Transcriptomic insights into skin cancer: A bioinformatics and network biology approach to biomarker identification
Bibliographic record
Abstract
Skin cancer is a widespread malignancy that primarily affects light-skinned populations globally, categorized into melanoma and non-melanoma skin cancers (NMSCs). Basal cell carcinoma and squamous cell carcinoma are the most common subtypes within NMSCs, with the global incidence of NMSCs projected to reach 2–3 million cases annually across regions like Europe, Canada, the USA, and Australia. Despite this prevalence, the genetic mechanisms behind skin cancer remain poorly understood. This study presents a novel gene discovery approach, leveraging transcriptome data from Next-Generation Sequencing datasets sourced from the European Nucleotide Archive to uncover new genes and pathways linked to skin cancer. The novelty of this research lies in its comprehensive approach that combines differential gene expression analysis with gene network and pathway enrichment analysis to identify actionable therapeutic targets. By utilizing bioinformatics tools such as DESeq2, Gene Set Enrichment Analysis (GSEA), and Cytoscape, we revealed critical gene interactions and pathways that have been underexplored in the context of skin cancer. Following rigorous quality control using FastQC and transcriptome-seq data alignment to the human genome (hg38), we identified 19 differentially expressed genes, including 2 down-regulated and 17 up-regulated. Key genes such as IL6, CCND2, PLAUR, and CD44 were found to be involved in important pathways like IL6_JAK_STAT3_SIGNALING, ANGIOGENESIS, and APICAL_SURFACE. These findings provide valuable insights into skin cancer pathogenesis and offer potential therapeutic targets, laying the groundwork for future research aimed at improving treatment outcomes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".