Multi-level characteristics recognition of cancer core therapeutic targets and drug screening for a broader patient population

Introduction: Target therapy for cancer cell mutation has brought attention to several challenges in clinical applications, including limited therapeutic targets, less patient benefits, and susceptibility to acquired due to their clear biological mechanisms and high specificity in targeting cancers with specific mutations. However, the identification of truly lethal synthetic lethal therapeutic targets for cancer cells remains uncommon, primarily due to compensatory mechanisms.Methods: In our pursuit of core therapeutic targets (CTTs) that exhibit extensive synthetic lethality in cancer and the corresponding potential drugs, we have developed a machine-learning model that utilizes multiple levels and dimensions of cancer characterization. This is achieved through the consideration of the transcriptional and post-transcriptional regulation of cancer-specific genes and the construction of a model that integrates statistics and machine learning. The model incorporates statistics such as Wilcoxon and Pearson, as well as random forest. Through WGCNA and network analysis, we identify hub genes in the SL network that serve as CTTs. Additionally, we establish regulatory networks for non-coding RNA (ncRNA) and drug-target interactions.Results: Our model has uncovered 7277 potential SL interactions, while WGCNA has identified 13 gene modules. Through network analysis, we have identified 30 CTTs with the highest degree in these modules. Based on these CTTs, we have constructed networks for ncRNA regulation and drug targets. Furthermore, by applying the same process to lung cancer and renal cell carcinoma, we have identified corresponding CTTs and potential therapeutic drugs. We have also analyzed common therapeutic targets among all three cancers.Discussion: The results of our study have broad applicability across various dimensions and histological data, as our model identifies potential therapeutic targets by learning multidimensional complex features from known synthetic lethal gene pairs. The incorporation of statistical screening and network analysis further enhances the confidence in these potential targets. Our approach provides novel theoretical insights and methodological support for the identification of CTTs and drugs in diverse types of cancer