FLG Gene Mutation Up-regulates the Abnormal Tumor Immune Response and Promotes the Progression of Prostate Cancer
Bibliographic record
Abstract
BACKGROUND: Prostate Cancer (PCa) ranks sixth with regard to the cause of cancerinduced male diseases worldwide, and inflammation is closely associated with its morbidity, deterioration, and prognosis. Tumor Mutation Burden (TMB) is identified to be the most common biomarker for the prediction of immunotherapy. But it is still unclear about the relationship of gene mutations in PCa with TMB and immune response. OBJECTIVES: To study the relationship between gene mutation and anti-tumor immune response in the prostate cancer tumor microenvironment. METHODS: In the present work, the PCa somatic mutation data were collected from the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) datasets. RESULTS: As a result, 8 genes with high mutation frequency, including TP53, PTEN, TTN, FLG, CTNNB1, SPOP, MUC16, and KMT2C, were discovered to be covered by 4 cohorts from the United States, Canada, the United Kingdom, and China. Overall, the FLG mutation was related to a greater TMB, which predicted the dismal prognostic outcome. Besides, the CIBERSORT algorithm and Gene Set Enrichment Analysis (GSEA) were adopted for analysis, which revealed that FLG mutation remarkably promoted immune response in the context of PCa and accelerated cancer development. To sum up, FLG shows a high mutation frequency in PCa, and is related to the increase in TMB, up-regulation of abnormal immune responses in tumors, and promotion of tumor progression. CONCLUSION: Therefore, it may be used as a biomarker to predict the abnormal immune responses and provide a therapeutic target for immunotherapy in the treatment of PCa.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".