Expression and clinical significance of SYNE1 and MAGI2 gene promoter methylation in gastric cancer

DUSP6 protein action and related hub genes prevention of sepsis-induced lung injury were screened by WGCNA and Venn

During a sepsis infection, the lung is extremely susceptible to damage. A condition known as acute respiratory distress syndrome (ARDS) may develop in extreme circumstances. The primary objective of this research is to identify important genes that are related with both sepsis and lung injury. These genes have the potential to act as novel biomarkers in the investigation of sepsis-induced lung injury prevention strategies. It was possible to download from GEO data both the sepsis-related dataset (GSE64457) and the lung injury-related dataset (GSE40839). In the GSE64457 dataset, using the "limma" package in R revealed 429 differentially expressed genes (DEGs) with logFC values more than or equal to -1 and p values <0.05. There were 266 genes that were up-regulated and 163 genes that were down-regulated. Through the use of Gene Ontology (GO), it was discovered that the majority of the DEGs were associated with the inflammatory response (BP terms), a particular granule lumen (CC terms), and protein binding (MF terms). By doing a pathway enrichment analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG), researchers were able to identify DEGs that were mostly associated with the NOD-like receptor signalling pathway, the TNF signalling pathway, and Epstein-Barr virus infection. Within the GSE40839 dataset, Weighted Gene Co-Expression Network Analysis (WGCNA) yielded a total of 7 modules, from which it was possible to screen out 2 critical modules and 693 key genes. The important genes and DEGs were both subjected to a Venn analysis. Finally, 14 genes that overlapped (ARL4A, LAIR1, MTHFD2, TSPAN13, DUSP6, PECR, CBS, TES, ASNS, SYNE1, FGF13, LCN2, KLF10, BCAT1) were closely associated to the incidence and development of sepsis-induced lung injury. This indicates that these genes are the essential genes to avoid the occurrence of sepsis-induced lung injury. This study provides novel strategies for preventing lung harm brought on by sepsis.

Effects of DNA methylation and its application in inflammatory bowel disease (Review)

Inflammatory bowel disease (IBD) is marked by persistent inflammation, and its development and progression are linked to environmental, genetic, immune system and gut microbial factors. DNA methylation (DNAm), as one of the protein modifications, is a crucial epigenetic process used by cells to control gene transcription. DNAm is one of the most common areas that has drawn increasing attention recently, with studies revealing that the interleukin (IL)‑23/IL‑12, wingless‑related integration site, IL‑6‑associated signal transducer and activator of transcription 3, suppressor of cytokine signaling 3 and apoptosis signaling pathways are involved in DNAm and in the pathogenesis of IBD. It has emerged that DNAm‑associated genes are involved in perpetuating the persistent inflammation that characterizes a number of diseases, including IBD, providing a novel therapeutic strategy for exploring their treatment. The present review discusses DNAm‑associated genes in the pathogenesis of IBD and summarizes their application as possible diagnostic, prognostic and therapeutic biomarkers in IBD. This may provide a reference for the particular form of IBD and its related methylation genes, aiding in clinical decision‑making and encouraging therapeutic alternatives.

Nesprin1 Deficiency Is Associated with Poor Prognosis of Renal Cell Carcinoma and Resistance to Sunitinib Treatment

INTRODUCTION

Nuclear envelope spectrin repeat protein (Nesprin) 1 encoded by SYNE1, crucially regulates the morphology and functions of the cell. Mutations in the SYNE1 gene are associated with various diseases; however, their significance in renal cell carcinoma (RCC) remains unknown. In this study, we have investigated the association of SYNE1/Nesprin1 with the progression and prognosis of clear cell RCC (ccRCC).

METHODS

In silico analyses of publicly available datasets of patients with RCC were performed. Based on the cohort data, Nesprin1 expression in nephrectomized tissue samples acquired from patients with ccRCC was analyzed using immunohistochemical staining. The invasion, migration, and proliferation of the SYNE1-knockdown human RCC cell lines were analyzed in vitro; moreover, RNA sequencing and gene set enrichment analysis were conducted to study the molecular mechanism underlying the association of SYNE1/Nesprin1 with prognosis of RCC.

RESULTS

Patients with RCC-associated SYNE1 gene mutations exhibited significantly worse overall and progression-free survivals. Patients with Nesprin1-negative ccRCC tumors exhibit significantly poorer overall, cancer-specific, and recurrence-free survival rates than those recorded in the Nesprin1-positive group. SYNE1 knockdown enhanced the invasion and migration of RCC cells; however, it did not influence the proliferation of cells. RNA sequencing and gene set enrichment analysis revealed that SYNE1 knockdown significantly altered the expression of genes associated with oxidative phosphorylation. Consistently, patients with RCC exhibiting low SYNE1 expression, who were treated with the vascular endothelial growth factor receptor inhibitor sunitinib, had worse progression-free survival.

CONCLUSIONS

The results indicate that the expression of SYNE1/Nesprin1 and SYNE1 mutations in patients with RCC are closely linked to their prognosis and responsiveness to sunitinib treatment.

