Identification of Differentially Expressed Genes and Prognostic Biomarkers of Breast Cancer Based on RNA-Seq and KEGG Pathway Network
Bibliographic record
Abstract
The incidence of breast cancer is a complex biological process and multiple genes involved in the regulation. The gene expression differences of tumor cells between different patients’ determine the different treatment and prognosis. Therefore investigate the characteristics changes of breast cancer from a genetic level include identification of differentially expressed genes and prognostic markers will facilitate the development of appropriate and effective treatment. This subject obtained RNA-Seq Level 3 gene expression data from TCGA database, SAM algorithm was used to find differentially expressed genes. Next, the DAVID bioinformatics tool was employed to analyze the function of these genes, and obtained the significantly enriched pathways of these genes. Then gene interaction information was extracted from the pathways, KEGG pathway network was built by integrating these information, and the network topology were analyzed. The hub nodes extracted from the network were as candidate genes. Then the genes which have a significant impact on the survival were identified by using Cox proportional hazards regression model. And these genes were introduced into a multivariate analysis, the sample risk scores were calculated, according to which samples were divided into a high risk group and a low risk group. The survival difference between these two groups was analyzed using Kaplan Meier method, and logrank test was used to assess the statistical significant. By analyzing the gene expression dataset of TCGA database, a total of 5880 differentially expressed genes were found. Eight significant pathways were obtained by enrichment analysis. Then we used the interaction information of genes extracted from the pathways to build a KEGG pathway network, and 32 candidate genes were obtained from the network. Three significant genes (AARS, ADK, and ADORA2A) which have significant impact on the prognosis of breast cancer were identified by Cox proportional hazards. These three genes can be used as new prognostic biomarkers in breast cancer, provide guidance for the treatment of breast cancer. Wherein AARS has been proven associated with breast cancer risk. By multivariate analysis, this subject divided breast cancer into a high risk group and a low risk group, and there exits significant difference between them.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".