In-silico drug screening and potential target identification for hepatocellular carcinoma using Support Vector Machines based on drug screening result

Application of artificial intelligence in the prediction of immunotherapy efficacy in hepatocellular carcinoma: Current status and prospects

Artificial Intelligence (AI) has increased as a potent tool in medicine, with promising oncology applications. The emergence of immunotherapy has transformed the treatment terrain for hepatocellular carcinoma (HCC), offering new hope to patients with this challenging malignancy. This article examines the role and future of AI in forecasting the effectiveness of immunotherapy in HCC. We highlight the potential of AI to revolutionize the prediction of therapy response, thus improving patient selection and clinical outcomes. The article further outlines the challenges and future research directions in this emerging field.

Distinguishing Rectal Cancer from Colon Cancer Based on the Support Vector Machine Method and RNA-sequencing Data

Colorectal cancer (CRC) is the third most commonly diagnosed cancer worldwide. Several studies have indicated that rectal cancer is significantly different from colon cancer in terms of treatment, prognosis, and metastasis. Recently, the differential mRNA expression of colon cancer and rectal cancer has received a great deal of attention. The current study aimed to identify significant differences between colon cancer and rectal cancer based on RNA sequencing (RNA-seq) data via support vector machines (SVM). Here, 393 CRC samples from the The Cancer Genome Atlas (TCGA) database were investigated, including 298 patients with colon cancer and 95 with rectal cancer. Following the random forest (RF) analysis of the mRNA expression data, 96 genes such as HOXB13, PRAC, and BCLAF1 were identified and utilized to build the SVM classification model with the Leave-One-Out Cross-validation (LOOCV) algorithm. In the training (n=196) and the validation cohorts (n=197), the accuracy (82.1 % and 82.2 %, respectively) and the AUC (0.87 and 0.91, respectively) indicated that the established optimal SVM classification model distinguished colon cancer from rectal cancer reasonably. However, additional experiments are required to validate the predicted gene expression levels and functions.

Machine learning methods in the computational biology of cancer

The objectives of this Perspective paper are to review some recent advances in sparse feature selection for regression and classification, as well as compressed sensing, and to discuss how these might be used to develop tools to advance personalized cancer therapy. As an illustration of the possibilities, a new algorithm for sparse regression is presented and is applied to predict the time to tumour recurrence in ovarian cancer. A new algorithm for sparse feature selection in classification problems is presented, and its validation in endometrial cancer is briefly discussed. Some open problems are also presented.

Using computational strategies to predict potential drugs for nasopharyngeal carcinoma

BACKGROUND

Nasopharyngeal carcinoma (NPC) is a unique cancer. Refinement of current therapy by discovering potential drugs may be approached by several computational strategies.

METHODS

We collected NPC genes from published microarray data and the literature. The NPC disease network was constructed via a protein-protein interaction (PPI) network. The Connectivity Map (CMap) was used to predict potential chemicals, and support vector machines (SVMs) were further utilized to classify the effectiveness of tested drugs against NPC using their gene expression from CMap.

RESULTS

A highly interconnected network was obtained. Several chemically sensitive genes were identified and 87 drugs were predicted with the potential for treating NPC by SVM, in which nearly half of them have anticancer effects according to the literature. The 2 top-ranked drugs, thioridazine and vorinostat, were demonstrated to be effective in inhibiting NPC cells.

CONCLUSION

This in silico approach provides a promising strategy for screening potential therapeutic drugs for NPC treatment.

A preclinical evaluation of antimycin a as a potential antilung cancer stem cell agent

Drug resistance and tumor recurrence are major obstacles in treating lung cancer patients. Accumulating evidence considers lung cancer stem cells (CSCs) as the major contributor to these clinical challenges. Agents that can target lung CSCs could potentially provide a more effective treatment than traditional chemotherapy. Here, we utilized the side-population (SP) method to isolate lung CSCs from A549 and PC-9 cell lines. Subsequently, a high throughput platform, connectivity maps (CMAPs), was used to identify potential anti-CSC agents. An antibiotic, antimycin A (AMA), was identified as a top candidate. SP A549 cells exhibited an elevated stemness profile, including Nanog, β-catenin, Sox2, and CD133, and increased self-renewal ability. AMA treatment was found to suppress β-catenin signaling components and tumor sphere formation. Furthermore, AMA treatment decreased the proliferation of gefitinib-resistant PC-9/GR cells and percentage of SP population. AMA demonstrated synergistic suppression of PC-9/GR cell viability when combined with gefitinib. Finally, AMA treatment suppressed tumorigenesis in mice inoculated with A549 SP cells. Collectively, we have identified AMA using CMAP as a novel antilung CSC agent, which acts to downregulate β-catenin signaling. The combination of AMA and targeted therapeutic agents could be considered for overcoming drug resistance and relapse in lung cancer patients.

Systems virology: host-directed approaches to viral pathogenesis and drug targeting

Systems biology approaches are required to advance our understanding of virus–host interactions, how these interactions cause disease and, ultimately, how to improve diagnostics, therapeutics and vaccines.

Over the past decade, the field of systems virology has evolved from using first-generation microarrays to the integration of multidimensional data sets. This has resulted in significant findings, including the identification of gene expression signatures that are predictive of viral pathogenesis and vaccine efficacy, insights into how viruses disrupt cellular metabolism, and the mapping of virus–host interactomes.

To fulfil its initial promise of revolutionizing our understanding of virus–host interactions, the field of systems virology must move beyond just the listing of molecules that are differentially expressed following viral infection; it must now look to define the relationships between key host molecules and their interactions with viral components.

Several key computational challenges must be addressed in order to move into this new phase of systems virology, including consideration of nonlinear relationships such as the dynamics of the system, the integration of multidimensional data sets and the identification of causal relationships.

Virologists, computer scientists and mathematicians must combine their skills and expertise in applying systems approaches to untangle the complex question of how viruses kill.

Katze and colleagues provide an overview of the evolution of systems virology and the insights obtained from using such methodologies to study virus–host interactions. Combining systems, mathematical and computational approaches with traditional virology research will offer a better understanding of how viruses cause disease and will help in the development of therapeutics.

High-throughput molecular profiling and computational biology are changing the face of virology, providing a new appreciation of the importance of the host in viral pathogenesis and offering unprecedented opportunities for better diagnostics, therapeutics and vaccines. Here, we provide a snapshot of the evolution of systems virology, from global gene expression profiling and signatures of disease outcome, to geometry-based computational methods that promise to yield novel therapeutic targets, personalized medicine and a deeper understanding of how viruses cause disease. To realize these goals, pipettes and Petri dishes need to join forces with the powers of mathematics and computational biology.