069829", "GSM2069830", "GSM2069831", "GSM2069832", "GSM2069833", "GSM2069834", "GSM2069835", "GSM2069836", "GSM2069837", "GSM2069838", "GSM2069839", "GSM2069840", "GSM2069841", "GSM2069842", "GSM2069843", "GSM2069844", "GSM2069845", "GSM2069846", "GSM2069847", "GSM2069848", "GSM2069849", "GSM2069850"], "sample_count": 28, "group_sizes": null, "group_sizes_basis": null, "platform": "11154", "taxon": "Homo sapiens", "title": "mRNA expressions in pre-treatment melanomas undergoing anti-PD-1 checkpoint inhibition therapy", "resolution_checked_at": "2026-11-01T15:39:55Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE91061", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE91061", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE91061", "version": null, "note": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM2420259", "GSM2420260", "GSM2420261", "GSM2420262", "GSM2420263", "GSM2420264", "GSM2420265", "GSM2420266", "GSM2420267", "GSM2420268", "GSM2420269", "GSM2420270", "GSM2420271", "GSM2420272", "GSM2420273", "GSM2420274", "GSM2420275", "GSM2420276", "GSM2420277", "GSM2420278", "GSM2420279", "GSM2420280", "GSM2420281", "GSM2420282", "GSM2420283", "GSM2420284", "GSM2420285", "GSM2420286", "GSM2420287", "GSM2420288", "GSM2420289", "GSM2420290", "GSM2420291", "GSM2420292", "GSM2420293", "GSM2420294", "GSM2420295", "GSM2420296", "GSM2420297", "GSM2420298", "GSM2420299", "GSM2420300", "GSM2420301", "GSM2420302", "GSM2420303", "GSM2420304", "GSM2420305", "GSM2420306", "GSM2420307", "GSM2420308", "GSM2420310", "GSM2420311", "GSM2420312", "GSM2420313", "GSM2420316", "GSM2420317", "GSM2420318", "GSM2420319", "GSM2420320", "GSM2420323", "GSM2420324", "GSM2420325", "GSM2420326", "GSM2420328", "GSM2420329", "GSM2420331", "GSM2420332", "GSM2420333", "GSM2420334", "GSM2420335", "GSM2420336", "GSM2420337", "GSM2420338", "GSM2420339", "GSM2420340", "GSM2420341", "GSM2420342", "GSM2420343", "GSM2420344", "GSM2420345", "GSM2420346", "GSM2420349", "GSM2420350", "GSM2420351", "GSM2420352", "GSM2420353", "GSM2420354", "GSM2420355", "GSM2420356", "GSM2420357", "GSM2420358", "GSM2420359", "GSM2420360", "GSM2420361", "GSM2420362", "GSM2420363", "GSM2420364", "GSM2420365", "GSM2420366", "GSM2420367", "GSM2420368", "GSM2420369", "GSM2420370", "GSM2420371", "GSM2420372", "GSM2420373", "GSM2420374", "GSM2420375", "GSM2420376"], "sample_count": 109, "group_sizes": null, "group_sizes_basis": null, "platform": "9052", "taxon": "Homo sapiens", "title": "Molecular portraits of tumor mutational and micro-environmental sculpting by immune checkpoint blockade therapy", "resolution_checked_at": "2026-11-01T15:39:55Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE122220 not downloaded", "asset:data:geo:GSE122220 licence not read", "asset:data:geo:GSE78220 not downloaded", "asset:data:geo:GSE78220 licence not read", "asset:data:geo:GSE91061 not downloaded", "asset:data:geo:GSE91061 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "machine_learning", "specific": "random_forest", "label": "Machine learning", "dsl_operations": [], "missing_operations": ["machine_learning_random_forest"], "expressible": false, "parameters": {}, "data_shapes": ["labelled_feature_matrix"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "The intersection between module genes associated with CAFs and DEGs related to anti-PD-1 therapy response were extracted and defined as CAFs-related DEGs (CDEGs), and further LASSO and random forest analysis were adopted to identify key CDEGs (KCDEGs) using the glmnet package (nlambda = 1000, 10-fold cross-validation) and randomForest package."}}, {"id": "method-2", "family": "group_comparison", "specific": "student_t_test", "label": "Group comparison", "dsl_operations": [], "missing_operations": ["student_t"], "expressible": false, "parameters": {}, "data_shapes": ["two_or_more_groups_continuous"], "evidence": {"score": 0.8, "band": "high", "quote": "Student's t-test"}}, {"id": "method-3", "family": "group_comparison", "specific": "wilcoxon", "label": "Group comparison", "dsl_operations": [], "missing_operations": ["group_comparison_wilcoxon"], "expressible": false, "parameters": {}, "data_shapes": ["two_or_more_groups_continuous"], "evidence": {"score": 0.8, "band": "high", "quote": "Wilcoxon"}}, {"id": "method-4", "family": "group_comparison", "specific": "anova", "label": "Group comparison", "dsl_operations": ["group_summary", "welch_t", "bootstrap_mean_difference", "group_comparison_anova"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["two_or_more_groups_continuous"], "evidence": {"score": 