Cancer is one of the leading causes of mortality worldwide, highlighting the urgent need for a deeper molecular understanding of the disease's heterogeneity and the development of personalized treatments. Since its establishment in 2017, the Human Pathology Atlas has been instrumental in linking gene expression profiling with patient survival outcomes, providing system-level insights and experimental validation across a wide range of cancer research. In this updated analysis, we analysed the expression profiles of 6,918 patients across 21 cancer types using the latest gene annotations. Our refined approach enabled us to offer an updated list of prognostic genes for human cancers, with a focus on hepatocellular, renal and colorectal cancers. To strengthen the reliability of our findings, we integrated data from 10 independent cancer cohorts, creating a cross-validated, reliable collection of prognostic genes. By applying a systems biology approach, we identified that patient survival outcomes in kidney renal clear cell carcinoma (KIRC) and liver hepatocellular carcinoma (LIHC) are strongly associated with gene expression profiles. We also developed a prognostic regulatory network specifically for KIRC and LIHC to enhance the utility of the Human Pathology Atlas for cancer research. The updated version of the Human Pathology Atlas lays the foundation for precision oncology and the development of personalized treatment strategies.

Simple Summary Hepatocellular carcinoma (HCC) is the most common malignancy of liver cancer. However, treatment of HCC is still severely limited due to limitation of drug therapy. We aimed to screen more possible target genes and candidate drugs for HCC, exploring the possibility of drug treatments from systems biological perspective. We identified ten candidate target genes, which are hub genes in HCC co-expression networks, which also possess significant prognostic value in two independent HCC cohorts. The rationality of these target genes was well demonstrated through variety analyses of patient expression profiles. We then screened candidate drugs for target genes and finally identified withaferin-a and mitoxantrone as the candidate drug for HCC treatment. The drug effectiveness was validated in in vitro model and computational analysis, providing more evidence for our drug repositioning method and results. Hepatocellular carcinoma (HCC) is a malignant liver cancer that continues to increase deaths worldwide owing to limited therapies and treatments. Computational drug repurposing is a promising strategy to discover potential indications of existing drugs. In this study, we present a systematic drug repositioning method based on comprehensive integration of molecular signatures in liver cancer tissue and cell lines. First, we identify robust prognostic genes and two gene co-expression modules enriched in unfavorable prognostic genes based on two independent HCC cohorts, which showed great consistency in functional and network topology. Then, we screen 10 genes as potential target genes for HCC on the bias of network topology analysis in these two modules. Further, we perform a drug repositioning method by integrating the shRNA and drug perturbation of liver cancer cell lines and identifying potential drugs for every target gene. Finally, we evaluate the effects of the candidate drugs through an in vitro model and observe that two identified drugs inhibited the protein levels of their corresponding target genes and cell migration, also showing great binding affinity in protein docking analysis. Our study demonstrates the usefulness and efficiency of network-based drug repositioning approach to discover potential drugs for cancer treatment and precision medicine approach.

Background

Pancreatic ductal adenocarcinoma (PDAC) is the most common form of pancreatic cancer, known for its significant resistance to chemotherapy. Leveraging computational drug repurposing approach emerges as a valuable strategy for identifying novel therapeutic uses for current drug agents.

Methods

In this study, we developed a systematic drug repositioning methodology that integrates molecular signatures from both pancreatic cancer tissues and celllines. Key prognostic genes and gene co-expression modules that associated with poor prognosis were identified in two distinct PDAC cohorts. We further discovered four promising genes that could be targeted for PDAC treatment based on network topology analysis of these crucial modules. Subsequently, we developed a drug repositioning framework that correlates gene knockout effects with drug-induced alterations in pancreatic cancer cell lines, facilitating the identification of potential drugs for each targeted gene. The efficacy of these candidates was subsequently evaluated through in vitro experiments.

Result

The study identified key prognostic genes and gene co-expression modules that correlated with poor prognosis in two independent PDAC cohorts. Employing our systematic drug repositioning approach, we identified AVL-292 and MK-5108 as promising candidates for PDAC treatment. Additional experimental validation confirmed that these compounds significantly inhibited cell motility, suggesting potential therapeutic benefits.

Conclusion

Our study demonstrates the feasibility and efficacy of a network-based drug repositioning approach in identifying potential drugs for the treatment of PDAC.

Non-alcoholic fatty liver disease (NAFLD) is a leading cause of chronic liver disease worldwide. We performed network analysis to investigate the dysregulated biological processes in the disease progression and revealed the molecular mechanism underlying NAFLD. Based on network analysis, we identified a highly conserved disease-associated gene module across three different NAFLD cohorts and highlighted the predominant role of key transcriptional regulators associated with lipid and cholesterol metabolism. In addition, we revealed the detailed metabolic differences between heterogeneous NAFLD patients through integrative systems analysis of transcriptomic data and liver-specific genomescale metabolic model. Furthermore, we identified transcription factors (TFs), including SREBF2, HNF4A, SREBF1, YY1, and KLF13, showing regulation of hepatic expression of genes in the NAFLD-associated modules and validated the TFs using data generated from a mouse NAFLD model. In conclusion, our integrative analysis facilitates the understanding of the regulatory mechanism of these perturbed TFs and their associated biological processes.

The pyruvate kinase enzyme is the key regulator in hepatic glucose metabolism. Detailed in sights into its coresponding gene PKLR, were brought up regarding to it’s biological functions in liver tissue. Systems biology-based approaches, including co-expression networks and genome-wide association study, provide solid supports to identify PKLR as a key gene influencing the liver metabolism. Here, we review studies in the process of systematically identification of PKLR through the integretation of the multi-omics data for metabolic dysfunction-associated fatty liver disease. We also summarize the results of these studies, and the effect of PKLR inhibition on both in vovo and in vitro studies. Finally, we summaries the therapeutic exploration related to PKLR inhibition for the treatment of NAFLD, highlighting the promising future of PKLR in a broader disease.