Gastrointestinal cancer: molecular pathogenesis and targeted therapy
Bibliographic record
Abstract
Gastrointestinal (GI) cancers pose a significant global health burden, driven by complex molecular alterations and microenvironmental interactions. Advances in molecular pathogenesis have elucidated recurrent driver gene mutations, such as KRAS, TP53, and APC, alongside dysregulated signaling pathways including Wnt, RAS-MAPK, and PI3K-AKT, which collectively underpin tumor initiation and progression. Complementing genetic changes, epigenetic alterations-such as DNA hypermethylation, histone modifications, and regulatory non-coding RNAs-further contribute to malignant evolution by reshaping chromatin architecture and gene expression. These mechanisms not only promote uncontrolled proliferation but also reinforce therapeutic resistance by dynamically modifying the tumor microenvironment (TME). Molecular subtyping efforts, including The Cancer Genome Atlas (TCGA) classification for gastric cancer (GC) and the Consensus Molecular Subtypes (CMS) for colorectal cancer (CRC), have delineated disease heterogeneity, revealing distinct pathogenic pathways and enabling refined prognostic stratification. Such insights provide the biological rationale for diagnostic techniques and targeted interventions. For instance, anti-EGFR and anti-VEGF monoclonal antibodies disrupt oncogenic signaling and tumor angiogenesis, respectively, and have demonstrated substantial clinical efficacy in selected patient populations. In parallel, immunotherapy has emerged as a transformative modality in oncology. Immune checkpoint inhibitors targeting PD-1/PD-L1 and CTLA-4 reinvigorate antitumor immunity and have reshaped standard-of-care protocols for several GI malignancies. Beyond conventional immunotherapies, innovative strategies such as CAR-T cell therapy and neoantigen-based vaccines are being actively investigated. These approaches aim to overcome immune evasion mechanisms and enhance tumor-specific targeting, offering promise for patients with resistant or advanced disease. This review comprehensively analyzes the evolving molecular landscape of GI cancers and the corresponding development of targeted and immunotherapeutic agents. It highlights a balanced integration of mechanistic discovery and clinical translation, underscoring their synergistic roles in advancing precision oncology and improving survival outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".