PHTF2 regulates lipids metabolism in gastric cancer

An integrated ceRNA network identifies miR-375 as an upregulated miRNA playing a tumor suppressive role in aggressive prostate cancer

Prostate cancer (PCa) remains a significant cause of morbidity and mortality among men worldwide. A number of genes have been implicated in prostate tumorigenesis, but the mechanisms underlying their dysregulation are still incompletely understood. Evidence has established the competing endogenous RNA (ceRNA) theory as a novel regulatory mechanism for post-transcriptional alterations. Yet, a comprehensive characterization of ceRNA network in PCa lacks. Here we utilize stringent in-silico methods to construct a large ceRNA network across different PCa stages, and provide experimental demonstration for the competing regulation among protumorigenic SEC23A, PHTF2, and their corresponding ceRNA pairs. Using machine learning, we establish a ceRNA-based signature (ceRNA_sig) predictive of androgen receptor (AR) activity, tumor aggressiveness, and patient outcomes. Importantly, we identify miR-375 as a key node in PCa ceRNA network, which is upregulated in PCa relative to normal tissues. Forced expression of miR-375 significantly inhibits, while its inhibition promotes, aggressive behaviors of both AR+ and AR- PCa cells in vitro and in vivo. Mechanistically, we show that miR-375 predominantly targets genes possessing oncogenic roles (e.g., proliferation, DNA repair, and metastasis), and thus release targets with tumor suppressive functions. This action model well clarifies why an upregulated miRNA plays a tumor suppressive role in PCa. Together, our study provides new insights into understanding of transcriptomic aberrations during PCa evolution, and nominates miR-375 as a potential therapeutic target for combating aggressive PCa.

Profiling the molecular and clinical landscape of glioblastoma utilizing the Oncology Research Information Exchange Network brain cancer database

Background

Glioblastoma exhibits aggressive growth and poor outcomes despite treatment, and its marked variability renders therapeutic design and prognostication challenging. The Oncology Research Information Exchange Network (ORIEN) database contains complementary clinical, genomic, and transcriptomic profiling of 206 glioblastoma patients, providing opportunities to identify novel associations between molecular features and clinical outcomes.

Methods

Survival analyses were performed using the Logrank test, and clinical features were evaluated using Wilcoxon and chi-squared tests with q-values derived via Benjamini-Hochberg correction. Mutational analyses utilized sample-level enrichments from whole exome sequencing data, and statistical tests were performed using the one-sided Fisher Exact test with Benjamini-Hochberg correction. Transcriptomic analyses utilized a student's t-test with Benjamini-Hochberg correction. Expression fold changes were processed with Ingenuity Pathway Analysis to determine pathway-level alterations between groups.

Results

Key findings include an association of MUC17, SYNE1, and TENM1 mutations with prolonged overall survival (OS); decreased OS associated with higher epithelial growth factor receptor (EGFR) mRNA expression, but not with EGFR amplification or mutation; a 14-transcript signature associated with OS > 2 years; and 2 transcripts associated with OS < 1 year.

Conclusions

Herein, we report the first clinical, genomic, and transcriptomic analysis of ORIEN glioblastoma cases, incorporating sample reclassification under updated 2021 diagnostic criteria. These findings create multiple avenues for further investigation and reinforce the value of multi-institutional consortia such as ORIEN in deepening our knowledge of intractable diseases such as glioblastoma.

Differential co-expression network analysis elucidated genes associated with sensitivity to farnesyltransferase inhibitor and prognosis of acute myeloid leukemia

Acute myeloid leukemia (AML) is a heterogeneous disease and the most common form of acute leukemia with a poor prognosis. Due to its complexity, the disease requires the identification of biomarkers for reliable prognosis. To identify potential disease genes that regulate patient prognosis, we used differential co-expression network analysis and transcriptomics data from relapsed, refractory, and previously untreated AML patients based on their response to treatment in the present study. In addition, we combined functional genomics and transcriptomics data to identify novel and therapeutically potential systems biomarkers for patients who do or do not respond to treatment. As a result, we constructed co-expression networks for response and non-response cases and identified a highly interconnected group of genes consisting of SECISBP2L, MAN1A2, PRPF31, VASP, and SNAPC1 in the response network and a group consisting of PHTF2, SLC11A2, PDLIM5, OTUB1, and KLRD1 in the non-response network, both of which showed high prognostic performance with hazard ratios of 4.12 and 3.66, respectively. Remarkably, ETS1, GATA2, AR, YBX1, and FOXP3 were found to be important transcription factors in both networks. The prognostic indicators reported here could be considered as a resource for identifying tumorigenesis and chemoresistance to farnesyltransferase inhibitor. They could help identify important research directions for the development of new prognostic and therapeutic techniques for AML.

