The protein-protein interaction network of intestinal gastric cancer patients reveals hub proteins with potential prognostic value

Bioinformatics Analysis and Validation of Potential Markers Associated with Prediction and Prognosis of Gastric Cancer

Gastric cancer (GC) is one of the most common cancers worldwide. Most patients are diagnosed at the progressive stage of the disease, and current anticancer drug advancements are still lacking. Therefore, it is crucial to find relevant biomarkers with the accurate prediction of prognoses and good predictive accuracy to select appropriate patients with GC. Recent advances in molecular profiling technologies, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics, have enabled the approach of GC biology at multiple levels of omics interaction networks. Systemic biological analyses, such as computational inference of "big data" and advanced bioinformatic approaches, are emerging to identify the key molecular biomarkers of GC, which would benefit targeted therapies. This review summarizes the current status of how bioinformatics analysis contributes to biomarker discovery for prognosis and prediction of therapeutic efficacy in GC based on a search of the medical literature. We highlight emerging individual multi-omics datasets, such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics, for validating putative markers. Finally, we discuss the current challenges and future perspectives to integrate multi-omics analysis for improving biomarker implementation. The practical integration of bioinformatics analysis and multi-omics datasets under complementary computational analysis is having a great impact on the search for predictive and prognostic biomarkers and may lead to an important revolution in treatment.

RPI-EDLCN: An Ensemble Deep Learning Framework Based on Capsule Network for ncRNA-Protein Interaction Prediction

Noncoding RNAs (ncRNAs) play crucial roles in many cellular life activities by interacting with proteins. Identification of ncRNA-protein interactions (ncRPIs) is key to understanding the function of ncRNAs. Although a number of computational methods for predicting ncRPIs have been developed, the problem of predicting ncRPIs remains challenging. It has always been the focus of ncRPIs research to select suitable feature extraction methods and develop a deep learning architecture with better recognition performance. In this work, we proposed an ensemble deep learning framework, RPI-EDLCN, based on a capsule network (CapsuleNet) to predict ncRPIs. In terms of feature input, we extracted the sequence features, secondary structure sequence features, motif information, and physicochemical properties of ncRNA/protein. The sequence and secondary structure sequence features of ncRNA/protein are encoded by the conjoint k-mer method and then input into an ensemble deep learning model based on CapsuleNet by combining the motif information and physicochemical properties. In this model, the encoding features are processed by convolution neural network (CNN), deep neural network (DNN), and stacked autoencoder (SAE). Then the advanced features obtained from the processing are input into the CapsuleNet for further feature learning. Compared with other state-of-the-art methods under 5-fold cross-validation, the performance of RPI-EDLCN is the best, and the accuracy of RPI-EDLCN on RPI1807, RPI2241, and NPInter v2.0 data sets was 93.8%, 88.2%, and 91.9%, respectively. The results of the independent test indicated that RPI-EDLCN can effectively predict potential ncRPIs in different organisms. In addition, RPI-EDLCN successfully predicted hub ncRNAs and proteins in Mus musculus ncRNA-protein networks. Overall, our model can be used as an effective tool to predict ncRPIs and provides some useful guidance for future biological studies.

Integrated Network Analysis of microRNAs, mRNAs, and Proteins Reveals the Regulatory Interaction between hsa-mir-200b and CFL2 Associated with Advanced Stage and Poor Prognosis in Patients with Intestinal Gastric Cancer

Intestinal gastric cancer (IGC) carcinogenesis results from a complex interplay between environmental and molecular factors, ultimately contributing to disease development. We used integrative bioinformatic analysis to investigate IGC high-throughput molecular data to uncover interactions among differentially expressed genes, microRNAs, and proteins and their roles in IGC. An integrated network was generated based on experimentally validated microRNA-gene/protein interaction data, with three regulatory circuits involved in a complex network contributing to IGC progression. Key regulators were determined, including 23 microRNA and 15 gene/protein hubs. The regulatory circuit networks were associated with hallmarks of cancer, e.g., cell death, apoptosis and the cell cycle, the immune response, and epithelial-to-mesenchymal transition, indicating that different mechanisms of gene regulation impact similar biological functions. Altered expression of hubs was related to the clinicopathological characteristics of IGC patients and showed good performance in discriminating tumors from adjacent nontumor tissues and in relation to T stage and overall survival (OS). Interestingly, expression of upregulated hub hsa-mir-200b and its downregulated target hub gene/protein CFL2 were related not only to pathological T staging and OS but also to changes during IGC carcinogenesis. Our study suggests that regulation of CFL2 by hsa-miR-200b is a dynamic process during tumor progression and that this control plays essential roles in IGC development. Overall, the results indicate that this regulatory interaction is an important component in IGC pathogenesis. Also, we identified a novel molecular interplay between microRNAs, proteins, and genes associated with IGC in a complex biological network and the hubs closely related to IGC carcinogenesis as potential biomarkers.

