Integrated Analysis of Long Noncoding RNA and Coding RNA Expression in Esophageal Squamous Cell Carcinoma
Bibliographic record
Abstract
Tumorigenesis is a complex dynamic biological process that includes multiple steps of genetic and epigenetic alterations, aberrant expression of noncoding RNA, and changes in the expression profiles of coding genes. We call the collection of those perturbations in genome space the "cancer initiatome." Long noncoding RNAs (lncRNAs) are pervasively transcribed in the genome and they have key regulatory functions in chromatin remodeling and gene expression. Spatiotemporal variation in the expression of lncRNAs has been observed in development and disease states, including cancer. A few dysregulated lncRNAs have been studied in cancers, but the role of lncRNAs in the cancer initiatome remains largely unknown, especially in esophageal squamous cell carcinoma (ESCC). We conducted a genome-wide screen of the expression of lncRNAs and coding RNAs from ESCC and matched adjacent nonneoplastic normal tissues. We identified differentially expressed lncRNAs and coding RNAs in ESCC relative to their matched normal tissue counterparts and validated the result using polymerase chain reaction analysis. Furthermore, we identified differentially expressed lncRNAs that are co-located and co-expressed with differentially expressed coding RNAs in ESCC and the results point to a potential interaction between lncRNAs and neighboring coding genes that affect ether lipid metabolism, and the interaction may contribute to the development of ESCC. These data provide compelling evidence for a potential novel genomic biomarker of esophageal squamous cell cancer.
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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".