DNMBP-AS1 Regulates NHLRC3 Expression by Sponging miR-93-5p/17-5p to Inhibit Colon Cancer Progression

Occlusion enhanced pan-cancer classification via deep learning

Quantitative measurement of RNA expression levels through RNA-Seq is an ideal replacement for conventional cancer diagnosis via microscope examination. Currently, cancer-related RNA-Seq studies focus on two aspects: classifying the status and tissue of origin of a sample and discovering marker genes. Existing studies typically identify marker genes by statistically comparing healthy and cancer samples. However, this approach overlooks marker genes with low expression level differences and may be influenced by experimental results. This paper introduces "GENESO," a novel framework for pan-cancer classification and marker gene discovery using the occlusion method in conjunction with deep learning. we first trained a baseline deep LSTM neural network capable of distinguishing the origins and statuses of samples utilizing RNA-Seq data. Then, we propose a novel marker gene discovery method called "Symmetrical Occlusion (SO)". It collaborates with the baseline LSTM network, mimicking the "gain of function" and "loss of function" of genes to evaluate their importance in pan-cancer classification quantitatively. By identifying the genes of utmost importance, we then isolate them to train new neural networks, resulting in higher-performance LSTM models that utilize only a reduced set of highly relevant genes. The baseline neural network achieves an impressive validation accuracy of 96.59% in pan-cancer classification. With the help of SO, the accuracy of the second network reaches 98.30%, while using 67% fewer genes. Notably, our method excels in identifying marker genes that are not differentially expressed. Moreover, we assessed the feasibility of our method using single-cell RNA-Seq data, employing known marker genes as a validation test.

High expression of NXPH4 correlates with poor prognosis, metabolic reprogramming, and immune infiltration in colon adenocarcinoma

Background

Colon adenocarcinoma (COAD) is a prevalent gastrointestinal malignant disease with high mortality rate, and identification of novel prognostic biomarkers and therapeutic targets is urgently needed. Although neurexophilin 4 (NXPH4) has been investigated in several tumors, its role in COAD remains unclear. The aim of this study was to explore the prognostic value and potential functions of NXPH4 in COAD.

Methods

The expression of NXPH4 in COAD were analyzed using The Cancer Genome Atlas (TCGA) and datasets from the Gene Expression Omnibus (GEO) database. The prognostic value of NXPH4 was determined using Kaplan-Meier analysis and Cox regression analysis. To investigate the possible mechanism underlying the role of NXPH4 in COAD, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were employed. The correlation between NXPH4 expression and immune cell infiltration levels was examined thorough single-sample gene set enrichment analysis (ssGSEA). Furthermore, the competing endogenous RNA (ceRNA) regulatory network that may be involved in NXPH4 in COAD was predicted and constructed through a variety of databases.

Results

NXPH4 expression was significantly higher in COAD tissue compared with normal colon tissues. Meanwhile, high expression of NXPH4 was associated with poor prognosis in COAD patients. GO-KEGG and GSEA analyses indicated that NXPH4 was associated with glycolysis and hypoxia pathway, and may promote COAD progression and metastasis by modulating metabolic reprogramming. ssGSEA analysis demonstrated that NXPH4 expression also associated with immune infiltration. Furthermore, we identified various microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) as upstream regulators of NXPH4 in COAD.

Conclusions

The present study revealed that high expression of NXPH4 is associated with tumor progression, metabolic reprogramming, and immune infiltration. These findings suggest that NXPH4 could serve as a reliable prognostic biomarker and a promising therapeutic target in COAD.

Downregulation of lncRNA EPB41L4A-AS1 promotes gastric cancer cell proliferation, migration and invasion

BACKGROUND

The incidence of gastric cancer ranks the first among digestive tract tumors in China. However, there are no specific symptoms in the early stage of the tumor and the diagnosis process is complex, so more effective detection methods are very needed. In this study, a novel long noncoding RNA (lncRNA) was introduced as a diagnostic biomarker for gastric cancer, which brought new thinking to the exploration of its pathological mechanism and clinical prediction.

METHODS

The level of lncRNA EPB41L4A-AS1 (EPB41L4A-AS1) in gastric cancer serum and cells was verified via real-time quantitative polymerase chain reaction (RT-qPCR). Receiver operating characteristic (ROC) curve was performed based on the EPB41L4A-AS1 level, and the diagnostic possibility of EPB41L4A-AS was analyzed. The chi-square test evaluated the correlation between EPB41L4A-AS expression and clinical information. The cells were cultured and transfected in vitro, and the mediations of abnormal EPB41L4A-AS level on the viability and motility of gastric cancer cells were verified through cell counting kit-8 (CCK-8) and Transwell assay. Furthermore, luciferase activity assay was performed to confirm the sponge molecule microRNA-17-5p (miR-17-5p) of EPB41L4A-AS1.

RESULTS

EPB41L4A-AS1 was decreased in gastric cancer, and low EPB41L4A-AS1 level indicated resultful diagnostic value. Overexpression of EPB41L4A-AS1 inhibited the activity of gastric cancer cells, while knockdown of EPB41L4A-AS1 promoted tumor deterioration. EPB41L4A-AS1 directly targeted and regulated the expression ofmiR-17-5p.

CONCLUSION

This study elaborated that EPB41L4A-AS1 is lowly expressed in gastric cancer. Silencing EPB41L4A-AS1 was beneficial to cell proliferation, migration, and invasion. EPB41L4A-AS1 provides a new possibility for the diagnosis of gastric cancer patients by targeting miR-17-5p.

