Prognostic value of sorting nexin 10 weak expression in stomach adenocarcinoma revealed by weighted gene co-expression network analysis

Pan-cancer analysis of the prognostic and immunological role of SNX29: a potential target for survival and immunotherapy

BACKGROUND

There is growing evidence that the SNX family is critical for clinical prognosis, immune infiltration and drug sensitivity in many types of tumors. The relationships between the SNX29 gene and clinical prognosis as well as pan-cancer cell infiltration and drug sensitivity have not been fully elucidated.

METHODS

In the current study, we explored the correlation between SNX29 expression and 33 types of malignancies via TCGA and GTEx. The relationship between SNX29 expression and prognostic outcome in the pan-caner cohort was also analyzed. Immune infiltration, microsatellite instability, tumor mutational burden and potential therapeutic targets of SNX29 were investigated by analyzing public databases.

RESULTS

The expression of SNX29 was found to be significantly upregulated in most tumor tissues compared to normal tissues. SNX29 expression was associated with prognosis and clinical stage. In the immune infiltration analysis, a significant relationship was found between SNX29 expression and the level of immune infiltration. In addition, we found associations between the SNX29 gene and tumor mutation burden, microsatellite instability, immunoinhibition-related genes and autophagy-related genes. Finally, the expression of SNX29 was significantly associated with the sensitivity of various tumor cell lines to 8 antitumor drugs. These results suggest that SNX29 expression is important in determining the progression, immune infiltration and drug sensitivity of various cancers.

CONCLUSION

This study provides novel insights into the potential pan-cancer targets of SNX29.

Identification of Hub Genes of Lung Adenocarcinoma Based on Weighted Gene Co-Expression Network in Chinese Population

Purpose: Lung adenocarcinoma is one of the most common malignancies. Though some historic breakthroughs have been made in lung adenocarcinoma, its molecular mechanisms of development remain elusive. The aim of this study was to identify the potential genes associated with the lung adenocarcinoma progression and to provide new ideas for the prognosis evaluation of lung adenocarcinoma.

Methods: The transcriptional profiles of ten pairs of snap-frozen tumor and adjacent normal lung tissues were obtained by performing RNA-seq. Weighted gene co-expression network analysis (WGCNA) was used to construct free-scale gene co-expression networks in order to explore the associations of gene sets with the clinical features and to investigate the functional enrichment analysis of co-expression genes. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and Gene Set Enrichment Analysis (GSEA) analyses were performed using clusterProfiler. The protein-protein network (PPI) was established using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and hub genes were identified using Cytohubba in Cytoscape. Transcription factor enrichment analysis was performed by the RcisTarget program in R language.

Results: Based on RNA-seq data, 1,545 differentially expressed genes (DEGs) were found. Eight co-expression modules were identified among these DEGs. The blue module exhibited a strong correlation with LUAD, in which ADCY4, RXFP1, AVPR2, CALCRL, ADRB1, RAMP3, RAMP2 and VIPR1 were hub genes. A low expression level of RXFP1, AVPR2, ADRB1 and VIPR1 was detrimental to the survival of LUAD patients. Genes in the blue module enriched in 86 Gene Ontology terms and five KEGG pathways. We also found that transcription factors EGR3 and EXOSC3 were related to the biological function of the blue module. Overall, this study brings a new perspective to the understanding of LUAD and provides possible molecular biomarkers for prognosis evaluation of LUAD.

LncRNA PCGEM1 in Human Cancers: Functions, Mechanisms and Promising Clinical Utility

As novel members of the noncoding RNA family, long noncoding RNAs (lncRNAs) have been widely reported to function as powerful regulators in gene expression processes, including chromosome remodeling, transcription interference and posttranscriptional modification. With the rapid development of metagenomic sequencing, numerous studies have indicated that the dysregulation of lncRNAs is closely associated with diverse human diseases, especially cancers. Prostate Gene Expression Marker 1 (PCGEM1), a recently identified lncRNA, has been reported to play a crucial role in the initiation and progression of multiple tumors by interacting with pivotal regulators of tumor-related signaling pathways. In this review, we will retrospectively review the recent studies of the expression of lncRNA PCGEM1 in human cancers and comprehensively describe the underlying regulatory mechanism by which PCGEM1 functions in tumors. More importantly, based on the relationship between PCGEM1 and cancers, the potential application of PCGEM1 in clinical diagnosis, prognosis and therapeutic treatment will also be highlighted.

