CDKN2B antisense RNA 1 suppresses tumor growth in human colorectal cancer by targeting MAPK inactivator dual-specificity phosphatase 1

Upregulation of hsa-miR-141-3p promotes uterine cervical carcinoma progression via targeting dual-specificity protein phosphatase 1

We aimed to explore the aberrant expression status of hsa-miR-141-3p and dual-specificity protein phosphatase 1 (DUSP1) and their relative mechanisms in uterine cervical carcinoma (UCC).Quantitative reverse transcription-polymerase chain reaction (RT-qPCR) was conducted to detect the expression of hsa-miR-141-3p. Immunohistochemical (IHC) staining was performed to examine the expression of DUSP1 in UCC. Gene chips and RNA-seq datasets were also obtained to assess the expression level. Integrated standardized mean difference (SMD) was calculated to evaluate the expression status of hsa-miR-141-3p in UCC tissues comprehensively. DUSP1-overexpression and hsa-miR-141-3p-inhibition HeLa cells were established, and CCK-8, transwell, wound healing, cell cycle, and apoptosis assays were implemented. The targets of hsa-miR-141-3p were obtained with online tools, and the combination of hsa-miR-141-3p and DUSP1 was validated via dual-luciferase reporter assay. Single-cell RNA-seq data were analyzed to explore hsa-miR-141-3p and DUSP1 in different cells. An integrated SMD of 1.41 (95% CI[0.45, 2.38], p = 0.0041) with 558 samples revealed the overexpression of hsa-miR-141-3p in UCC tissues. And the pooled SMD of -1.06 (95% CI[-1.45, -0.66], p < 0.0001) with 1,268 samples indicated the downregulation of DUSP1. Inhibition of hsa-miR-141-3p could upregulate DUSP1 expression and suppress invasiveness and metastasis of HeLa cells. Overexpression of DUSP1 could hamper proliferation, invasion, and migration and boost apoptosis and distribution of G1 phase. The dual-luciferase reporter assay validated the combination of hsa-miR-141-3p and DUSP1. Moreover, the targets of hsa-miR-141-3p were mainly enriched in the MAPK signaling pathway and activated in fibroblasts and endothelial cells. The current study illustrated the upregulation of hsa-miR-141-3p and the downregulation of DUSP1 in UCC tissues. Hsa-miR-141-3p could promote UCC progression by targeting DUSP1.

Colon cancer inhibitory properties of Caulerpa lentillifera polysaccharide and its molecular mechanisms based on three-dimensional cell culture model

Caulerpa lentillifera is rich in polysaccharides, and its polysaccharides show a significant effect in different biological activities including anti-cancer activity. As an edible algae-derived polysaccharide, exploring the role of colon cancer can better develop the application from a dietary therapy perspective. However, more in-depth studies of C. lentillifera polysaccharide on anti-colon cancer activity and mechanism are needed. In this study, we found that Caulerpa lentillifera polysaccharides (CLP) showed potential anti-colon cancer effect on human colon cancer cell HT29 in monolayer (IC50 = 1.954 mg/mL) and spheroid (IC50 = 0.402 mg/mL). Transcriptomics and metabolomics analyses revealed that CLP had an inhibitory effect on HT29 3D spheroid cells by activating aminoacyl-tRNA biosynthesis as well as arginine and proline metabolism pathways. Furthermore, the anti-colon cancer effects of CLP were confirmed through other human colon cancer cell HCT116 and LoVo in monolayer cells (IC50 = 1.890 mg/mL and 1.437 mg/mL, respectively) and 3D spheroid cells (IC50 = 0.344 mg/mL and 0.975 mg/mL, respectively), and three patient-derived organoids with IC50 values of 6.333-8.780 mg/mL. This study provided basic data for the potential application of CLP in adjuvant therapeutic food for colon cancer on multiple levels, while further investigation of detailed mechanism in vivo was still required.