Novel mutations in a second primary gastric cancer in a patient treated for primary colon cancer

A 60-year-old man presented with complaints of abdominal pain and melena. Patient had a history of colon cancer 16 years back and had undergone right hemi colectomy for microsatellite instability (MSI) negative, mismatch repair (MMR) stable, T2N0 disease with no mutations on next-generation sequencing (NGS). Investigations revealed a second primary in stomach (intestinal type of adenocarcinoma) with no recurrent lesions in colon or distant metastasis. He was started on CapOx with Bevacizumab and developed gastric outlet obstruction. Total gastrectomy with D2 lymphadenectomy and Roux-en-Y oesophageao-jejunal pouch anastomosis was done. The histopathology showed intestinal type of adenocarcinoma with pT3N2 disease. NGS showed 3 novel mutations in KMT2A, LTK, and MST1R gene. The pathway enrichment analysis and Gene Ontology were carried out, followed by the construction of protein-protein interaction network to discover associations among the genes. The results suggested that these mutations have not been reported in gastric cancer earlier and despite not having a direct pathway of carcinogenesis they probably act through modulation of host of miRNA's. Further studies are needed to investigate the role of KMT2A, LTK, and MST1R gene in gastric carcinogenesis.

LPCAT1 enhances the invasion and migration in gastric cancer: Based on computational biology methods and in vitro experiments

BACKGROUND AND AIM

The biological functions and clinical implications of lysophosphatidylcholine acyltransferase 1 (LPCAT1) remain unclarified in gastric cancer (GC). The aim of the current study was to explore the possible clinicopathological significance of LPCAT1 and its perspective mechanism in GC tissues.

MATERIALS AND METHODS

The protein expression and mRNA levels of LPCAT1 were detected from in-house immunohistochemistry and public high-throughput RNA arrays and RNA sequencing. To have a comprehensive understanding of the clinical value of LPCAT1 in GC, all enrolled data were integrated to calculate the expression difference and standard mean difference (SMD). The biological mechanism of LPCAT1 in GC was confirmed by computational biology and in vitro experiments. Migration and invasion assays were also conducted to confirm the effect of LPCAT1 in GC.

RESULTS

Both protein and mRNA expression levels of LPCAT1 in GC were remarkably higher than those in noncancerous controls. Comprehensively, the SMD of LPCAT1 mRNA was 1.11 (95% CI = 0.86-1.36) in GC, and the summarized AUC was 0.85 based on 15 datasets containing 1727 cases of GC and 940 cases of non-GC controls. Moreover, LPCAT1 could accelerate the invasion and migration of GC by boosting the neutrophil degranulation pathway and disturbing the immune microenvironment.

CONCLUSION

An increased level of LPCAT1 may promote the progression of GC.

Iroquois Family Genes in Gastric Carcinogenesis: A Comprehensive Review

Gastric cancer (GC) is the fifth leading cause of cancer-associated death worldwide, accounting for 768,793 related deaths and 1,089,103 new cases in 2020. Despite diagnostic advances, GC is often detected in late stages. Through a systematic literature search, this study focuses on the associations between the Iroquois gene family and GC. Accumulating evidence indicates that Iroquois genes are involved in the regulation of various physiological and pathological processes, including cancer. To date, information about Iroquois genes in GC is very limited. In recent years, the expression and function of Iroquois genes examined in different models have suggested that they play important roles in cell and cancer biology, since they were identified to be related to important signaling pathways, such as wingless, hedgehog, mitogen-activated proteins, fibroblast growth factor, TGFβ, and the PI3K/Akt and NF-kB pathways. In cancer, depending on the tumor, Iroquois genes can act as oncogenes or tumor suppressor genes. However, in GC, they seem to mostly act as tumor suppressor genes and can be regulated by several mechanisms, including methylation, microRNAs and important GC-related pathogens. In this review, we provide an up-to-date review of the current knowledge regarding Iroquois family genes in GC.