Identification of Lymph Node Metastasis-Related Key Genes and Prognostic Risk Model in Bladder Cancer by Co-Expression Analysis

Background: Lymph node metastasis (LNM) is an important pathological characteristic of bladder cancer (BCa). However, the molecular mechanism underlying LNM was not thoroughly elaborated. Identification for LNM-related biomarkers may contribute to making suitable therapies. So, the current study was aimed to identify key genes and construct a prognostic signature. Methods: Based on the Cancer Genome Atlas (TCGA) database, gene expression and clinical information were obtained. Then, the weighted gene co-expression network analysis (WGCNA) was performed to identify the key modules and hub genes. A function analysis and a gene set enrichment analysis were applied to explore biological functions and pathways of interested genes. Furthermore, a prognostic model based on LNM-related genes was constructed by using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Results: Finally, nine co-expression modules were constructed, and two modules (turquoise and green) were significantly associated with LNM. Three hub genes were identified as DACT3, TNS1, and MSRB3, which were annotated in actin binding, actin cytoskeleton, adaptive immune response, and cell adhesion molecular binding by the GSEA method. Further analysis demonstrated that three hub genes were associated with the overall survival of BCa patients. In addition, we built a prognostic signature based on the genes from LNM-related modules and evaluated the prognostic value of this signature. Conclusion: In general, this study revealed the key genes related to LNM and prognostic signature, which might provide new insights into therapeutic target of BCa.

SNX10 and PTGDS are associated with the progression and prognosis of cervical squamous cell carcinoma

Background

Cervical cancer (CC) is the primary cause of death in women. This study sought to investigate the potential mechanism and prognostic genes of CC.

Methods

We downloaded four gene expression profiles from GEO. The RRA method was used to integrate and screen differentially expressed genes (DEGs) between CC and normal samples. Functional analysis was performed by clusterprofiler. We built PPI network by Search Tool for the Retrieval of Interacting Genes Database (STRING) and selected hub modules via Molecular COmplex Detection (MCODE). CMap database was used to find molecules with therapeutic potential for CC. The hub genes were validated in GEO datasets, Gene Expession Profiling Interactive Analysis (GEPIA), immunohistochemistry, Cox regression analysis, TCGA methylation analysis and ONCOMINE were carried out. ROC curve analysis and GSEA were also performed to describe the prognostic significance of hub genes.

Results

Functional analysis revealed that 147 DEGs were significantly enriched in binding, cell proliferation, transcriptional activity and cell cycle regulation. PPI network screened 30 hub genes, with CDK1 having the strongest connectivity with CC. Cmap showed that apigenin, thioguanine and trichostatin A might be used to treat CC(P < 0.05). Eight genes (APOD, CXCL8, MMP1, MMP3, PLOD2, PTGDS, SNX10 and SPP1) were screened out through GEPIA. Of them, only PTGDS and SNX10 had not appeared in previous studies about CC. The validation in GEO showed that PTGDS showed low expression while SNX10 presented high expression in tumor tissues. Their expression profiles were consistent with the results in immunohistochemistry. ROC curve analysis indicated that the model had a good diagnostic efficiency (AUC = 0.738). GSEA analysis demonstrated that the two genes were correlated with the chemokine signaling pathway (P < 0.05). TCGA methylation analysis showed that patients with lowly-expressed and highly-methylated PTGDS had a worse prognosis than those with highly-expressed and lowly-methylated PTGDS (p = 0.037). Cox regression analysis showed that SNX10 and PTGDS were independent prognostic indicators for OS among CC patients (P = 0.007 and 0.003).

Conclusions

PTGDS and SNX10 showed abnormal expression and methylation in CC. Both genes might have high prognostic value of CC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08212-w.

