bioRxivBioinformaticsPreprint
Solid Tumors Pan Cancer Transcriptome: Tissue/Cancer specific expression groups at the Isoform-Level
Abstract
Most of the human genome is transcribed into diverse isoforms whose tissue specificity is profoundly disrupted in cancer, yet isoform-level dysregulation remains poorly characterized across solid tumors. Here, we introduce STPCaT (Solid Tumors Pan-Cancer Transcriptome), an isoform-centric analysis extending TransTEx to systematically classify transcript expression across TCGA solid tumors and GTEx normal tissues. STPCaT reveals a striking collapse of normal tissue-specific programs in cancer, accompanied by the emergence of two dominant expression groups: cancer-high (CanHigh) and normal-high (NorHigh) isoforms. We uncover a large repertoire of previously unannotated Cancer-Testis Antigens (CTAs), majority of which are absent from existing CTA databases, with broad relevance across multiple cancers, including gliomas. In pan-gliomas, consensus clustering and random-forest feature selection identify compact, highly discriminative isoform signatures that robustly stratify low-grade and glioblastomas with up to 97-98% accuracy using as few as five transcripts. These signatures recapitulate canonical glioma biology and highlight pathways linked to migration, development, and vesicle trafficking. Independent validation in the GLASS consortium cohort demonstrates cohort-specific trends that partially recapitulate primary findings, reflecting known biological heterogeneity across patient populations. Together, STPCaT provides a scalable, isoform-resolved resource for tumor stratification, CTAs discovery, and precision oncology applications across solid tumors. TeaserSTPCaT uncovers an isoform-level collapse of tissue specificity across solid tumors, revealing a hidden landscape of diagnostic biomarkers and unannotated cancer-testis antigens.
- organism: human
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: database / resource
- research focus: benchmarking
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 1
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Detecting predicted cancer-testis antigens in proteomics datasets of healthy and tumoral samples
Karla Cristina Tabosa Machado, Tayná da Silva Fiúza, … Gustavo A. de Souza·IEEE AccessPeer-reviewed·Jan 1, 2024
- Cancer/Testis Antigens Differentially Expressed in Prostate Cancer: Potential New Biomarkers and Targets for Immunotherapies
P. Kulkarni, N. M. Carleton, … L. Kagohara·bioRxiv·Aug 22, 2019
- Transcriptome analysis reveals high tumor heterogeneity with respect to re-activation of stemness and proliferation programs
Artem Baranovsky, Timofei M. Ivanov, … Dmitri Davidovich Pervouchine·bioRxiv·Sep 2, 2021
- Differential expression of H2A isoforms contribute to tissue and lineage specificity with HIST2H2AC as a potential cancer biomarker
S. G. Shah, M. Rashid, … S. Gupta·bioRxiv·Mar 10, 2021
- Cancer as a tissue anomaly: classifying tumor transcriptomes based only on healthy data
Thomas P. Quinn, Thin Nguyen, … Svetha Venkatesh·Frontiers in GeneticsPeer-reviewed·Jul 2, 2019
- Pathogenic impact of transcript isoform switching in 1209 cancer samples covering 27 cancer types using an isoform-specific interaction network
A. Kahraman, T. Karakulak, … C. von Mering·bioRxiv·Mar 27, 2020