Identification of m6A/m5C/m1A-associated LncRNAs for prognostic assessment and immunotherapy in pancreatic cancer

Methylation of RNA plays an important role in cancer. Classical forms of such modifications include N6-methyladenine (m6A), 5-methylcytosine (m5C), and N1-methyladenine (m1A). Methylation-regulated long non-coding (lnc) RNAs are involved in various biological processes, such as tumor proliferation, apoptosis, immune escape, invasion, and metastasis. Therefore, we performed an analysis of transcriptomic and clinical data of pancreatic cancer samples in The Cancer Genome Atlas (TCGA). Using the co-expression method, we summarized 44 m6A/m5C/m1A-related genes and obtained 218 methylation-associated lncRNAs. Next, with COX regression, we screened 39 lncRNAs that are strongly associated with prognosis and found that their expression differed significantly between normal tissues and pancreatic cancer samples (P < 0.001). We then used the least absolute shrinkage and selection operator (LASSO) to construct a risk model comprising seven lncRNAs. In validation set, the nomogram generated by combining clinical characteristics accurately predicted the survival probability of pancreatic cancer patients at 1, 2, and 3 years after diagnosis (AUC = 0.652, 0.686, and 0.740, respectively). Tumor microenvironment analysis showed that the high-risk group had significantly more resting memory CD4 T cells, M0 macrophages, and activated dendritic cells and fewer naïve B cells, plasma cells, and CD8 T cells than the low-risk group (both P < 0.05). Most immune-checkpoint genes were significantly different between the high- and low-risk groups (P < 0.05). The Tumor Immune Dysfunction and Exclusion score showed that high-risk patients benefited more from treatment with immune checkpoint inhibitors (P < 0.001). Overall survival was also lower in high-risk patients with more tumor mutations than in low-risk patients with fewer mutations (P < 0.001). Finally, we explored the sensitivity of the high- and low-risk groups to seven candidate drugs. Our findings indicated that m6A/m5C/m1A-associated lncRNAs are potentially useful biomarkers for the early diagnosis and estimating the prognosis of, and ascertaining the responses to immunotherapy in, patients with pancreatic cancer.

HAMP as a Prognostic Biomarker for Colorectal Cancer Based on Tumor Microenvironment Analysis

Colorectal cancer (CRC) is the most common digestive tumor in the world and has a high mortality rate. The development and treatment of CRC are related to the immune microenvironment, but immune response-related prognostic biomarkers are lacking. In this study, we used The Cancer Genome Atlas (TCGA) to explore the tumor microenvironment (TME) and weighted gene coexpression network analysis (WGCNA) to identify significant prognostic genes. We also identified differentially expressed genes in the TCGA data and explored immune-related genes and transcription factors (TFs). Then, we built a TF regulatory network and performed a comprehensive prognostic analysis of an lncRNA-associated competitive endogenous RNA network (ceRNA network) to build a prognostic model. CCR8 and HAMP were identified both in the WGCNA key module and as immune-related genes. HAMP had good prognostic value for CRC and was highly expressed in CRC tissues and had a negative correlation with CD4⁺ T cells and M0 macrophages based on immunohistochemistry and immunofluorescence staining of clinical specimens.We found that HAMP had high prognostic and therapeutic target value for CRC and was associated with liver metastasis. These analysis results revealed that HAMP may be a candidate immune-related prognostic biomarker for CRC.

Prognostic Role of M6A-Associated Immune Genes and Cluster-Related Tumor Microenvironment Analysis: A Multi-Omics Practice in Stomach Adenocarcinoma

N6-methylandrostenedione (m6A) methylation plays a very important role in the development of malignant tumors. The immune system is the key point in the progression of tumors, particularly in terms of tumor treatment and drug resistance. Tumor immunotherapy has now become a hot spot and a new approach for tumor treatment. However, as far as the stomach adenocarcinoma (STAD) is concerned, the in-depth research is still a gap in the m6A-associated immune markers. The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases is extremely important for our research, where we obtained gene mutation, gene expression data and relevant clinical information of STAD patients. Firstly, the samples from GEO were used as external validation groups, while the TCGA samples were divided into a training group and an internal validation group randomly. Using the way of Single factor COX-LASSO- and multi-factor Cox to construct the prognostic model. Then, all samples were subjected to cluster analysis to generate high and low expression groups of immune gene. Meanwhile, we also collected the correlation between these types and tumor microenvironment. On this basis, a web version of the dynamic nomogram APP was developed. In addition, we performed microenvironmental correlation, copy number variation and mutation analyses for model genes. The prognostic model for STAD developed here demonstrated a very strong predictive ability. The results of cluster analysis manifested that the immune gene low expression group had lower survival rate and higher degree of immune infiltration. Therefore, the immune gene low expression group was associated with lower survival rates and a higher degree of immune infiltration. Gene set enrichment analysis suggested that the potential mechanism might be related to the activation of immunosuppressive functions and multiple signaling pathways. Correspondingly, the web version of the dynamic nomogram APP produced by the DynNom package has successfully achieved rapid and accurate calculation of patient survival rates. Finally, the multi-omics analysis of model genes further enriched the research content. Interference of RAB19 was confirmed to facilitate migration of STAD cells in vitro, while its overexpression inhibited these features. The prognostic model for STAD constructed in this study is accurate and efficient based on multi-omics analysis and experimental validation. Additionally, the results of the correlation analysis between the tumor microenvironment and m6Ascore are the basics of further exploration of the pathophysiological mechanism in STAD.