WGCNA identification of TLR7 as a novel diagnostic biomarker, progression and prognostic indicator, and immunotherapeutic target for stomach adenocarcinoma

Stomach adenocarcinoma (STAD) is a malignant tumor with high histological heterogeneity. However, the potential mechanism of STAD tumorigenesis remains to be elucidated. The purpose of our research was to identify candidate genes associated with the diagnosis, progression, prognosis, and immunotherapeutic targets of STAD. Based on tumor samples from the GSE28541 dataset, weighted gene co-expression network analysis revealed 16 modules related to STAD stage and grade. The salmon module emerged as the most relevant module (cor = 0.34), and functional enrichment analysis showed that the genes in the salmon were primarily related to major histocompatibility complex, immune response, and cell differentiation. Toll-like receptor 7 (TLR7) was recognized as the real hub gene in the salmon module. Compared to normal stomach tissues, the transcriptional and translational levels of TLR7 were significantly elevated in STAD. Receiver operating characteristic curves verified that TLR7 displayed remarkable sensitivity and specificity for the diagnosis of STAD. The functions of TLR7 were primarily enriched in the regulation of Toll-like receptor signaling pathway, pattern recognition receptor signaling pathway, and innate immune response. Overexpression of TLR7 tended to indicate more advanced STAD, higher degree of STAD, and poorer prognosis of STAD. In addition, TLR7 expression was positively correlated with immune cell infiltration and immune checkpoint expression. Somatic copy number alteration of TLR7 was also significantly related to immune cell infiltration. In conclusion, this study revealed the crucial role of TLR7 in STAD and provided new perspectives for the selection of biomarkers, progression and prognosis indicators, and immunotherapeutic targets for STAD.

Weighted correlation network analysis identifies FN1, COL1A1 and SERPINE1 associated with the progression and prognosis of gastric cancer

BACKGROUND

Gastric cancer (GC) is one of the most deadliest tumours worldwide, and its prognosis remains poor.

OBJECTIVE

This study aims to identify and validate hub genes associated with the progression and prognosis of GC by constructing a weighted correlation network.

METHODS

The gene co-expression network was constructed by the WGCNA package based on GC samples and clinical data from the TCGA database. The module of interest that was highly related to clinical traits, including stage, grade and overall survival (OS), was identified. GO and KEGG pathway enrichment analyses were performed using the clusterprofiler package in R. Cytoscape software was used to identify the 10 hub genes. Differential expression and survival analyses were performed on GEPIA web resources and verified by four GEO datasets and our clinical gastric specimens. The receiver operating characteristic (ROC) curves of hub genes were plotted using the pROC package in R. The potential pathogenic mechanisms of hub genes were analysed using gene set enrichment analysis (GSEA) software.

RESULTS

A total of ten modules were detected, and the magenta module was identified as highly related to OS, stage and grade. Enrichment analysis of magenta module indicated that ECM-receptor interaction, focal adhesion, PI3K-Akt pathway, proteoglycans in cancer were significantly enriched. The PPI network identified ten hub genes, namely COL1A1, COL1A2, FN1, POSTN, THBS2, COL11A1, SPP1, MMP13, COMP, and SERPINE1. Three hub genes (FN1, COL1A1 and SERPINE1) were finally identified to be associated with carcinogenicity and poor prognosis of GC, and all were independent risk factors for GC. The area under the curve (AUC) values of FN1, COL1A1 and SERPINE1 for the prediction of GC were 0.702, 0.917 and 0.812, respectively. GSEA showed that three hub genes share 15 common upregulated biological pathways, including hypoxia, epithelial mesenchymal transition, angiogenesis, and apoptosis.

CONCLUSION

We identified FN1, COL1A1 and SERPINE1 as being associated with the progression and poor prognosis of GC.

Identification of novel candidate biomarkers for pancreatic adenocarcinoma based on TCGA cohort

Pancreatic adenocarcinoma (PAAD) is the most serious solid tumor type throughout the world. The present study aimed to identify novel biomarkers and potential efficacious small drugs in PAAD using integrated bioinformatics analyses. A total of 4777 differentially expressed genes (DEGs) were filtered, 2536 upregulated DEGs and 2241 downregulated DEGs. Weighted gene co-expression network analysis was then used and identified 12 modules, of which, blue module with the most significant enrichment result was selected. KEGG and GO enrichment analyses showed that all DEGs of blue module were enriched in EMT and PI3K/Akt pathway. Three hub genes (ITGB1, ITGB5, and OSMR) were determined as key genes with higher expression levels, significant prognostic value and excellent diagnostic efficiency for PAAD. Additionally, some small molecule drugs that possess the potential to treat PAAD were screened out, including thapsigargin (TG). Functional in vitro experiments revealed that TG repressed cell viability via inactivating the PI3K/Akt pathway in PAAD cells. Totally, our findings identified three key genes implicated in PAAD and screened out several potential small drugs to treat PAAD.