Prognostic significance of high NPC2 expression in gastric cancer

Gastric cancer is one of the most common malignancies worldwide, and the third leading cause of cancer-related death. The identification of novel biomarkers and therapeutic targets is critical to improve the prognosis. A total of 380 patients with primary gastric cancer from the TCGA database were analyzed. The receiver operating characteristic curves were plotted. We further evaluated the independent prognostic ability of NPC2 expression for overall survival (OS) and relapse-free survival (RFS) through the Kaplan-Meier curve and Cox analysis. The NPC2 expression was significantly higher (P < 0.001) in gastric cancer. High NPC2 expression was significantly (P < 0.0001) associated with poor OS and poor RFS. The age, stage, radiation therapy, residual tumor, and NPC2 expression showed independent prognostic value for OS. The gender and NPC2 expression showed independent prognostic value for RFS. The higher NPC2 expression was observed in gastric cancer, compared with adjacent normal tissue (P < 0.001), confirmed by the IHC staining. The CCK-8 assay showed that NPC2 knockdown inhibits cell proliferation while NPC2 overexpression promotes cell proliferation (P < 0.05). NPC2 expression may serve as a promising prognostic biomarker for patients with gastric cancer.

Identification and validation of anoikis-associated gene SNCG as a prognostic biomarker in gastric cancer

Anoikis is a newly discovered form of apoptosis that was originally identified in the extracellular matrix (ECM). Recent studies have reported that anoikis is central to cancer metastasis. Here, SNCG was identified as hub anoikis-associated gene in GC and associated with prognosis of patients with GC. To screen the hub anoikis-associated genes connected to GC, the database of Cancer Genome Atlas (TCGA) was employed. For further validating these identified genes, the Gene Expression Omnibus (GEO) dataset was applied, and Western blotting and quantitative Real-Time PCR were carried out. To identify hub genes, we conducted the analyses of univariate Cox regression, differential expression, and weighted gene co-expression network analysis (WGCNA). According to the identified hub genes, we constructed a model of prognosis. Following complex analysis, SNCG was finally identified as hub anoikis-associated gene in GC. Indeed, K-M and receiver operating characteristic analyses suggested that the expression patterns of SNCG can be used as prognostic factors for GC survival. The expression and survival trends of SNCG were verified in the validation cohort and in vitro experimental analyses. The analysis of immune cell infiltration showed that the infiltrated immune cells varied among patients with GC and gene SNCG. Furthermore, due to the significant association of the constructed risk signature with patient age and survival, this risk signature can be used to predict the prognosis of GC. We suggest that SNCG was served as hub anoikis-associated gene in GC. Meanwhile, SNCG may have prognostic potential for overall patient survival.

Structural screens identify candidate human homologs of insect chemoreceptors and cryptic Drosophila gustatory receptor-like proteins

Insect odorant receptors and gustatory receptors define a superfamily of seven transmembrane domain ion channels (referred to here as 7TMICs), with homologs identified across Animalia except Chordata. Previously, we used sequence-based screening methods to reveal conservation of this family in unicellular eukaryotes and plants (DUF3537 proteins) (Benton et al., 2020). Here, we combine three-dimensional structure-based screening, ab initio protein folding predictions, phylogenetics, and expression analyses to characterize additional candidate homologs with tertiary but little or no primary structural similarity to known 7TMICs, including proteins in disease-causing Trypanosoma. Unexpectedly, we identify structural similarity between 7TMICs and PHTF proteins, a deeply conserved family of unknown function, whose human orthologs display enriched expression in testis, cerebellum, and muscle. We also discover divergent groups of 7TMICs in insects, which we term the gustatory receptor-like (Grl) proteins. Several Drosophila melanogaster Grls display selective expression in subsets of taste neurons, suggesting that they are previously unrecognized insect chemoreceptors. Although we cannot exclude the possibility of remarkable structural convergence, our findings support the origin of 7TMICs in a eukaryotic common ancestor, counter previous assumptions of complete loss of 7TMICs in Chordata, and highlight the extreme evolvability of this protein fold, which likely underlies its functional diversification in different cellular contexts.

LINC01089, suppressed by YY1, inhibits lung cancer progression by targeting miR-301b-3p/HPDG axis

PURPOSE

LINC01089 is a newly identified lncRNA and rarely reported in human cancers. Our study aimed to investigate its role in lung cancer.

