Prognostic and predictive value of a five-molecule panel in resected pancreatic ductal adenocarcinoma: A multicentre study

Network-Based Methods and Their Applications in Drug Discovery

Drug discovery is time-consuming, expensive, and predominantly follows the "one drug → one target → one disease" paradigm. With the rapid development of systems biology and network pharmacology, a novel drug discovery paradigm, "multidrug → multitarget → multidisease", has emerged. This new holistic paradigm of drug discovery aligns well with the essence of networks, leading to the emergence of network-based methods in the field of drug discovery. In this Perspective, we initially introduce the concept and data sources of networks and highlight classical methodologies employed in network-based methods. Subsequently, we focus on the practical applications of network-based methods across various areas of drug discovery, such as target prediction, virtual screening, prediction of drug therapeutic effects or adverse drug events, and elucidation of molecular mechanisms. In addition, we provide representative web servers for researchers to use network-based methods in specific applications. Finally, we discuss several challenges of network-based methods and the directions for future development. In a word, network-based methods could serve as powerful tools to accelerate drug discovery.

Network pharmacology: towards the artificial intelligence-based precision traditional Chinese medicine

Network pharmacology (NP) provides a new methodological perspective for understanding traditional medicine from a holistic perspective, giving rise to frontiers such as traditional Chinese medicine network pharmacology (TCM-NP). With the development of artificial intelligence (AI) technology, it is key for NP to develop network-based AI methods to reveal the treatment mechanism of complex diseases from massive omics data. In this review, focusing on the TCM-NP, we summarize involved AI methods into three categories: network relationship mining, network target positioning and network target navigating, and present the typical application of TCM-NP in uncovering biological basis and clinical value of Cold/Hot syndromes. Collectively, our review provides researchers with an innovative overview of the methodological progress of NP and its application in TCM from the AI perspective.

Chemotherapy suppresses SHH gene expression via a specific enhancer

Sonic hedgehog (SHH) signaling is a key regulator of embryonic development and tissue homeostasis that is involved in gastrointestinal (GI) cancer progression. Regulation of SHH gene expression is a paradigm of long-range enhancer function. Using the classical chemotherapy drug 5-fluorouracil (5FU) as an example, here we show that SHH gene expression is suppressed by chemotherapy. SHH is downstream of immediate early genes (IEGs), including Early growth response 1 (Egr1). A specific 139 kb upstream enhancer is responsible for its down-regulation. Knocking down EGR1 expression or blocking its binding to this enhancer renders SHH unresponsive to chemotherapy. We further demonstrate that down-regulation of SHH expression does not depend on 5FU's impact on nucleotide metabolism or DNA damage; rather, a sustained oxidative stress response mediates this rapid suppression. This enhancer is present in a wide range of tumors and normal tissues, thus providing a target for cancer chemotherapy and its adverse effects on normal tissues. We propose that SHH is a stress-responsive gene downstream of IEGs, and that traditional chemotherapy targets a specific enhancer to suppress its expression.

Construction of a hypoxia-derived gene model to predict the prognosis and therapeutic response of head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC) ranks as the sixth most common cancer worldwide and has a poor prognosis in the advanced stage. Increasing evidence has shown that hypoxia contributes to genetic alterations that have essential effects on the occurrence and progression of cancers. However, the exact roles hypoxia-related genes play in HNSCC remain unclear. In this study, we downloaded the mRNA expression profiles and clinical data of patients with HNSCC from The Cancer Genome Atlas and Gene Expression Omnibus. Two molecular subtypes were identified based on prognostic hypoxia-related genes using the ConsensusClusterPlus method. ESTIMATE was used to calculate the immune score of each patient. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology were used for functional annotation. A prognostic risk model was generated by Cox regression and least absolute shrinkage and selection operator analysis. We identified two distinct molecular subtypes, cluster 1 and cluster 2, based on 200 hypoxia-related genes. Additionally, we identified three hypoxia-immune subgroups (hypoxia-high/immune-low, hypoxia-low/immune-high, and mixed subgroups). The hypoxia-high/immune-low group had the worst prognosis, while the hypoxia-low/immune-high group had the best prognosis. Patients in the hypoxia-low/immune-high group were more sensitive to anti-PD-L1 treatment and chemotherapy than those in the hypoxia-high/immune-low group. Furthermore, we constructed a prognostic risk model based on the differentially expressed genes between the hypoxia-immune subgroups. The survival analysis and time-dependent ROC analysis results demonstrated the good performance of the established 7-gene signature for predicting HNSCC prognosis. In conclusions, the constructed hypoxia-related model might serve as a promising biomarker for the diagnosis and prognosis of HNSCC, and it could predict immunotherapy and chemotherapy efficacy in HNSCC.

Overexpression of OAS1 Is Correlated With Poor Prognosis in Pancreatic Cancer

Background

OAS1 expression in pancreatic cancer has been confirmed by many studies. However, the prognostic value and mechanism of OAS1 in pancreatic cancer have not been analyzed.

