{"doi":"10.1111/bph.70390","title":"TNMplot: An enhanced platform for pharmacological target identification through cross‐stage and pan‐cancer gene expression analysis","abstract":"<jats:p>\n                    <jats:ext-link xmlns:xlink=\"http://www.w3.org/1999/xlink\" xlink:href=\"http://TNMplot.com\">TNMplot.com</jats:ext-link>\n                    , a web‐based platform integrating RNA‐Seq and gene‐chip data from 56,938 samples, enables differential gene expression analysis across normal, primary tumour and metastatic tissues, facilitating large‐scale transcriptomic profiling across 22 tumour types. We introduced an updated version of the TNMplot database with novel features that support pharmacological and translational oncology research. A key addition is the stage‐based expression comparison, which allows the identification of progression‐related genes from 4470 cancer samples, including breast (\n                    <jats:italic>n</jats:italic>\n                     = 2331), colorectal (\n                    <jats:italic>n</jats:italic>\n                     = 648), lung (\n                    <jats:italic>n</jats:italic>\n                     = 1399), skin (\n                    <jats:italic>n</jats:italic>\n                     = 31) and prostate (\n                    <jats:italic>n</jats:italic>\n                     = 61) tumours. These progression markers can inform drug target discovery and the timing of therapeutic intervention. The platform now includes enhanced visualisation tools, such as a pan‐cancer dot matrix enabling simultaneous multi‐tissue and multi‐gene comparison, and new multi‐gene analytics including density plots, gene–gene correlation, correlation matrices, correlation profile analysis, gene signature evaluation and targetgram analysis. These tools support the investigation of druggable pathways, co‐expression networks and pharmacogenomic biomarker panels. A unique feature of TNMplot remains the parallel analysis of RNA‐seq and gene chip datasets, enabling robust cross‐platform validation of candidate pharmacological targets in diverse patient populations. In conclusion, the updated TNMplot platform offers a comprehensive and versatile environment for transcriptomic analysis in support of pharmacological hypothesis generation, biomarker discovery and preclinical target validation in oncology.\n                  </jats:p>","journal":"British Journal of Pharmacology","year":2026,"id":647850,"datarank":0.26876392038420827,"base_score":1.791759469228055,"endowment":1.791759469228055,"self_citation_contribution":0.26876392038420827,"citation_network_contribution":0.0,"self_endowment_contribution":0.26876392038420827,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":5,"citer_count":4,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":311112,"name":"Balázs Győrffy","orcid":"0000-0002-5772-3766","position":1,"is_corresponding":false},{"id":1688060,"name":"Áron Bartha","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"TNMplot: An enhanced platform for pharmacological target identification through cross‐stage and pan‐cancer gene expression analysis","abstract":"<jats:p>\n                    <jats:ext-link xmlns:xlink=\"http://www.w3.org/1999/xlink\" xlink:href=\"http://TNMplot.com\">TNMplot.com</jats:ext-link>\n                    , a web‐based platform integrating RNA‐Seq and gene‐chip data from 56,938 samples, enables differential gene expression analysis across normal, primary tumour and metastatic tissues, facilitating large‐scale transcriptomic profiling across 22 tumour types. We introduced an updated version of the TNMplot database with novel features that support pharmacological and translational oncology research. A key addition is the stage‐based expression comparison, which allows the identification of progression‐related genes from 4470 cancer samples, including breast (\n                    <jats:italic>n</jats:italic>\n                     = 2331), colorectal (\n                    <jats:italic>n</jats:italic>\n                     = 648), lung (\n                    <jats:italic>n</jats:italic>\n                     = 1399), skin (\n                    <jats:italic>n</jats:italic>\n                     = 31) and prostate (\n                    <jats:italic>n</jats:italic>\n                     = 61) tumours. These progression markers can inform drug target discovery and the timing of therapeutic intervention. The platform now includes enhanced visualisation tools, such as a pan‐cancer dot matrix enabling simultaneous multi‐tissue and multi‐gene comparison, and new multi‐gene analytics including density plots, gene–gene correlation, correlation matrices, correlation profile analysis, gene signature evaluation and targetgram analysis. These tools support the investigation of druggable pathways, co‐expression networks and pharmacogenomic biomarker panels. A unique feature of TNMplot remains the parallel analysis of RNA‐seq and gene chip datasets, enabling robust cross‐platform validation of candidate pharmacological targets in diverse patient populations. In conclusion, the updated TNMplot platform offers a comprehensive and versatile environment for transcriptomic analysis in support of pharmacological hypothesis generation, biomarker discovery and preclinical target validation in oncology.\n                  </jats:p>","is_dataset_classified":null,"base_score":1.791759469228055,"endowment":1.791759469228055,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"41858237","pmcid":null,"openalex_id":"https://openalex.org/W7138990671","authors":[],"funders":[{"funder_name":"Semmelweis Momentum Programme","grant_id":"","title":null}],"total_grants":1,"fwci":17.9685,"citation_percentile":0.99362586,"influential_citations":0,"citation_trend":[{"year":2026,"count":5}],"oa_status":"hybrid","license":"cc-by","oa_locations":[{"url":"https://doi.org/10.1111/bph.70390","host_type":"journal"},{"url":"https://doi.org/10.1111/bph.70390","host_type":"publisher"},{"url":"https://bpspubs.onlinelibrary.wiley.com/doi/pdf/10.1111/bph.70390","host_type":"publisher"},{"url":"https://bpspubs.onlinelibrary.wiley.com/doi/full-xml/10.1111/bph.70390","host_type":"publisher"},{"url":"https://pubmed.ncbi.nlm.nih.gov/41858237","host_type":"repository"}],"fields_of_study":["Gene expression and cancer classification","Bioinformatics and Genomic Networks","Advanced Biosensing Techniques and Applications"],"mesh_terms":["Antineoplastic Agents","Humans","Neoplasms","Gene Expression Regulation, Neoplastic","Gene Expression Profiling","Databases, Genetic","Transcriptome"],"keywords":["Biomarker discovery","Druggability","Gene expression profiling","Identification (biology)","Pharmacogenomics","Biomarker","Gene expression","Drug discovery","Transcriptome","Cancer","Differential expression","Gene array","Transcriptomics","Rna‐seq"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-10T01:57:25.227807Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}