METHODS

YY1, LINC01089, and miR-301b-3p levels in lung cancer tissues and cells were assessed using qRT-PCR. Bioinformatics analysis and luciferase reporter, ChIP, and RIP assays were carried out for determining the relationships among YY1, LINC01089, miR-301b-3p, and HPGD. Gain- and loss-of-function assays were carried out to confirm the impacts of LINC01089 and HPDG in lung cancer cells. CCK-8 assay was used to assess cell proliferation rate, and Transwell assay was applied to measure cell invasion and migration. An in vivo tumor model was applied for validating the role of LINC01089.

RESULTS

LINC01089 was decreased in lung cancer tissues and cells, and low LINC01089 level predicted a poor clinical outcome. YY1 directly bound to LINC01089 promoter region and inhibited its transcription. LINC01089 knockdown thwarted the proliferation, invasion, and migration capacity of H1299 and A549 cells and aggravated tumor growth. Specifically, LINC01089 functioned as a competing endogenous RNA of miR-301b-3p to modulate HPGD and thereby affected lung cancer progression.

CONCLUSION

Our data revealed that LINC01089, directly suppressed by YY1, inhibited lung cancer progression by targeting the miR-301b-3p/HPGD axis. Graphical abstract 1. LINC01089 expression was downregulated in lung cancer tisuues and cell lines, and low LINC01089 levels predicted a poor clinical outcome. 2. LINC01089 knockdown enhanced proliferation, invasion, and migration of H1299 and A549 cells in vitro and promoted lung cancer cell tumorigenesis and metastasis in vivo. 3. LINC01089, directly suppressed by YY1, functioned as a competing endogenous RNA against miR-301b-3p to increase HPGD expression.

Lipid metabolism characterization in gastric cancer identifies signatures to predict prognostic and therapeutic responses

Purpose: Increasing evidence has elucidated the significance of lipid metabolism in predicting therapeutic efficacy. Obviously, a systematic analysis of lipid metabolism characterizations of gastric cancer (GC) needs to be reported.

Experimental design: Based on two proposed computational algorithms (TCGA-STAD and GSE84437), the lipid metabolism characterization of 367 GC patients and its systematic relationship with genomic characteristics, clinicopathologic features, and clinical outcomes of GC were analyzed in our study. Differentially expressed genes (DEGs) were identified based on the lipid metabolism cluster. At the same time, we applied single-factor Cox regression and random forest to screen signature genes to construct a prognostic model, namely, the lipid metabolism score (LMscore). Next, we deeply explored the predictive value of the LMscore for GC. To verify the specific changes in lipid metabolism, a total of 90 serum, 30 tumor, and non-tumor adjacent tissues from GC patients, were included for pseudotargeted metabolomics analysis via SCIEX triple quad 5500 LC-MS/MS system.

Results: Five lipid metabolism signature genes were identified from a total of 3,104 DEGs. The LMscore could be a prognosticator for survival in different clinicopathological GC cohorts. As well, the LMscore was identified as a predictive biomarker for responses to immunotherapy and chemotherapeutic drugs. Additionally, significant changes in sphingolipid metabolism and sphingolipid molecules were discovered in cancer tissue from GC patients by pseudotargeted metabolomics.

Conclusion: In conclusion, multivariate analysis revealed that the LMscore was an independent prognostic biomarker of patient survival and therapeutic responses in GC. Depicting a comprehensive landscape of the characteristics of lipid metabolism may help to provide insights into the pathogenesis of GC, interpret the responses of gastric tumors to therapies, and achieve a better outcome in the treatment of GC. In addition, significant alterations of sphingolipid metabolism and increased levels of sphingolipids, in particular, sphingosine (d16:1) and ceramide, were discovered in GC tissue by lipidome pseudotargeted metabolomics, and most of the sphingolipid molecules have the potential to be diagnostic biomarkers for GC.

Tumor vessel normalization and immunotherapy in gastric cancer

Gastric cancer (GC) is a common malignant tumor, and patients with GC have a low survival rate due to limited effective treatment methods. Angiogenesis and immune evasion are two key processes in GC progression, and they act synergistically to promote tumor progression. Tumor vascular normalization has been shown to improve the efficacy of cancer immunotherapy, which in turn may be improved through enhanced immune stimulation. Therefore, it may be interesting to identify synergies between immunomodulatory agents and anti-angiogenic therapies in GC. This strategy aims to normalize the tumor microenvironment through the action of the anti-vascular endothelial growth factor while stimulating the immune response through immunotherapy and prolonging the survival of GC patients.

