ReviewBiomarker research2026
Cancer in transition: discovery of tumor-intrinsic transcriptional programs shaping the immune and microenvironmental landscape.
Review in Biomarker research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Machine Learning-Based Identification of Biomarkers for Early-Stage Non-Small Cell Lung Cancer Through Gene Expression Analysis.International journal of molecular sciences · 2026Article
- Comparative evaluation of hrHPV DNA testing, cervical cytology and histopathology for cervical precancer detection.Bioinformation · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer biomarker discovery has traditionally focused on individual molecular features; however, tumor behavior and therapeutic response are governed by integrated transcriptional and epigenetic programs that shape immune and microenvironmental states. Deciphering the determinants of metastatic evolution and the complexity of cancer ecosystems is imperative for designing novel preventive and targeted therapies. Tumor complexity, driven by intrinsic cellular heterogeneity and the dynamic plasticity of cancer cells in response to microenvironmental cues, complicates therapeutic strategies based solely on defined molecular or genetic traits. Consequently, there is an urgent need for reliable predictive biomarkers that reflect cancer vulnerabilities, indicate disease progression, or predict patient-specific therapeutic responses to enable truly individualized treatment strategies. Current biomarkers encompass genetic and epigenetic alterations, non-coding RNAs, epithelial-mesenchymal transition-, stemness-, and metastasis-associated transcription factors, as well as cellular components of the tumor microenvironment, including immune cell subsets and cancer-associated fibroblasts. In immuno-oncology, additional biomarkers such as tumor mutational burden, mismatch repair deficiency/microsatellite instability-high status, and PD-L1 expression are widely used for patient stratification. Importantly, the reversible nature of epigenetic modifications, aberrant transcription factor activity, and cell-intrinsic signaling alterations, together with dynamic interactions within the tumor microenvironment, profoundly influence cancer behavior and treatment outcomes. This review summarizes recent advances in cancer and immune-related biomarker research. It outlines a regulatory, systems-level framework that integrates tumor-intrinsic gene control programs with multi-omic, cellular, spatial, and AI-enabled biomarkers. This framework aims to capture tumor plasticity more effectively and advance precision oncology.
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Registered trials
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 Socratic graph.