Prediction of lncRNA and disease associations based on residual graph convolutional networks with attention mechanism

LncRNAs are non-coding RNAs with a length of more than 200 nucleotides. More and more evidence shows that lncRNAs are inextricably linked with diseases. To make up for the shortcomings of traditional methods, researchers began to collect relevant biological data in the database and used bioinformatics prediction tools to predict the associations between lncRNAs and diseases, which greatly improved the efficiency of the study. To improve the prediction accuracy of current methods, we propose a new lncRNA-disease associations prediction method with attention mechanism, called ResGCN-A. Firstly, we integrated lncRNA functional similarity, lncRNA Gaussian interaction profile kernel similarity, disease semantic similarity, and disease Gaussian interaction profile kernel similarity to obtain lncRNA comprehensive similarity and disease comprehensive similarity. Secondly, the residual graph convolutional network was used to extract the local features of lncRNAs and diseases. Thirdly, the new attention mechanism was used to assign the weight of the above features to further obtain the potential features of lncRNAs and diseases. Finally, the training set required by the Extra-Trees classifier was obtained by concatenating potential features, and the potential associations between lncRNAs and diseases were obtained by the trained Extra-Trees classifier. ResGCN-A combines the residual graph convolutional network with the attention mechanism to realize the local and global features fusion of lncRNA and diseases, which is beneficial to obtain more accurate features and improve the prediction accuracy. In the experiment, ResGCN-A was compared with five other methods through 5-fold cross-validation. The results show that the AUC value and AUPR value obtained by ResGCN-A are 0.9916 and 0.9951, which are superior to the other five methods. In addition, case studies and robustness evaluation have shown that ResGCN-A is an effective method for predicting lncRNA-disease associations. The source code for ResGCN-A will be available at https://github.com/Wangxiuxiun/ResGCN-A .

GCNFORMER: graph convolutional network and transformer for predicting lncRNA-disease associations

BACKGROUND

A growing body of researches indicate that the disrupted expression of long non-coding RNA (lncRNA) is linked to a range of human disorders. Therefore, the effective prediction of lncRNA-disease association (LDA) can not only suggest solutions to diagnose a condition but also save significant time and labor costs.

METHOD

In this work, we proposed a novel LDA predicting algorithm based on graph convolutional network and transformer, named GCNFORMER. Firstly, we integrated the intraclass similarity and interclass connections between miRNAs, lncRNAs and diseases, and built a graph adjacency matrix. Secondly, to completely obtain the features between various nodes, we employed a graph convolutional network for feature extraction. Finally, to obtain the global dependencies between inputs and outputs, we used a transformer encoder with a multiheaded attention mechanism to forecast lncRNA-disease associations.

RESULTS

The results of fivefold cross-validation experiment on the public dataset revealed that the AUC and AUPR of GCNFORMER achieved 0.9739 and 0.9812, respectively. We compared GCNFORMER with six advanced LDA prediction models, and the results indicated its superiority over the other six models. Furthermore, GCNFORMER's effectiveness in predicting potential LDAs is underscored by case studies on breast cancer, colon cancer and lung cancer.

CONCLUSIONS

The combination of graph convolutional network and transformer can effectively improve the performance of LDA prediction model and promote the in-depth development of this research filed.

Crosstalk between long non-coding RNAs and p53 signaling pathway in colorectal cancer: A review study

Colorectal cancer (CRC) is one of the most prevalent malignancies worldwide and the third leading cause of cancer-related fatalities. Long non-coding RNAs (lncRNAs) are key regulators of diverse physiological processes and are dysregulated in a wide range of pathophysiological circumstances such as CRC. Studies revealed that aberrant expressions of lncRNAs clearly modulate the expression level of p53 gene in CRC, thereby transactivating multiple downstream pathways. P53 is regarded as a crucial tumor suppressor gene which promotes cell-cycle arrest, DNA repair, senescence or apoptosis in response to cellular stresses. P53 is also mutated in CRC as well as various types of human malignancies. Therefore, lncRNAs interact with the p53 signaling pathway in numerus ways and significantly influence CRC-related processes. The current findings in the investigation of the crosstalk between lncRNAs and the P53 pathway in controlling CRC carcinogenesis, tumor progression, and therapeutic resistance are summarized in the this review. A deeper knowledge of CRC carcinogenesis may also have implications in CRC prevention and treatment through more research.