The molecular structure and function of sorting nexin 10 in skeletal disorders, cancers, and other pathological conditions

SNX10 is a member of the phox homology domain-containing family of phosphoinositide-binding proteins. Intracellularly, SNX10 localizes to endosomes where it mediates intracellular trafficking, endosome organization, and protein localization to the centrosome and cilium. It is highly expressed in bone and the gut where it participates in bone mineral and calcium homeostasis through the regulation of osteoclastic bone resorption and gastric acid secretion, respectively. Not surprisingly, patients harboring mutations in SNX10 mutation manifest a phenotype of autosomal recessive osteopetrosis or malignant infantile osteopetrosis, which is clinically characterized by dense bones with increased cortical bone into the medullary space with bone marrow occlusion or depletion, bone marrow failure, and anemia. Accordingly, SNX10 mutant osteoclasts exhibit impaired bone resorptive capacity. Beyond the skeleton, there is emerging evidence implicating SNX10 in cancer development, metabolic disorders, inflammation, and chaperone-mediated autophagy. Understanding the structural basis through which SNX10 exerts its diverse biological functions in both cell and tissue-specific manners may therefore inform new therapeutic opportunities toward the treatment and management of SNX10-related diseases.

Sorting nexins: A novel promising therapy target for cancerous/neoplastic diseases

Sorting nexins (SNXs) are a diverse group of cytoplasmic- and membrane-associated phosphoinositide-binding proteins containing the PX domain proteins. The function of SNX proteins in regulating intracellular protein trafficking consists of endocytosis, endosomal sorting, and endosomal signaling. Dysfunctions of SNX proteins are demonstrated to be involved in several cancerous/neoplastic diseases. Here, we review the accumulated evidence of the molecular structure and biological function of SNX proteins and discuss the regulatory role of SNX proteins in distinct cancerous/neoplastic diseases. SNX family proteins may be a valuable potential biomarker and therapeutic strategy for diagnostics and treatment of cancerous/neoplastic diseases.

Identification of novel biomarkers correlated with prostate cancer progression by an integrated bioinformatic analysis

Prostate cancer (PCa) is a highly aggressive malignant tumor and the biological mechanisms underlying its progression remain unclear.We performed weighted gene co-expression network analysis in PCa dataset from the Cancer Genome Atlas database to identify the key module and key genes related to the progression of PCa. Furthermore, another independent datasets were used to validate our findings.A total of 744 differentially expressed genes were screened out and 5 modules were identified for PCa samples from the Cancer Genome Atlas database. We found the brown module was the key module and related to tumor grade (R2 = 0.52) and tumor invasion depth (R2 = 0.39). Besides, 24 candidate hub genes were screened out and 2 genes (BIRC5 and DEPDC1B) were identified and validated as real hub genes that associated with the progression and prognosis of PCa. Moreover, the biological roles of BIRC5 were related to G-protein coupled receptor signal pathway, and the functions of DEPDC1B were related to the G-protein coupled receptor signal pathway and retinol metabolism in PCa.Taken together, we identified 1 module, 24 candidate hub genes and 2 real hub genes, which were prominently associated with PCa progression. With more experiments and clinical trials, these genes may provide a promising future for PCa treatment.

LncRNA PCGEM1 enhances metastasis and gastric cancer invasion through targeting of miR-129-5p to regulate P4HA2 expression

AIM

Aberrantly expressed long non-coding RNAs (lncRNAs) are critical instigators of gastric cancer (GC) progression and metastasis. The ceRNA (competing endogenous RNAs) network is well-known in modulating tumor pathological and physiological processes. This research aims to determine the more effective molecular mechanisms of lncRNA PCGEM1 (prostate cancer gene expression marker 1).

METHODS

Bioinformatics database and Ago2-RIP were performed to predict and verify the potential targets of lncRNA PCGEM1. Both gain- and loss-of-function experiments were carried out to dissect the biological functions of RNAs. Fluorescence in situ hybridization, dual-luciferase reporter assays, western blot, and real-time PCR (RT-PCR) experiments were utilized to determine the pathophysiological pathways of competitive endogenous RNAs (ceRNAs).