Methods

The RNA-seq in pancreatic cancer were obtained by UCSC XENA and GEO database. In addition, immunohistochemical validation and analysis were performed using samples from the 900th hospital. The prognosis of OAS1 was evaluated by timeROC package, Cox regression analysis, and Kaplan-Meier survival curves. Then, the main functional and biological signaling pathways enrichment and its relationship with the abundance of immune cells were analyzed by bioinformatics.

Results

OAS1 was highly expressed in pancreatic cancer compared with normal pancreatic tissue. High OAS1 expression was associated with poor overall survival (p<0.05). The OAS1 was significantly correlated to TNM staging (p=0.014). The timeROC analysis showed that the AUC of OAS1 was 0.734 for 3-year OS. In addition, the expression of OAS1 was significantly correlated with the abundance of a variety of immune markers. GSEA showed that enhanced signaling pathways associated with OAS1 include Apoptosis, Notch signaling pathway, and P53 signaling pathway.

Conclusions

OAS1 is a valuable prognostic factor in pancreatic cancer. Moreover, it may be a potential immunotherapeutic target.

Identification and Validation of Prognostic Model for Pancreatic Ductal Adenocarcinoma Based on Necroptosis-Related Genes

Background: Pancreatic ductal adenocarcinoma (PDAC) is one of the most malignant tumors with a poor prognosis. Recently, necroptosis has been reported to participate in the progression of multiple tumors. However, few studies have revealed the relationship between necroptosis and PDAC, and the role of necroptosis in PDAC has not yet been clarified.

Methods: The mRNA expression data and corresponding clinical information of PDAC patients were downloaded from the TCGA and GEO databases. The necroptosis-related genes (NRGs) were obtained from the CUSABIO website. Consensus clustering was performed to divide PDAC patients into two clusters. Univariate and LASSO Cox regression analyses were applied to screen the NRGs related to prognosis to construct the prognostic model. The predictive value of the prognostic model was evaluated by Kaplan-Meier survival analysis and ROC curve. Univariate and multivariate Cox regression analyses were used to evaluate whether the risk score could be used as an independent predictor of PDAC prognosis. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and single-sample gene set enrichment analysis (ssGSEA) were used for functional enrichment analysis. Finally, using qRT-PCR examined NRGs mRNA expression in vitro.

Results: Based on the TCGA database, a total of 22 differential expressed NRGs were identified, among which eight NRGs (CAPN2, CHMP4C, PLA2G4F, PYGB, BCL2, JAK3, PLA2G4C and STAT4) that may be related to prognosis were screened by univariate Cox regression analysis. And CAPN2, CHMP4C, PLA2G4C and STAT4 were further selected to construct the prognostic model. Kaplan-Meier survival analysis and ROC curve showed that there was a significant correlation between the risk model and prognosis. Univariate and multivariate Cox regression analyses showed that the risk score of the prognostic model could be used as an independent predictor. The model efficacy was further demonstrated in the GEO cohort. Functional analysis revealed that there were significant differences in immune status between high and low-risk groups. Finally, the qRT-PCR results revealed a similar dysregulation of NRGs in PDAC cell lines.

Conclusion: This study successfully constructed and verified a prognostic model based on NRGs, which has a good predictive value for the prognosis of PDAC patients.

Decoding multilevel relationships with the human tissue-cell-molecule network

Understanding the biological functions of molecules in specific human tissues or cell types is crucial for gaining insights into human physiology and disease. To address this issue, it is essential to systematically uncover associations among multilevel elements consisting of disease phenotypes, tissues, cell types and molecules, which could pose a challenge because of their heterogeneity and incompleteness. To address this challenge, we describe a new methodological framework, called Graph Local InfoMax (GLIM), based on a human multilevel network (HMLN) that we established by introducing multiple tissues and cell types on top of molecular networks. GLIM can systematically mine the potential relationships between multilevel elements by embedding the features of the HMLN through contrastive learning. Our simulation results demonstrated that GLIM consistently outperforms other state-of-the-art algorithms in disease gene prediction. Moreover, GLIM was also successfully used to infer cell markers and rewire intercellular and molecular interactions in the context of specific tissues or diseases. As a typical case, the tissue-cell-molecule network underlying gastritis and gastric cancer was first uncovered by GLIM, providing systematic insights into the mechanism underlying the occurrence and development of gastric cancer. Overall, our constructed methodological framework has the potential to systematically uncover complex disease mechanisms and mine high-quality relationships among phenotypical, tissue, cellular and molecular elements.

Identifying Outcomes of Patients With Advanced Pancreatic Adenocarcinoma and RECIST Stable Disease Using Radiomics Analysis

Few studies have explored the biomarkers for predicting the heterogeneous outcomes of patients with advanced pancreatic adenocarcinoma showing stable disease (SD) on the initial postchemotherapy computed tomography. We aimed to devise a radiomics signature (RS) to predict these outcomes for further risk stratification.