Comprehensive transcriptional analysis of pig facial skin development

Background

Skin development is a complex process that is influenced by many factors. Pig skin is used as an ideal material for xenografts because it is more anatomically and physiologically similar to human skin. It has been shown that the skin development of different pig breeds is different, and some Chinese pig breeds have the characteristics of skin thickness and facial skin folds, but the specific regulatory mechanism of this skin development is not yet clear.

Methods

In this study, the facial skin of Chenghua sows in the four developmental stages of postnatal Day 3 (D3) , Day 90 (D90) , Day 180 (D180), and Year 3 (Y3) were used as experimental materials, and RNA sequencing (RNA-seq) analysis was used to explore the changes in RNA expression in skin development at the four developmental stages, determine the differentially expressed messenger RNAs (mRNAs), long noncoding RNAs (lncRNAs), microRNAs (miRNAs), and circular RNAs (circRNAs), and perform functional analysis of related genes by Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses.

Results

A pairwise comparison of the four developmental stages identified several differentially expressed genes (DEGs) and found that the number of differentially expressed RNAs (DE RNAs) increased with increasing developmental time intervals. Elastin (ELN) is an important component of the skin. Its content affects the relaxation of the epidermis and dermal connection, and its expression is continuously downregulated during the four developmental stages. The functions of DEGs at different developmental stages were examined by performing GO and KEGG analyses, and the GO terms and enrichment pathways of mRNAs, lncRNAs, miRNAs, and circRNAs highly overlapped, among which the PPAR signaling pathway, a classical pathway for skin development, was enriched by DEGs of D3 vs. D180, D90 vs. D180 and D180 vs. Y3. In addition, we constructed lncRNA-miRNA-mRNA and circRNA-miRNA interaction networks and found genes that may be associated with skin development, but their interactions need further study.

Conclusions

We identified a number of genes associated with skin development, performed functional analyses on some important DEGs and constructed interaction networks that facilitate further studies of skin development.

Construction of gene subgroups of Crohn disease based on transcriptome data

BACKGROUND

The global prevalence of Crohn disease (CD), a chronic inflammatory disease of the intestine, has been increasing; however, the etiology and pathogenesis of this disease have not been fully elucidated. Therefore, in the present study, we aimed to better understand the molecular mechanisms underlying CD to aid the development of novel therapeutic strategies for this condition.

METHODS

Based on the transcriptome data from patients with CD, this study used an unsupervised learning method to construct gene co-expression molecular subgroups and the R and SPSS software to identify the biological, clinical, and genetic characteristics and signatures of each subgroup.

RESULTS

Two subgroups were analyzed. Compared to subgroup II, subgroup I consisted of older patients with a more limited range of disease presentation and had a higher number of smokers. The specific genes associated with this subgroup, including CDKN2B, solute carrier family 22 member 5, and phytanoyl-CoA 2-hydroxylase, can be targeted for managing intestinal dysbacteriosis. The number of patients showing infiltrating lesions was higher, the number of smokers was lower, and CD severity was worse in patients in subgroup II than those in subgroup I. The specific genes relevant to subgroup II included cluster of differentiation 44, tryptophanyl-tRNA synthetase, and interleukin 10 receptor, alpha subunit, which may be related to viral infection.

CONCLUSION

The present study segregated patients with CD into 2 subgroups; the findings reported herein provide a new theoretical basis for the diagnosis and treatment of CD and could aid a thorough identification of potential therapeutic targets for this disease.