Data mining identifies novel RNA-binding proteins involved in colon and rectal carcinomas

Colorectal adenocarcinoma (COREAD) is the second most deadly cancer and third most frequently encountered malignancy worldwide. Despite efforts in molecular subtyping and subsequent personalized COREAD treatments, multidisciplinary evidence suggests separating COREAD into colon cancer (COAD) and rectal cancer (READ). This new perspective could improve diagnosis and treatment of both carcinomas. RNA-binding proteins (RBPs), as critical regulators of every hallmark of cancer, could fulfill the need to identify sensitive biomarkers for COAD and READ separately. To detect new RBPs involved in COAD and READ progression, here we used a multidata integration strategy to prioritize tumorigenic RBPs. We analyzed and integrated 1) RBPs genomic and transcriptomic alterations from 488 COAD and 155 READ patients, 2) ∼ 10,000 raw associations between RBPs and cancer genes, 3) ∼ 15,000 immunostainings, and 4) loss-of-function screens performed in 102 COREAD cell lines. Thus, we unraveled new putative roles of NOP56, RBM12, NAT10, FKBP1A, EMG1, and CSE1L in COAD and READ progression. Interestingly, FKBP1A and EMG1 have never been related with any of these carcinomas but presented tumorigenic features in other cancer types. Subsequent survival analyses highlighted the clinical relevance of FKBP1A, NOP56, and NAT10 mRNA expression to predict poor prognosis in COREAD and COAD patients. Further research should be performed to validate their clinical potential and to elucidate their molecular mechanisms underlying these malignancies.

Colon cancer transcriptome

Over the last four decades, methodological innovations have continuously changed transcriptome profiling. It is now feasible to sequence and quantify the transcriptional outputs of individual cells or thousands of samples using RNA sequencing (RNA-seq). These transcriptomes serve as a connection between cellular behaviors and their underlying molecular mechanisms, such as mutations. This relationship, in the context of cancer, provides a chance to unravel tumor complexity and heterogeneity and uncover novel biomarkers or treatment options. Since colon cancer is one of the most frequent malignancies, its prognosis and diagnosis seem to be critical. The transcriptome technology is developing for an earlier and more accurate diagnosis of cancer which can provide better protectivity and prognostic utility to medical teams and patients. A transcriptome is a whole set of expressed coding and non-coding RNAs in an individual or cell population. The cancer transcriptome includes RNA-based changes. The combined genome and transcriptome of a patient may provide a comprehensive picture of their cancer, and this information is beginning to affect treatment decision-making in real-time. A full assessment of the transcriptome of colon (colorectal) cancer has been assessed in this review paper based on risk factors such as age, obesity, gender, alcohol use, race, and also different stages of cancer, as well as non-coding RNAs like circRNAs, miRNAs, lncRNAs, and siRNAs. Similarly, they have been examined independently in the transcriptome study of colon cancer.

Computational Tactics for Precision Cancer Network Biology

Network biology has garnered tremendous attention in understanding complex systems of cancer, because the mechanisms underlying cancer involve the perturbations in the specific function of molecular networks, rather than a disorder of a single gene. In this article, we review the various computational tactics for gene regulatory network analysis, focused especially on personalized anti-cancer therapy. This paper covers three major topics: (1) cell line's (or patient's) cancer characteristics specific gene regulatory network estimation, which enables us to reveal molecular interplays under varying conditions of cancer characteristics of cell lines (or patient); (2) computational approaches to interpret the multitudinous and massive networks; (3) network-based application to uncover molecular mechanisms of cancer and related marker identification. We expect that this review will help readers understand personalized computational network biology that plays a significant role in precision cancer medicine.

iCancer-Pred: A tool for identifying cancer and its type using DNA methylation

DNA methylation is an important epigenetics, which occurs in the early stages of tumor formation. And it also is of great significance to find the relationship between DNA methylation and cancer. This paper proposes a novel model, iCancer-Pred, to identify cancer and classify its types further. The datasets of DNA methylation information of 7 cancer types have been collected from The Cancer Genome Atlas (TCGA). The coefficient of variation firstly is used to reduce the number of features, and then the elastic network is applied to select important features. Finally, a fully connected neural network is constructed with these selected features. In predicting seven types of cancers, iCancer-Pred has achieved an overall accuracy of over 97% accuracy with 5-fold cross-validation. For the convenience of the application, a user-friendly web server: http://bioinfo.jcu.edu.cn/cancer or http://121.36.221.79/cancer/ is available. And the source codes are freely available for download at https://github.com/Huerhu/iCancer-Pred.

Association of FLG mutation with tumor mutation load and clinical outcomes in patients with gastric cancer

Background: Stomach adenocarcinoma (STAD) is one of the most frequently diagnosed cancers in the world with a poor prognosis due to genetic heterogeneity. The present study aimed to explore potential prognostic predictors and therapeutic targets that can be used for STAD treatment.

