5-Methylcytosine (m5C) Modification Patterns and Tumor Immune Infiltration Characteristics in Clear Cell Renal Cell Carcinoma
Bibliographic record
Abstract
Recently, studies have revealed the prognostic value of 5-methylcytosine (m5C) in clear cell renal cell carcinoma (ccRCC). However, the role of m5C methylation in ccRCC immune infiltration and the immunotherapeutic response remains unknown. Based on the mRNA expressions of 14 m5C regulators, we evaluated the m5C modification patterns of 530 tumor samples from the TCGA-ccRCC database. We used the principal component analysis (PCA) algorithm to construct individual patient m5Cscores to facilitate individual analysis of m5C modification patterns in ccRCC patients. We finally defined three different m5C modification patterns. Different clinical features and immune heterogeneity existed among the three patterns, and their immune infiltration characteristics could correspond to different immune phenotypes, including the immune-inflamed, immune-excluded, and immune-desert phenotype. We designed the m5Cscore calculated by the PCA algorithm to measure individual patients' m5C modification patterns. The low m5Cscore group presented with a positive prognosis, increased TMB, and immune activation. Additionally, low m5Cscore patients showed an increased response to immune checkpoint inhibitors. We further the value of the m5Cscore in predicting OS verified in four other tumor cohorts. Our findings revealed that m5C methylation modifications are essential in regulating ccRCC immune infiltration. Assessing single ccRCC patients' m5C modification patterns can fully improve our comprehension of tumor immune characteristics and be used to provide effective personalized immunotherapy strategies for clinical use.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".