Identification of lymph node metastasis-related genes and patterns of immune infiltration in colon adenocarcinoma
Bibliographic record
Abstract
Backgrounds: Colon adenocarcinoma(COAD) is one of the most common tumors of the digestive tract. Lymph node metastasis (LNM) is a well-established prognostic factor for COAD. The mechanism of COAD lymph node metastasis in immunology remains unknown. The identification of LNM-related biomarkers of COAD could help in its treatment. Thus, the current study was aimed to identify key genes and construct a prognostic signature. Methods: Gene expression and clinical data were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes were calculated by using R software. GO functional and KEGG pathway enrichment analysis were processed. The CIBERSORT algorithm was used to assess immune cell infiltration. STRING database was used to screen key genes and constructed a protein-protein interaction network (PPI network). The LASSO-Cox regression analysis was performed based on the components of the PPI network. The correlation analysis between LNM-related signature and immune infiltrating cells was then investigated. TISIDB was used to explore the correlation between the abundance of immunomodulators and the expression of the inquired gene. Results: In total, 394 differentially expressed genes were identified. After constructing and analyzing the PPI network, 180 genes were entered into the LASSO-Cox regression model, constructing a gene signature. Five genes(PMCH, LRP2, NAT1, NKAIN4, and CD1B) were identified as LNM-related genes of clinical value. Correlation analysis revealed that LRP2 and T follicular helper cells (R=0.34, P=0.0019) and NKAIN4 and T follicular helper cells (R=0.23, P=0.041) had significant correlations. Immunologic analysis revealed that LRP2 and NKAIN4 are potential coregulators of immune checkpoints in COAD. Conclusion: In general, this study revealed the key genes related to lymph node metastasis and prognostic signature. Several potential mechanisms and therapeutic and prognostic targets of lymph node metastasis were also demonstrated in COAD.
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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) | 0.000 | 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) | 0.001 | 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".