MicroRNA-375 restrains the progression of lung squamous cell carcinoma by modulating the ERK pathway via UBE3A-mediated DUSP1 degradation

MiRNA-375 has been reported to play critical roles in a variety of cancers. To unravel its biological roles, especially its specific mechanisms of action in lung squamous cell carcinoma (LUSC), LUSC tissue microarrays and miRNAscope were performed to identify the miR-375 expression. Associations with clinicopathologic features, survival, and the prognostic value of miR-375 in LUSC were clarified in a retrospective study of 90 pairs of LUSC tissues. In vitro and in vivo gain- and loss-of-function assays were conducted to validate the effects and mechanism of miR-375 in LUSC. The mechanism responsible for interactions was verified by dual-luciferase reporter gene assay, immunoprecipitation (IP) analysis, immunofluorescence (IF) assay and ubiquitination assay. We found that miR-375 had higher expression in noncancerous adjacent tissues than in LUSC tissues. Clinicopathologic analyses showed that miR-375 expression was correlated with pathologic stage and was an independent predictor of overall survival (OS) for LUSC. MiR-375, as a tumor inhibitor, inhibited proliferation and metastasis while promoting apoptosis of LUSC cells. Mechanistic research indicated that miR-375 targeted ubiquitin-protein ligase E3A (UBE3A), which in turn promoted the activity of the ERK signaling pathway via ubiquitin-mediated dual-specificity protein phosphatase 1 (DUSP1) degradation. Collectively, we propose a novel mechanism of tumorigenesis and metastasis of LUSC via the miR-375/UBE3A/DUSP1/ERK axis, which could potentially facilitate new strategies for the treatment of LUSC.

Comprehensive Analysis of Cellular Senescence-Related Genes in Prognosis, Molecular Characterization and Immunotherapy of Hepatocellular Carcinoma

BACKGROUND

Cellular senescence is a tumor suppressive response in which the cell cycle is in a state of permanent arrest and can inhibit tumor cell proliferation. In recent years, induction of cellular senescence has been shown to be important for antitumor therapy, and the link between cellular senescence and clinical prognosis and immunotherapy of hepatocellular carcinoma is still unknown.

METHODS

We performed enrichment analysis of genes in three cellular senescence gene sets, screened for gene sets significantly enriched in hepatocellular carcinoma and extracted genes from them. Signature were constructed using senescence-related genes, and their expression was verified at the protein and RNA levels. Survival, clinical staging and grading, immune infiltration, immunotherapy, and drug sensitivity were also analyzed between risk groups.

RESULTS

The q-PCR and immunohistochemistry results revealed significant differences in the expression of the signature genes between normal and tumor tissues. Significant differences in clinicopathological features, prognosis and immune infiltration were observed between risk groups. In the low-risk group, better OS and lower TMB scores were demonstrated, while the high-risk group had higher immune checkpoint expression, as well as lower risk of immune escape. In addition, we found that the High-risk group was more sensitive to sorafenib.

CONCLUSION

In summary, the signature constructed using aging-related genes can reliably predict patient prognosis and immunotherapy efficacy, providing a new idea for immune system therapy of hepatocellular carcinoma.

NcPath: a novel platform for visualization and enrichment analysis of human non-coding RNA and KEGG signaling pathways

SUMMARY

Non-coding RNAs play important roles in transcriptional processes and participate in the regulation of various biological functions, in particular miRNAs and lncRNAs. Despite their importance for several biological functions, the existing signaling pathway databases do not include information on miRNA and lncRNA. Here, we redesigned a novel pathway database named NcPath by integrating and visualizing a total of 178 308 human experimentally validated miRNA-target interactions (MTIs), 32 282 experimentally verified lncRNA-target interactions (LTIs) and 4837 experimentally validated human ceRNA networks across 222 KEGG pathways (including 27 sub-categories). To expand the application potential of the redesigned NcPath database, we identified 556 798 reliable lncRNA-protein-coding genes (PCG) interaction pairs by integrating co-expression relations, ceRNA relations, co-TF-binding interactions, co-histone-modification interactions, cis-regulation relations and lncPro Tool predictions between lncRNAs and PCG. In addition, to determine the pathways in which miRNA/lncRNA targets are involved, we performed a KEGG enrichment analysis using a hypergeometric test. The NcPath database also provides information on MTIs/LTIs/ceRNA networks, PubMed IDs, gene annotations and the experimental verification method used. In summary, the NcPath database will serve as an important and continually updated platform that provides annotation and visualization of the pathways on which non-coding RNAs (miRNA and lncRNA) are involved, and provide support to multimodal non-coding RNAs enrichment analysis. The NcPath database is freely accessible at http://ncpath.pianlab.cn/.

