CKLF as a Prognostic Biomarker and Its Association with Immune Infiltration in Hepatocellular Carcinoma
Bibliographic record
Abstract
The Chemokine-like factor (CKLF)-like MARVEL transmembrane domain-containing (CMTM) family, comprising nine members, is involved in the tumorigenesis and progression of various cancers. However, the expression profiles and clinical significance of CMTM family members in hepatocellular carcinoma (HCC) are not fully clarified. In this study, the RNA-sequencing and clinical data were downloaded from The Cancer Genome Atlas (TCGA) databases. The Kaplan–Meier method and the Cox proportional hazards regression analysis were used to evaluate the prognostic significance of CMTM family members. Single-sample gene set enrichment analysis (ssGSEA) and ESTIMATE algorithms were employed to explore the relationship between CMTM family genes and the tumor microenvironment in HCC. Finally, the prognostic CMTM family gene expression was further validated by quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemical (IHC) staining in clinical HCC tissue specimens. The results indicated that, compared with normal tissues, the expression of CKLF, CMTM1, CMTM3, CMTM4, CMTM7, and CMTM8 were significantly upregulated in HCC, while the expression of CMTM2, CMTM5, and CMTM6 were significantly downregulated in HCC. Univariate and multivariate Cox regression analysis demonstrated that CKLF was an independent prognostic biomarker for the overall survival (OS) of HCC patients. In HCC, the expression of CKLF was found to be correlated with immune cell infiltration, immune-related functions, and immune checkpoint genes. The qRT-PCR and IHC confirmed that CKLF was highly expressed in HCC. Overall, this research suggested that CKLF is involved in immune cell infiltration and may serve as a critical prognostic biomarker, which provides new light on the therapeutics for HCC.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".