Evidence map›Paper›PMID 42324541›Full record

ArticleJournal of translational medicine2026

The expression landscape and clinical significance of cancer-specific RNA transcripts across human cancers.

Haochen Li, Jie Ding, Xianghuo He, Zhiao Chen

Abstract read
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Article in Journal of translational medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Haochen Li *Fudan University Shanghai Cancer Center and Institute of Biomedical Sciences, Shanghai Medical College, Fudan University, Shanghai, 200032, China.
Jie Ding *Fudan University Shanghai Cancer Center and Institute of Biomedical Sciences, Shanghai Medical College, Fudan University, Shanghai, 200032, China.
Xianghuo HeFudan University Shanghai Cancer Center and Institute of Biomedical Sciences, Shanghai Medical College, Fudan University, Shanghai, 200032, China. xhhe@fudan.edu.cn.
Zhiao ChenFudan University Shanghai Cancer Center and Institute of Biomedical Sciences, Shanghai Medical College, Fudan University, Shanghai, 200032, China. zachen@fudan.edu.cn.ORCID 0000-0003-4105-8674

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTranscript-level analyses allow for the precise characterization of gene expression and its functional role in cancer. However, most of these studies rely on reanalyses of next-generation sequencing data, whose incomplete or inaccurate assemblies limit the comprehensive and faithful characterization of transcripts. To systematically elucidate transcriptomic expression in tumors and define broadly applicable therapeutic strategies, we investigated cancer-specific RNA transcripts (cancer-SRTs) expressed across multiple cancer types based on long-read sequencing data.

methodsWe characterized the expression profiles of 44,405 cancer-SRTs across multiple cancer types using t-SNE and correlation analyses. Transcripts expressed in more than 10 cancer types were further investigated through enrichment, survival, and correlation analyses to elucidate their functions and clinical relevance. To explore the mechanisms driving cancer-SRT generation, we analyzed alternative splicing events within these transcripts and integrated copy number variation, DNA methylation, and ATAC-seq data from matched TCGA tumor samples. Using the expression of 131 transcripts strongly associated with tumor hallmarks, we developed a risk-score model to evaluate associations with patient survival, tumor stage, immune characteristics, and responses to immune checkpoint blockade. Finally, the in vitro anti-tumor effects of siRNAs targeting two cancer-SRTs were evaluated using CCK-8 assay, colony formation, and transwell assays.

resultsCancer-SRTs exhibit substantial structural diversity and are enriched in malignancy-associated pathways. The expression of these transcripts is associated with multiple genomic and epigenetic processes. We identify 131 transcripts that are strongly associated with tumor hallmarks and develop a risk-score model for evaluating patient prognosis and tumor progression. The model also exhibited strong associations with features of immune evasion.

conclusionsCancer-SRTs are widely expressed yet highly heterogeneous across tumor types, and are subject to multiple regulatory mechanisms underlying their functional and clinical significance. These findings advance our understanding of tumor biology and lay the groundwork for developing diagnostic, prognostic, and therapeutic strategies based on these transcripts. Future studies investigating their underlying mechanisms and applications in immunotherapy will be critical for precision cancer treatment.

Indexed as

Gene Expression ProfilingGene Expression Regulation, NeoplasticNeoplasmsRNA, NeoplasmAlternative SplicingDNA MethylationHumansRNA, MessengerRNA, MessengerRNA, NeoplasmBiomarkersCancer specific RNA transcriptsClinical relevanceExpression regulationPan-cancer

Identifiers

PMID42324541
PMCPMC13307683

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.