Evaluation of Feature Selection Techniques for Breast Cancer Risk Prediction

Smart Biosensor for Breast Cancer Survival Prediction Based on Multi-View Multi-Way Graph Learning

Biosensors play a crucial role in detecting cancer signals by orchestrating a series of intricate biological and physical transduction processes. Among various cancers, breast cancer stands out due to its genetic underpinnings, which trigger uncontrolled cell proliferation, predominantly impacting women, and resulting in significant mortality rates. The utilization of biosensors in predicting survival time becomes paramount in formulating an optimal treatment strategy. However, conventional biosensors employing traditional machine learning methods encounter challenges in preprocessing features for the learning task. Despite the potential of deep learning techniques to automatically extract useful features, they often struggle to effectively leverage the intricate relationships between features and instances. To address this challenge, our study proposes a novel smart biosensor architecture that integrates a multi-view multi-way graph learning (MVMWGL) approach for predicting breast cancer survival time. This innovative approach enables the assimilation of insights from gene interactions and biosensor similarities. By leveraging real-world data, we conducted comprehensive evaluations, and our experimental results unequivocally demonstrate the superiority of the MVMWGL approach over existing methods.

Genetic and lifestyle factors for breast cancer risk assessment in Southeast China

BACKGROUND

Despite the rising incidence and mortality of breast cancer among women in China, there are currently few predictive models for breast cancer in the Chinese population and with low accuracy. This study aimed to identify major genetic and life-style risk factors in a Chinese population for potential application in risk assessment models.

METHODS

A case-control study in southeast China was conducted including 1321 breast cancer patients and 2045 controls during 2013-2016, in which the data were randomly divided into a training set and a test set on a 7:3 scale. The association between genetic and life-style factors and breast cancer was examined using logistic regression models. Using AUC curves, we also compared the performance of the logistic model to machine learning models, namely LASSO regression model and support vector machine (SVM), and the scores calculated from CKB, Gail and Tyrer-Cuzick models in the test set.

RESULTS

Among all factors considered, the best model was achieved when polygenetic risk score, lifestyle, and reproductive factors were considered jointly in the logistic regression model (AUC = 0.73; 95% CI: 0.70-0.77). The models created in this study performed better than those using scores calculated from the CKB, Gail, and Tyrer-Cuzick models. However, the logistic model and machine learning models did not significantly differ from one another.

CONCLUSION

In summary, we have found genetic and lifestyle risk predictors for breast cancer with moderate discrimination, which might provide reference for breast cancer screening in southeast China. Further population-based studies are needed to validate the model for future applications in personalized breast cancer screening programs.

Accurate breast cancer diagnosis using a stable feature ranking algorithm

BACKGROUND

Breast cancer (BC) is one of the most common cancers among women. Since diverse features can be collected, how to stably select the powerful ones for accurate BC diagnosis remains challenging.

METHODS

A hybrid framework is designed for successively investigating both feature ranking (FR) stability and cancer diagnosis effectiveness. Specifically, on 4 BC datasets (BCDR-F03, WDBC, GSE10810 and GSE15852), the stability of 23 FR algorithms is evaluated via an advanced estimator (S), and the predictive power of the stable feature ranks is further tested by using different machine learning classifiers.

RESULTS

Experimental results identify 3 algorithms achieving good stability ([Formula: see text]) on the four datasets and generalized Fisher score (GFS) leading to state-of-the-art performance. Moreover, GFS ranks suggest that shape features are crucial in BC image analysis (BCDR-F03 and WDBC) and that using a few genes can well differentiate benign and malignant tumor cases (GSE10810 and GSE15852).

CONCLUSIONS

The proposed framework recognizes a stable FR algorithm for accurate BC diagnosis. Stable and effective features could deepen the understanding of BC diagnosis and related decision-making applications.

ConSIG: consistent discovery of molecular signature from OMIC data

The discovery of proper molecular signature from OMIC data is indispensable for determining biological state, physiological condition, disease etiology, and therapeutic response. However, the identified signature is reported to be highly inconsistent, and there is little overlap among the signatures identified from different biological datasets. Such inconsistency raises doubts about the reliability of reported signatures and significantly hampers its biological and clinical applications. Herein, an online tool, ConSIG, was constructed to realize consistent discovery of gene/protein signature from any uploaded transcriptomic/proteomic data. This tool is unique in a) integrating a novel strategy capable of significantly enhancing the consistency of signature discovery, b) determining the optimal signature by collective assessment, and c) confirming the biological relevance by enriching the disease/gene ontology. With the increasingly accumulated concerns about signature consistency and biological relevance, this online tool is expected to be used as an essential complement to other existing tools for OMIC-based signature discovery. ConSIG is freely accessible to all users without login requirement at https://idrblab.org/consig/.

ZNF143 Expression is Associated with COPD and Tumor Microenvironment in Non-Small Cell Lung Cancer

Background

Chronic obstructive pulmonary disease (COPD) is an inflammatory-related disease highly associated with increased lung cancer risk. Studies have explored the tumor promoting roles for zinc finger protein 143 (ZNF143). However, the role of ZNF143 in COPD and tumor microenvironment of non-small cell lung cancer (NSCLC) has not been fully elucidated.

Methods

COPD-related key genes were identified by differential gene expression evaluation, WGCNA and SVM-RFE analysis using mRNA expression data retrieved from public databases. ROC analysis was conducted to evaluate the diagnostic value of ZNF143. Correlation between ZNF143 and clinic-pathological features, associations with tumor-infiltrating immune cells (TICs) and the relationship with predictors of immunotherapy efficacy were explored. ZNF143 gene expression was validated by qRT-PCR using an independent cohort.

Results

Bioinformatic and machine learning analysis showed that ZNF143 was a COPD-related gene. ZNF143 expression was significantly upregulated in COPD and is a potential diagnostic biomarker in COPD with AUC > 0.85. ZNF143 expression was significantly upregulated in lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD). ZNF143 expression levels were significantly higher in LUAD patients with COPD relative to the levels in patients only with LUAD. Upregulation of ZNF143 in patients with comorbidity of NSCLC and COPD was further confirmed by qRT-PCR analysis. High expression of ZNF143 was significantly correlated with advanced TNM stage in LUSC. High ZNF143 expression was associated with activated TICs in both LUAD and LUSC samples. Moreover, ZNF143 expression was significantly correlated with the levels of several known predictors of immunotherapy efficacy, including PD-L1, PD-L2, TMB and TIDE in NSCLC.

Conclusion

ZNF143 is a novel COPD biomarker. High expression level of ZNF143 is associated with immune microenvironment and high risk of progression of COPD to NSCLC.