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Identification of prognostic gene biomarkers for metastatic skin cancer using data mining
Gang Liu1, Chen Li1, Haiyan Zhen2
1School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu 730000, P.R. China.
Abstract:
Skin cancer is a common malignant tumor in China and throughout the world, and the rate of recurrence is considerably high, thus endangering the quality of life and health of patients, and increasing the economic burden and pressure to the families of those afflicted. Due to the limitations of traditional drug treatments, it is difficult to achieve the desired therapeutic effect of complete removal. However, targeted gene therapy may be a novel means of treating skin cancer, as the targeted nature of treatment may improve therapeutic outcomes. However, targeted gene therapy requires physicians to select the appropriate gene, which means suitable genetic biomarkers must be identified from complex genetic data. In the present study, the least absolute shrinkage and selection operator regression analysis method was used with 10-fold cross verification to reduce the dimensions of gene data in patients with skin cancer, and subsequently, 20 gene biomarkers were screened. A prognostic model was constructed using these 20 gene biomarkers, and the validity of the model was assessed using a training set and a verification set, which showed that the model performed well. Finally, gene function analysis of these 20 gene biomarkers was determined. Relevant studies were found to show that the genetic biomarkers identified in this paper may possess value for the follow-up clinical treatment of skin cancer.
Insights
Researchers identified 20 gene biomarkers for skin cancer prognosis using advanced regression analysis. This discovery could lead to more effective targeted gene therapies for this common malignancy, improving patient outcomes.
Area of Science:
- Oncology
- Genetics
- Bioinformatics
Background:
- Skin cancer presents a significant global health challenge with high recurrence rates.
- Traditional treatments often fall short of complete tumor removal, necessitating innovative therapeutic approaches.
- Targeted gene therapy offers a promising avenue for improved skin cancer treatment outcomes.
Purpose of the Study:
- To identify reliable genetic biomarkers for skin cancer.
- To develop a prognostic model for skin cancer using identified biomarkers.
- To explore the potential of these biomarkers in targeted gene therapy.
Main Methods:
- Utilized least absolute shrinkage and selection operator (LASSO) regression analysis.
- Employed 10-fold cross-validation for robust gene selection from complex genetic data.
- Constructed and validated a prognostic model using a training and verification set.
Main Results:
- Successfully screened 20 significant gene biomarkers associated with skin cancer.
- Developed a prognostic model demonstrating good performance in both training and verification sets.
- Performed gene function analysis on the identified biomarkers.
Conclusions:
- The 20 identified gene biomarkers show potential for advancing skin cancer prognosis.
- These biomarkers may be valuable for future clinical applications and targeted gene therapy development.
- The study highlights the utility of bioinformatics methods in identifying key genetic markers for complex diseases.