The enhanced cell cycle related to the response to adjuvant therapy in pancreatic ductal adenocarcinoma

A major clinical challenge for treating patients with pancreatic ductal adenocarcinoma (PDAC) is identifying those that may benefit from adjuvant chemotherapy versus those that will not. Thus, there is a need for a robust and convenient biomarker for predicting chemotherapy response in PDAC patients. In this study, network inference was conducted by integrating the differentially expressed cell cycle signatures and target genes between the basal-like subtype and classical subtype of PDAC. As a result from this statistical analysis, two dominant cell cycle genes, RASAL2 and ASPM, were identified. Based on the expression levels of these two genes, we constructed a "Enhanced Cell Cycle" scoring system (ECC score). Patients were given an ECC score, and respectively divided into ECC-high and ECC-low groups. Survival, pathway enrichment, immune environment characteristics, and chemotherapy response analysis' were performed between the two groups in a total of 891 patients across 5 cohorts. ECC-high patients exhibited shortened recurrence-free survival (RFS) and overall survival (OS) rates. In addition, it was found that adjuvant chemotherapy could significantly improve the outcome of the ECC-high patients while ECC-low patients did not benefit from adjuvant chemotherapy. It was also found that there was less CD8+ T cell, natural killer (NK) cell, M1 macrophage, and plasma cell infiltration in ECC-high patients when compared to ECC-low patients. Also, the expression of CD73, an immune suppressor gene, and it's related hypoxia pathway were elevated in the ECC-high group when compared to the ECC-low group. In conclusion, this study showed that patients characterized as ECC-high not only had reduced RFS and OS rates, but were also more sensitive to adjuvant chemotherapy and could potentially be less sensitive to immune checkpoint inhibitors. Being able to characterize patients by these parameters would allow doctors to make more informed decisions on patient treatment regimens.

TCM network pharmacology: A new trend towards combining computational, experimental and clinical approaches

Traditional Chinese medicine (TCM) is a precious treasure of the Chinese nation and has unique advantages in the prevention and treatment of diseases. The holistic view of TCM coincides with the new generation of medical research paradigm characterized by network and system. TCM gave birth to a new method featuring holistic and systematic "network target", a core theory and method of network pharmacology. TCM is also an important research object of network pharmacology. TCM network pharmacology, which aims to understand the network-based biological basis of complex diseases, TCM syndromes and herb treatments, plays a critical role in the origin and development process of network pharmacology. This review introduces new progresses of TCM network pharmacology in recent years, including predicting herb targets, understanding biological foundation of diseases and syndromes, network regulation mechanisms of herbal formulae, and identifying disease and syndrome biomarkers based on biological network. These studies show a trend of combining computational, experimental and clinical approaches, which is a promising direction of TCM network pharmacology research in the future. Considering that TCM network pharmacology is still a young research field, it is necessary to further standardize the research process and evaluation indicators to promote its healthy development.

Network Pharmacology to Uncover the Biological Basis of Spleen Qi Deficiency Syndrome and Herbal Treatment

Spleen qi deficiency (SQD) syndrome is one of the basic traditional Chinese medicine (TCM) syndromes related to various diseases including chronic inflammation and hypertension and guides the use of many herbal formulae. However, the biological basis of SQD syndrome has not been clearly elucidated due to the lack of appropriate methodologies. Here, we propose a network pharmacology strategy integrating computational, clinical, and experimental investigation to study the biological basis of SQD syndrome. From computational aspects, we used a powerful disease gene prediction algorithm to predict the SQD syndrome biomolecular network which is significantly enriched in biological functions including immune regulation, oxidative stress, and lipid metabolism. From clinical aspects, SQD syndrome is involved in both the local and holistic disorders, that is, the digestive diseases and the whole body's dysfunctions. We, respectively, investigate SQD syndrome-related digestive diseases including chronic gastritis and irritable bowel syndrome and the whole body's dysfunctions such as chronic fatigue syndrome and hypertension. We found innate immune and oxidative stress modules of SQD syndrome biomolecular network dysfunction in chronic gastritis patients and irritable bowel syndrome patients. Lymphocyte modules were downregulated in chronic fatigue syndrome patients and hypertension patients. From experimental aspects, network pharmacology analysis suggested that targets of Radix Astragali and other four herbs commonly used for SQD syndrome are significantly enriched in the SQD syndrome biomolecular network. Experiments further validated that Radix Astragali ingredients promoted immune modules such as macrophage proliferation and lymphocyte proliferation. These findings indicate that the biological basis of SQD syndrome is closely related to insufficient immune response including decreased macrophage activity and reduced lymphocyte proliferation. This study not only demonstrates the potential biological basis of SQD syndrome but also provides a novel strategy for exploring relevant molecular mechanisms of disease-syndrome-herb from the network pharmacology perspective.