Methods: We collected relevant data of STAD patients from the Cancer Genome Atlas (TCGA), including somatic mutation, transcriptome, and survival data. We performed a series of analyses such as tumor mutational burden (TMB), immune infiltration, and copy number variation (CNV) analysis to evaluate the potential mechanism of filaggrin (FLG) mutation in gastric cancer. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and gene set enrichment analysis (GSEA) were performed for annotation of differentially expressed genes (DEGs). The STRING online database was used to construct the protein–protein interaction (PPI) and ceRNA network and hub genes were identified. Univariate and multivariate Cox regression analyses were used to determine the effect of selected DEGs on tumor prognosis.

Results: The FLG-mutant group (FLG-MT) showed a higher mutation load and immunogenicity in gastric cancer. GO and KEGG analyses identified and ranked unique biologic processes and immune-related pathway maps that correlated with the FLG-mutant target. GSEA analysis showed that several tumorigenesis and metastasis-related pathways were indeed enriched in FLG-mutant tumor tissue. Both cell cycle–related pathways and the DNA damage and repair associated pathways were also enriched in the FLG-MT group. The FLG mutations resulted in increased gastric cancer sensitivity to 24 chemotherapeutic drugs. The ceRNA network was established using Cytoscape and the PPI network was established in the STRING database. The results of the prognostic information further demonstrated that the OS and DFS were significantly higher in FLG mutation carriers, and the FLG gene mutation might be a protective factor.

Conclusion: The multiple molecular mechanisms of the FLG gene in STAD are worthy of further investigation and may reveal novel therapeutic targets and biomarkers for STAD treatment.

Combination of 35-Gene Mutation Profile and Radiotherapy Dosimetry Predicts the Therapeutic Outcome of Definitive Chemoradiation in Patients With Esophageal Squamous Cell Carcinoma

Esophageal cancer is a common malignancy worldwide and a leading cause of cancer-related mortality. Definitive concurrent chemoradiotherapy (CCRT) has been widely used to treat locally advanced esophageal squamous cell carcinoma (ESCC). In this study, we evaluated the predictive power of a 35-gene mutation profile and radiation parameters in patients with ESCC. Data from 44 patients with ESCC who underwent definitive CCRT were retrospectively reviewed. A 35-gene mutation profile, derived from reported ESCC-specific next-generation sequencing results, and radiation dosimetry parameters were examined using the Kaplan–Meier curve and Cox proportional hazards model. All patients were native Chinese and underwent CCRT with a median follow-up time of 22.0 months. Significant prognostic factors affecting progression-free survival in the multivariable Cox regression model were clinical nodal staging ≥2 (hazard ratio, HR: 2.52, 95% CI: 1.15–5.54, p = 0.022), ≥10% lung volume receiving ≥30 Gy (V30) (HR: 2.36, 95% CI: 1.08–5.17, p = 0.032), and mutation of fibrous sheath interacting protein 2 (FSIP2) (HR: 0.08, 95% CI: 0.01–0.58, p = 0.013). For overall survival, significant prognostic factors in the multivariable Cox regression model were lung V30 ≥10% (HR: 3.71, 95% CI: 1.48–9.35, p = 0.005) and mutation of spectrin repeat containing nuclear envelope protein 1 (SYNE1) (HR: 2.95, 95% CI: 1.25–6.97, p = 0.014). Our cohort showed higher MUC17 (79.5% vs. 5.7%), FSIP2 (18.2% vs. 6.2%), and SYNE1 (38.6% vs. 11.0%) mutation rates and lower TP53 (38.6% vs. 68.7%) mutation rates than the ESCC cohorts from The Cancer Genome Atlas. In conclusion, by using a combination of a 35-gene mutation profile and radiotherapy dosimetry, mutations in FSIP2 and SYNE1 as well as lung V30 were identified as potential predictors for developing a prediction model for clinical outcomes in patients with ESCC administered definitive CCRT.

A New Story of the Three Magi: Scaffolding Proteins and lncRNA Suppressors of Cancer

Scaffolding molecules exert a critical role in orchestrating cellular response through the spatiotemporal assembly of effector proteins as signalosomes. By increasing the efficiency and selectivity of intracellular signaling, these molecules can exert (anti/pro)oncogenic activities. As an archetype of scaffolding proteins with tumor suppressor property, the present review focuses on MAGI1, 2, and 3 (membrane-associated guanylate kinase inverted), a subgroup of the MAGUK protein family, that mediate networks involving receptors, junctional complexes, signaling molecules, and the cytoskeleton. MAGI1, 2, and 3 are comprised of 6 PDZ domains, 2 WW domains, and 1 GUK domain. These 9 protein binding modules allow selective interactions with a wide range of effectors, including the PTEN tumor suppressor, the β-catenin and YAP1 proto-oncogenes, and the regulation of the PI3K/AKT, the Wnt, and the Hippo signaling pathways. The frequent downmodulation of MAGIs in various human malignancies makes these scaffolding molecules and their ligands putative therapeutic targets. Interestingly, MAGI1 and MAGI2 genetic loci generate a series of long non-coding RNAs that act as a tumor promoter or suppressor in a tissue-dependent manner, by selectively sponging some miRNAs or by regulating epigenetic processes. Here, we discuss the different paths followed by the three MAGIs to control carcinogenesis.