Multi-omics analysis identifies DLX4 as a novel biomarker for diagnosis, prognosis, and immune infiltration: from pan-cancer to renal cancer
Bibliographic record
Abstract
BACKGROUND: DLX4 is involved in the regulation of embryonic development, but its function in cancer remains unclear. Here, we conducted a pan-cancer analysis to investigate the molecular mechanisms of DLX4, with a particular emphasis on its role in renal cancer. METHODS: A comprehensive analysis of DLX4 was performed, focusing on differences in expression, prognostic value, somatic mutations, methylation modifications, and immune landscapes across various cancer types using multiple databases. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were utilized to explore the potential biological functions. Additionally, we evaluated the expression profile, prognostic significance, and immune infiltration of DLX4 in Kidney Renal Clear Cell Carcinoma (KIRC). The effect of DLX4 on KIRC was further validated by Spatial Transcriptomics, Real-time PCR (RT-PCR), and Immunohistochemistry experiments. RESULTS: DLX4 was found to be upregulated in 26 cancer types and associated with poor prognosis. It was also correlated with tumor mutational burden (TMB), microsatellite instability, mismatch repair, and methylation, and was significantly enriched in pathways related to cell proliferation. In KIRC, DLX4 expression increased along with TMB and immune scores, likely due to the infiltration of regulatory T cells (Tregs) and T-helper 2 (Th2) cells. Spatial transcriptomics revealed a strong correlation between DLX4 localization and tumor cells. Experimental validation confirmed that DLX4 expression is significantly upregulated in renal cancer tissues. CONCLUSION: Our study explored the mechanisms of DLX4 in pan-cancer, especially in renal clear cell carcinoma, identifying it as a promising biomarker and therapeutic target.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".