0.8, "band": "high", "quote": "ANOVA"}}, {"id": "method-5", "family": "survival_analysis", "specific": "kaplan_meier", "label": "Survival analysis", "dsl_operations": ["survival_km"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.8, "band": "high", "quote": "Kaplan-Meier"}}, {"id": "method-6", "family": "survival_analysis", "specific": "log_rank_test", "label": "Survival analysis", "dsl_operations": ["logrank_test"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.8, "band": "high", "quote": "Log-rank"}}, {"id": "method-7", "family": "correlation_analysis", "specific": "spearman", "label": "Correlation analysis", "dsl_operations": ["spearman_rho"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["paired_numeric_columns"], "evidence": {"score": 0.8, "band": "high", "quote": "Spearman correlation"}}, {"id": "method-8", "family": "clustering", "specific": "hierarchical", "label": "Clustering", "dsl_operations": ["hierarchical_cluster"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["feature_matrix"], "evidence": {"score": 0.8, "band": "high", "quote": "hierarchical clustering"}}], "targets": [], "gaps": ["method.reader_differs_from_extractor", "method.machine_learning_random_forest"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": false, "required_operations": [], "missing_operations": ["machine_learning_random_forest"], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: random_forest."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE91061", "GSE78220", "GSE122220"], "resolved_accessions": ["GSE91061", "GSE78220", "GSE122220"], "statements": ["The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material."], "basis": "The paper names GSE91061, GSE78220, GSE122220 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": [], "assets": ["source:pmid:35479936", "data:geo:GSE122220"], "steps": []}, "must_build": {"dsl_operations": [{"operation": "machine_learning_random_forest", "unlocks_papers": null, "priority": null}], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE122220"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T17:43:53Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 20085, "completion_tokens": 2403, "cached_tokens": 188}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:35479936", "type": "paper", "label": "pmid:35479936"}, {"id": "source:pmid:35479936", "type": "manuscript", "label": "PMID 35479936 full text"}, {"id": "data:geo:GSE122220", "type": "dataset", "label": "GEO GSE122220"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:35479936", "type": "paper", "label": "pmid:35479936"}, {"id": "source:pmid:35479936", "type": "manuscript", "label": "PMID 35479936 full text"}, {"id": "data:geo:GSE122220", "type": "dataset", "label": "GEO GSE122220"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-35479936", "type": "extraction", "used": ["source:pmid:35479936"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE122220", "type": "accession_resolution", "used": ["data:geo:GSE122220"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:35479936", "parent_hash": "sha256:59d90b0b933ec34ea94e757f176c7cae4d4839fd0336f52f0be70dafeb661961", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:GSE122220 licence not read", "asset:data:geo:GSE122220 not downloaded", "asset:data:geo:GSE78220 licence not read", "asset:data:geo:GSE78220 not downloaded", "asset:data:geo:GSE91061 licence not read", "asset:data:geo:GSE91061 not downloaded", "execution.not_run", "method.machine_learning_random_forest", "method.reader_differs_from_extractor", "review.human_critique", "review.independent_reader", "review.machine_critique", "step:run_analysis blocked on method:machine_learning_random_forest"], "assays": [{"role": "data_generation", "definition_id": "clinical_trial", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["iCoefficient(KCDEGi)*Expression(KCDEGi) SKCM patients were assigned to low- and high-risk groups based on the me"], "order_basis": "paper_text_order:5625", "id": "assay:1"}, {"role": "analysis", "definition_id": "machine_learning", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["The intersection between module genes associated with CAFs and DEGs related to anti-PD-1 therapy response were extracted and defined as CAFs-related DEGs (CDEGs), and further LASSO and random forest analysis were adopted to identify key CDEGs (KCDEGs) using the glmnet package (nlambda = 1000, 10-fold cross-validation) and randomForest package."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}