ChIP-seq assay revealed histone modification H3K9ac involved in heat shock response of the sea cucumber Apostichopus japonicus

Heat stress poses an increasing threat for the marine invertebrate Apostichopus japonicus. Histone lysine acetylation is a central chromatin modification for epigenetic regulation of gene expression during stress response. In this study, a genome-wide characterization for acetylated lysine 9 on histone H3 (H3K9ac) binding regions in normal temperature (18 °C) and heat-stress conditions (26 °C) via ChIP-seq were carried out. The results that revealed H3K9ac was an extensive epigenetic modulation in A. japonicus. The GO terms "regulation of transcription, DNA-templated" and "transcription coactivator activity" were significantly enriched in both groups. Particularly, various transcriptional factors (TFs) families showed notable modification of H3K9ac. Differentially acetylated regions (DARs) with H3K9ac modification under heat stress were identified with 24 hyperacetylated and 23 hypoacetylated peaks, respectively. We further examined the transcriptional expression for 13 genes with dysregulated H3K9ac level in the promoter regions by qRT-PCR. Combined H3K9ac ChIP-seq characteristics with the transcriptional expression, 5 up-up genes (ZCCHC3, RPA70, MTRR, β-Gal and PHTF2) and 2 down-down genes (PRPF39 and BSL78_10147) were identified. Surprisingly, the increasing mRNA expression of NECAP1 under heat stress was negatively related to the decreasing H3K9ac level in its promoter region. Our research is the first genome-wide characterization for the epigenetic modification H3K9ac in A. japonicus, and will help to advance the understanding of the roles of H3K9ac in transcriptional regulation under heat-stress condition.

Identification of SLITRK6 as a Novel Biomarker in hepatocellular carcinoma by comprehensive bioinformatic analysis

Hepatocellular carcinoma (HCC) is the most common primary malignancy of the adult liver and morbidity are increasing in recent years, however, there is still no effective strategy to prevent and diagnose HCC. Therefore, it is urgent to research the effective biomarker to predict clinical outcomes of HCC tumorigenesis. In the current study, differentially expressed genes in HCC and normal tissues were investigated using the Gene Expression Omnibus (GEO) dataset GSE144269 and The Cancer Genome Atlas (TCGA). Gene differential expression analysis and weighted correlation network analysis (WGCNA) methods were used to identify nine and 16 key gene modules from the GEO dataset and TCGA dataset, respectively, in which the green module in the GEO dataset and magenta module in TCGA were significantly correlated with HCC occurrence. Third, the enrichment score of gene function annotation results showed that these two key modules focus on the positive regulation of inflammatory response and cell differentiation, etc. Besides, PPI network analysis, mutation analysis, and survival analysis found that SLITRK6 had high connectivity, and its mutation significantly impacted overall survival. In addition, SLITRK6 was found to be low expressed in tumor cells. To summarize, SLITRK6 mutation was found to significantly affect the occurrence and prognosis of HCC. SLITRK6 was confirmed as a new potential gene target for HCC, which may provide a new theoretical basis for personalized diagnosis and chemotherapy of HCC in the future.

Molecular characterization of metabolic subtypes of gastric cancer based on metabolism-related lncRNA

Increasing evidence has demonstrated that lncRNAs are critical regulators in diverse biological processes, but the function of lncRNA in metabolic regulation remains largely unexplored. In this study, we evaluated the association between lncRNA and metabolic pathways and identified metabolism-related lncRNAs. Gastric cancer can be mainly subdivided into 2 clusters based on these metabolism-related lncRNA regulators. Comparative analysis shows that these subtypes are found to be highly consistent with previously identified subtypes based on other omics data. Functional enrichment analysis shows that they are enriched in distinct biological processes. Mutation analysis shows that ABCA13 is a protective factor in subtype C1 but a risk factor in C2. Analysis of chemotherapeutic and immunotherapeutic sensitivity shows that these subtypes tend to display distinct sensitivity to the same chemical drugs. In conclusion, these findings demonstrated the significance of lncRNA in metabolic regulation. These metabolism-related lncRNA regulators can improve our understanding of the underlying mechanism of lncRNAs and advance the research of immunotherapies in the clinical management of gastric cancer.

Tissue-based metabolomics reveals metabolic signatures and major metabolic pathways of gastric cancer with help of transcriptomic data from TCGA

PURPOSE

The aim of the present study was to screen differential metabolites of gastric cancer (GC) and identify the key metabolic pathways of GC.

