Integrated RNA-sequencing and network analysis approach to identify the Hub genes and vital pathways associated with gastric cancer
Bibliographic record
Abstract
Context: Gastric cancer is one of the most prevalent types of cancer in developing countries and ranks fourth in terms of death causes. Helicobacter pylori infection is a significant contributor to the emergence of gastric cancer. Lack of early diagnosis of gastric cancer is a leading cause of death. Aims: To identify the key genes and pathways involved in gastric cancer. Methods: This study performed a comprehensive analysis of RNA-Seq data from human gastric cancer and adjacent normal tissues. Raw data passed quality checks with FastQC and were aligned to GRCh38 using HISAT2. Subread's FeatureCounts handled transcript assembly and quantification. DESeq2 pinpointed significant genes, while ClueGO explored gene ontology and KEGG pathways. Protein-protein interaction networks, constructed with StringApp, aided in identifying hub genes through CytoHubba. This holistic approach yields insights into the molecular mechanisms underpinning gastric cancer. Results: This study detected 711 differentially expressed genes (DEGs) between normal and gastric samples. A total of 594 genes were identified as upregulated and 117 as downregulated. Major DEGs are enriched in signal transduction, stimulus-response regulation, transmembrane signaling receptor activity, and signal transduction pathways involving cytokines. In addition, 20 hub genes from the PPI network were identified based on MCC rank analysis from the CytoHubba plugin, contributing to the progression of gastric cancer. Conclusions: The top six hub genes, CD4, CTLA4, CD28, CD80, CD27, and SELL, are expected to regulate several pathways and may serve as potential biomarkers for the early detection and treatment of gastric cancer patients.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".