AVAILABILITY AND IMPLEMENTATION

NcPath database is freely available at http://ncpath.pianlab.cn/. The code and manual to use NcPath can be found at https://github.com/Marscolono/NcPath/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Predicting prognosis and immunotherapeutic response of clear cell renal cell carcinoma

Immune checkpoint inhibitors have emerged as a novel therapeutic strategy for many different tumors, including clear cell renal cell carcinoma (ccRCC). However, these drugs are only effective in some ccRCC patients, and can produce a wide range of immune-related adverse reactions. Previous studies have found that ccRCC is different from other tumors, and common biomarkers such as tumor mutational burden, HLA type, and degree of immunological infiltration cannot predict the response of ccRCC to immunotherapy. Therefore, it is necessary to further research and construct corresponding clinical prediction models to predict the efficacy of Immune checkpoint inhibitors. We integrated PBRM1 mutation data, transcriptome data, endogenous retrovirus data, and gene copy number data from 123 patients with advanced ccRCC who participated in prospective clinical trials of PD-1 inhibitors (including CheckMate 009, CheckMate 010, and CheckMate 025 trials). We used AI to optimize mutation data interpretation and established clinical prediction models for survival (for overall survival AUC: 0.931; for progression-free survival AUC: 0.795) and response (ORR AUC: 0.763) to immunotherapy of ccRCC. The models were internally validated by bootstrap. Well-fitted calibration curves were also generated for the nomogram models. Our models showed good performance in predicting survival and response to immunotherapy of ccRCC.

An integrative transcriptome analysis reveals potential predictive, prognostic biomarkers and therapeutic targets in colorectal cancer

BACKGROUND

A deep understanding of potential molecular biomarkers and therapeutic targets related to the progression of colorectal cancer (CRC) from early stages to metastasis remain mostly undone. Moreover, the regulation and crosstalk among different cancer-driving molecules including messenger RNAs (mRNAs), long non-coding RNAs (lncRNAs) and micro-RNAs (miRNAs) in the transition from stage I to stage IV remain to be clarified, which is the aim of this study.

METHODS

We carried out two separate differential expression analyses for two different sets of samples (stage-specific samples and tumor/normal samples). Then, by the means of robust dataset analysis we identified distinct lists of differently expressed genes (DEGs) for Robust Rank Aggregation (RRA) and weighted gene co-expression network analysis (WGCNA). Then, comprehensive computational systems biology analyses including mRNA-miRNA-lncRNA regulatory network, survival analysis and machine learning algorithms were also employed to achieve the aim of this study. Finally, we used clinical samples to carry out validation of a potential and novel target in CRC.

RESULTS

We have identified the most significant stage-specific DEGs by combining distinct results from RRA and WGCNA. After finding stage-specific DEGs, a total number of 37 DEGs were identified to be conserved across all stages of CRC (conserved DEGs). We also found DE-miRNAs and DE-lncRNAs highly associated to these conserved DEGs. Our systems biology approach led to the identification of several potential therapeutic targets, predictive and prognostic biomarkers, of which lncRNA LINC00974 shown as an important and novel biomarker.

CONCLUSIONS

Findings of the present study provide new insight into CRC pathogenesis across all stages, and suggests future assessment of the functional role of lncRNA LINC00974 in the development of CRC.