Introducing effective genes in lymph node metastasis of breast cancer patients using SHAP values based on the mRNA expression data.

OBJECTIVE: Breast cancer, a global concern predominantly impacting women, poses a significant threat when not identified early. While survival rates for breast cancer patients are typically favorable, the emergence of regional metastases markedly diminishes survival prospects. Detecting metastases and comprehending their molecular underpinnings are crucial for tailoring effective treatments and improving patient survival outcomes. METHODS: Various artificial intelligence methods and techniques were employed in this study to achieve accurate outcomes. Initially, the data was organized and underwent hold-out cross-validation, data cleaning, and normalization. Subsequently, feature selection was conducted using ANOVA and binary Particle Swarm Optimization (PSO). During the analysis phase, the discriminative power of the selected features was evaluated using machine learning classification algorithms. Finally, the selected features were considered, and the SHAP algorithm was utilized to identify the most significant features for enhancing the decoding of dominant molecular mechanisms in lymph node metastases. RESULTS: In this study, five main steps were followed for the analysis of mRNA expression data: reading, preprocessing, feature selection, classification, and SHAP algorithm. The RF classifier utilized the candidate mRNAs to differentiate between negative and positive categories with an accuracy of 61% and an AUC of 0.6. During the SHAP process, intriguing relationships between the selected mRNAs and positive/negative lymph node status were discovered. The results indicate that GDF5, BAHCC1, LCN2, FGF14-AS2, and IDH2 are among the top five most impactful mRNAs based on their SHAP values. CONCLUSION: The prominent identified mRNAs including GDF5, BAHCC1, LCN2, FGF14-AS2, and IDH2, are implicated in lymph node metastasis. This study holds promise in elucidating a thorough insight into key candidate genes that could significantly impact the early detection and tailored therapeutic strategies for lymph node metastasis in patients with breast cancer.

Uncovering key molecular mechanisms in the early and late-stage of papillary thyroid carcinoma using association rule mining algorithm.

OBJECTIVE: Thyroid Cancer (TC) is the most frequent endocrine malignancy neoplasm. It is the sixth cause of cancer in women worldwide. The treatment process could be expedited by identifying the controlling molecular mechanisms at the early and late stages, which can contribute to the acceleration of treatment schemes and the improvement of patient survival outcomes. In this work, we study the significant mRNAs through Machine Learning Algorithms in both the early and late stages of Papillary Thyroid Cancer (PTC). METHOD: During the course of our study, we investigated various methods and techniques to obtain suitable results. The sequence of procedures we followed included organizing data, using nested cross-validation, data cleaning, and normalization at the initial stage. Next, to apply feature selection, a t-test and binary Non-Dominated Sorting Genetic Algorithm II (NSGAII) were chosen to be employed. Later on, during the analysis stage, the discriminative power of the selected features was evaluated using machine learning and deep learning algorithms. Finally, we considered the selected features and utilized Association Rule Mining algorithm to identify the most important ones for improving the decoding of dominant molecular mechanisms in PTC through its early and late stages. RESULT: The SVM classifier was able to distinguish between early and late-stage categories with an accuracy of 83.5% and an AUC of 0.78 based on the identified mRNAs. The most significant genes associated with the early and late stages of PTC were identified as (e.g., ZNF518B, DTD2, CCAR1) and (e.g., lnc-DNAJB6-7:7, RP11-484D2.3, MSL3P1), respectively. CONCLUSION: Current study reveals a clear picture of the potential candidate genes that could play a major role not only in the early stage, but also throughout the late one. Hence, the findings could be of help to identify therapeutic targets for more effective PTC drug developments.