Identification of Hub Gene TIMP1 and Relative ceRNAs Regulatory Network in Colorectal Cancer
Bibliographic record
Abstract
OBJECTIVE: This study aimed to discover the ceRNAs network in the pathophysiological development of human colorectal cancer (CRC) and to screen biomarkers for target therapy and prognosis by using integrated bioinformatics analysis. METHODS: Data on gene expressions of mRNAs, miRNAs, and circRNAs and clinical information were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases, respectively. Differentially expressed mRNAs (DEmRNAs) were identified by using the DESeq2 package of R software. Functional enrichment analysis was conducted using the ClusterProfiler package of R software. The protein-protein interaction (PPI) network was shown by the STRING website. Survival analysis of hub genes was performed using the survival package in R software. Interactions among hub genes, differentially expressed miRNAs (DEmiRNAs), and differentially expressed circRNAs (DEcircRNAs) were used to construct the ceRNAs network. RESULTS: A total of 412 DEmRNAs including 82 upregulated and 330 downregulated genes were screened out between 473 CRC and 41 normal samples. Two hundred and sixty DEcircRNAs including 253 upregulated and 7 downregulated genes were altered between 23 CRC and 23 normal samples. One hundred and ninety DEmiRNAs including 82 upregulated and 108 downregulated genes were obtained between 450 CRC and 8 normal samples. A ceRNAs and PPI network were successfully constructed, and TIMP1 associated with prognosis was employed. CONCLUSION: The present study identified a novel circRNAs-miRNAs-mRNA ceRNAs network, which implied that TIMP1 and related miRNAs, circRNAs were potential biomarkers underlying the development of CRC, providing new insights for survival predictions and therapeutic targets.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".