RESULTS

GC cells expressed high levels of cytoplasmic PCGEM1. Loss-of-function experiments displayed that the silencing of PCGEM1 suppressed metastatic and invasive cell qualities. PCGEM1 was also found to have associations with miR-129-5p. Subsequently, luciferase reporter and RIP experiments, together with RT-PCR, verified that PCGEM1 functioned as a ceRNA of P4HA2 (Prolyl 4-Hydroxylase Subunit Alpha 2) via sponging miR-129-5p to up-regulate P4HA2 expression. Finally, the rescue assays determined that P4HA2 overexpression rescued the inhibited cell invasion and metastasis caused by PCGEM1 down-regulation.

CONCLUSION

These findings found that an over-expression of PCGEM1 in GC acts as a miR-129-5p sponge, leading to higher levels of P4HA2. The PCGEM1/miR-129-5p/P4HA2 axis was confirmed to possess a crucial role in GC metastasis and invasion, suggesting its utility as a potential diagnostic and therapeutic biomarker.

Integrated profiling identifies SLC5A6 and MFAP2 as novel diagnostic and prognostic biomarkers in gastric cancer patients

Gastric cancer (GC) is one of the leading causes of malignancy‑associated mortality worldwide. However, the underlying molecular mechanisms of GC are unclear and the prognosis of GC is poor. Therefore, it is important and urgent to explore the underlying mechanisms and screen for novel diagnostic and prognostic biomarkers, as well as therapeutic targets. In the current study, scale‑free gene co‑expression networks were constructed using weighted gene co‑expression network analysis, the potential associations between gene sets and clinical features were investigated, and the hub genes were identified. The gene expression profiles of GSE38749 were downloaded from the Gene Expression Omnibus database. RNA‑seq and clinical data for GC from The Cancer Genome Atlas were utilized for verification. Furthermore, the expression of candidate biomarkers in gastric tissues was investigated. Survival analysis was performed using Kaplan‑Meier and log‑rank test. The predictive role of candidate biomarkers in GC was evaluated using a receiver operator characteristic (ROC) curve. Gene Ontology, gene set enrichment analysis and gene set variation analysis methods were used to interpret the function of candidate biomarkers in GC. A total of 29 modules were identified via the average linkage hierarchical clustering. A significant module consisting of 48 genes associated with clinical traits was found; three genes with high connectivity in the clinical significant module were identified as hub genes. Among them, SLC5A6 and microfibril‑associated protein 2 (MFAP2) were negatively associated with the overall survival, and their expression was elevated in GC compared with non‑tumor tissues. Additionally, ROC curves indicated that SLC5A6 and MFAP2 showed a good diagnostic power in discriminating cancerous from normal tissues. SLC5A6 and MFAP2 were identified as novel diagnostic and prognostic biomarkers in GC patients; both of these genes were first reported here in connection with GC and deserved further research.

Long noncoding RNA NALT1-induced gastric cancer invasion and metastasis via NOTCH signaling pathway

BACKGROUND

Long noncoding RNAs (lncRNAs) are aberrant and play critical roles in gastric cancer (GC) progression and metastasis. Searching for coexpressed lncRNA clusters or representative biomarkers related to malignant phenotypes of GC may help to elucidate the mechanism of tumor development and predict the prognosis of GC.

AIM

To investigate the prognostic value of NOTCH1 associated with lncRNA in T cell acute lymphoblastic leukemia 1 (NALT1) in GC and the mechanism of its involvement in GC invasion and metastasis.

METHODS

RNA sequencing and corresponding clinical data were downloaded from The Cancer Genome Atlas database. The significance module was studied by weighted gene coexpression network analysis. A total of 336 clinical samples were included in the study. Gene silencing, reverse transcription polymerase chain reaction, western blotting, scrape motility assay, and Transwell migration assay were used to assess the function of hub-lncRNAs.

RESULTS

At the transcriptome level, 3339 differentially expressed lncRNAs were obtained. weighted gene coexpression network analysis was used to obtain 15 lncRNA clusters and observe their coexpression. Pearson’s correlation showed that blue module was correlated with tumor grade and survival. NALT1 was the hub-lncRNA of blue module and was an independent risk factor for GC prognosis. NALT1 was overexpressed in GC and its expression was closely related to invasion and metastasis. The mechanism may involve NALT1 regulation of NOTCH1, which is associated with lncRNA in T cell acute lymphoblastic leukemia, through cis regulation, thereby affecting the expression of the NOTCH signaling pathway.

CONCLUSION

NALT1 is overexpressed and promotes invasion and metastasis of GC. The mechanism may be related to regulation of NOTCH1 by NALT1 and its effect on NOTCH signaling pathway expression.