METHODS

GC (n=28) and matched paracancerous (PC) tissues were collected, and LC-MS/MS analysis were performed to detect metabolites of GC and PC tissues. Metabolite pathways based on differential metabolites were enriched by MetaboAnalyst, and genes related to metabolite pathways were identified using the KEGGREST function of the R software package. Transcriptomics data from The Cancer Genome Atlas (TCGA) was analyzed to obtain differentially expressed genes (DEGs) of GC. Overlapping genes were acquired from metabonimics and transcriptomics data. Pathway enrichment analysis was performed using String. The protein expression of genes was validated by the Human Protein Atlas (HPA) database.

RESULTS

A total of 325 key metabolites were identified, 111 of which were differentially expressed between the GC and PC groups. Seven metabolite pathways enriched by MetaboAnalyst were chosen, and 361 genes were identified by KEGGREST. A total of 2831 DEGs were identified from the TCGA cohort. Of these, 1317 were down-regulated, and 1636 were up-regulated. Twenty-two overlapping genes were identified between genes related to metabolism and DEGs. Glycerophospholipid (GPL) metabolism is likely associated with GC, of which AGPAT9 and ETNPPL showed lower expressed in GC tissues.

CONCLUSIONS

We investigated the tissue-based metabolomics profile of GC, and several differential metabolites were identified. GPL metabolism may affect on progression of GC.

Integrated analysis identifies oxidative stress genes associated with progression and prognosis in gastric cancer

Oxidative stress (OS) reactions are reported to be associated with oncogenesis and tumor progression. However, little is known about the potential diagnostic value of OS in gastric cancer (GC). This study identified hub OS genes associated with the prognosis and progression of GC and illustrated the underlying mechanisms. The transcriptome data and corresponding GC clinical information were collected from The Cancer Genome Atlas (TCGA) database. Aberrantly expressed OS genes between tumors and adjacent normal tissues were screened, and 11 prognosis-associated genes were identified with a series of bioinformatic analyses and used to construct a prognostic model. These genes were validated in the Gene Expression Omnibus (GEO) database. Furthermore, weighted gene co-expression network analysis (WGCNA) was subsequently conducted to identify the most significant hub genes for the prediction of GC progression. Analysis revealed that a good prognostic model was constructed with a better diagnostic accuracy than other clinicopathological characteristics in both TCGA and GEO cohorts. The model was also significantly associated with the overall survival of patients with GC. Meanwhile, a nomogram based on the risk score was established, which displayed a favorable discriminating ability for GC. In the WGCNA analysis, 13 progression-associated hub OS genes were identified that were also significantly associated with the progression of GC. Furthermore, functional and gene ontology (GO) analyses were performed to reveal potential pathways enriched with these genes. These results provide novel insights into the potential applications of OS-associated genes in patients with GC.

Analysis of Cyclin-Dependent Kinase 1 as an Independent Prognostic Factor for Gastric Cancer Based on Statistical Methods

OBJECTIVE

The aim of this study was to investigate the expression of cyclin-dependent kinase 1 (CDK1) in gastric cancer (GC), evaluate its relationship with the clinicopathological features and prognosis of GC, and analyze the advantage of CDK1 as a potential independent prognostic factor for GC.

METHODS

The Cancer Genome Atlas (TCGA) data and corresponding clinical features of GC were collected. First, the aim gene was selected by combining five topological analysis methods, where the gene expression in paracancerous and GC tissues was analyzed by Limma package and Wilcox test. Second, the correlation between gene expression and clinical features was analyzed by logistic regression. Finally, the survival analysis was carried out by using the Kaplan-Meier. The gene prognostic value was evaluated by univariate and multivariate Cox analyses, and the gene potential biological function was explored by gene set enrichment analysis (GSEA).

RESULTS

CDK1 was selected as one of the most important genes associated with GC. The expression level of CDK1 in GC tissues was significantly higher than that in paracancerous tissues, which was significantly correlated with pathological stage and grade. The survival rate of the CDK1 high expression group was significantly lower than that of the low expression group. CDK1 expression was significantly correlated with overall survival (OS). CDK1 expression was mainly involved in prostate cancer, small cell lung cancer, and GC and was enriched in the WNT signaling pathway and T cell receptor signaling pathway.

CONCLUSION

CDK1 may serve as an independent prognostic factor for GC. It is also expected to be a new target for molecular targeted therapy of GC.