LINC00957 Acted as Prognostic Marker Was Associated With Fluorouracil Resistance in Human Colorectal Cancer

Dysregulation of long non-coding RNA gene expression pathways in monocytes of type 2 diabetes patients with cardiovascular disease

BACKGROUND

Monocytes play a central role in the pathophysiology of cardiovascular complications in type 2 diabetes (T2D) patients through different mechanisms. We investigated diabetes-induced changes in lncRNA genes from T2D patients with cardiovascular disease (CVD), long-duration diabetes, and poor glycemic control.

METHODS

We performed paired-end RNA sequencing of monocytes from 37 non-diabetes controls and 120 patients with T2D, of whom 86 had either macro or microvascular disease or both. Monocytes were sorted from peripheral blood using flow cytometry; their RNA was purified and sequenced. Alignments and gene counts were obtained with STAR to reference GRCh38 using Gencode (v41) annotations followed by batch correction with CombatSeq. Differential expression analysis was performed with EdgeR and pathway analysis with IPA software focusing on differentially expressed genes (DEGs) with a p-value < 0.05. Additionally, differential co-expression analysis was done with csdR to identify lncRNAs highly associated with diabetes-related expression networks with network centrality scores computed with Igraph and network visualization with Cytoscape.

RESULTS

Comparing T2D vs. non-T2D, we found two significantly upregulated lncRNAs (ENSG00000287255, FDR = 0.017 and ENSG00000289424, FDR = 0.048) and one significantly downregulated lncRNA (ENSG00000276603, FDR = 0.017). Pathway analysis on DEGs revealed networks affecting cellular movement, growth, and development. Co-expression analysis revealed ENSG00000225822 (UBXN7-AS1) as the highest-scoring diabetes network-associated lncRNA. Analysis within T2D patients and CVD revealed one lncRNA upregulated in monocytes from patients with microvascular disease without clinically documented macrovascular disease. (ENSG00000261654, FDR = 0.046). Pathway analysis revealed DEGs involved in networks affecting metabolic and cardiovascular pathologies. Co-expression analysis identified lncRNAs strongly associated with diabetes networks, including ENSG0000028654, ENSG00000261326 (LINC01355), ENSG00000260135 (MMP2-AS1), ENSG00000262097, and ENSG00000241560 (ZBTB20-AS1) when we combined the results from all patients with CVD. Similarly, we identified from co-expression analysis of diabetes patients with a duration ≥ 10 years vs. <10 years two lncRNAs: ENSG00000269019 (HOMER3-AS10) and ENSG00000212719 (LINC02693). The comparison of patients with good vs. poor glycemic control also identified two lncRNAs: ENSG00000245164 (LINC00861) and ENSG00000286313.

CONCLUSION

We identified dysregulated diabetes-related genes and pathways in monocytes of diabetes patients with cardiovascular complications, including lncRNA genes of unknown function strongly associated with networks of known diabetes genes.

The role of the Notch signaling pathway in bacterial infectious diseases

The Notch signaling pathway is the most crucial link in the normal operation and maintenance of physiological functions of mammalian life processes. Notch receptors interact with ligands and this leads to three cleavages and goes on to enter the nucleus to initiate the transcription of target genes. The Notch signaling pathway deeply participates in the differentiation and function of various cells, including immune cells. Recent studies indicate that the outcomes of Notch signaling are changeable and highly dependent on different bacterial infection. The Notch signaling pathway plays a different role in promoting and inhibiting bacterial infection. In this review, we focus on the latest research findings of the Notch signaling pathway in bacterial infectious diseases. The Notch signaling pathway is critically involved in a variety of development processes of immunosuppression of different APCs. The Notch signaling pathway leads to functional changes in epithelial cells to aggravate tissue damage. Specifically, we illustrate the regulatory mechanism of the Notch signaling pathway in various bacterial infections, such as Mycobacterium tuberculosis, Mycobacterium avium paratuberculosis, Mycobacterium leprae, Helicobacter pylori, Klebsiella pneumoniae, Bacillus subtilis, Staphylococcus aureus, Ehrlichia chaffeensis and sepsis. Collectively, this review will not only help beginners intuitively and systematically understand the Notch signaling pathway in bacterial infectious diseases but also help experts to generate fresh insight in this field.

LncRNAs in colorectal cancer: Biomarkers to therapeutic targets

Colorectal cancer (CRC) is the third leading cause of cancer-related death in men and women worldwide. As early detection is associated with lower mortality, novel biomarkers are urgently needed for timely diagnosis and appropriate management of patients to achieve the best therapeutic response. Long noncoding RNAs (lncRNAs) have been reported to play essential roles in CRC progression. Accordingly, the regulatory roles of lncRNAs should be better understood in general and for identifying diagnostic, prognostic and predictive biomarkers in CRC specifically. In this review, the latest advances on the potential diagnostic and prognostic lncRNAs as biomarkers in CRC samples were highlighted, Current knowledge on dysregulated lncRNAs and their potential molecular mechanisms were summarized. The potential therapeutic implications and challenges for future and ongoing research in the field were also discussed. Finally, novel insights on the underlying mechanisms of lncRNAs were examined as to their potential role as biomarkers and therapeutic targets in CRC. This review may be used to design future studies and advanced investigations on lncRNAs as biomarkers for the diagnosis, prognosis and therapy in CRC.

Identification of Autophagy-Related LncRNA to Predict the Prognosis of Colorectal Cancer

Objective: To establish a prediction model based on autophagy-related lncRNAs and investigate the functional enrichment of autophagy-related lncRNAs in colorectal cancer.

Methods: TCGA database was used to extract the transcriptome data and clinical features of colorectal cancer patients. HADb was used to obtain autophagy-related genes. Pearson correlation analysis was performed to identify autophagy-related lncRNAs. The autophagy-related lncRNAs with prognostic values were selected. Based on the selected lncRNAs, the risk score model and nomogram were constructed, respectively. Calibration curve, concordance index, and ROC curve were performed to evaluate the predictive efficacy of the prediction model. GSEA was performed to figure out the functional enrichment of autophagy-related lncRNAs.

Results: A total of 13413 lncRNAs and 938 autophagy-related genes were obtained. A total of 709 autophagy-related genes were identified in colon cancer tissues, and 11 autophagy-related lncRNAs (AL138756.1, LINC01063, CD27-AS1, LINC00957, EIF3J-DT, LINC02474, SNHG16, AC105219.1, AC068580.3, LINC02381, and LINC01011) were finally selected and set as prognosis-related lncRNAs. According to the risk score, patients were divided into the high-risk and low-risk groups, respectively. The survival K–M (Kaplan–Meier) curve showed the low-risk group exhibits better overall survival than the high-risk group. The AUCs under the ROC curves were 0.72, 0.814, and 0.83 at 1, 3, and 5 years, respectively. The C-index (concordance index) of the model was 0.814. The calibration curves at 1, 3, and 5 years showed the predicting values were consistent with the actual values. Functional enrichment analysis showed that autophagy-related lncRNAs were enriched in several pathways.

Conclusions: A total of 11 specific autophagy-related lncRNAs were identified to own prognostic value in colon cancer. The predicting model based on the lncRNAs and clinical features can effectively predict the OS. Furthermore, functional enrichment analysis showed that autophagy-related genes were enriched in various biological pathways.

The role of multiple metabolic genes in predicting the overall survival of colorectal cancer: A study based on TCGA and GEO databases

The recent advances in gene chip technology have led to the identification of multiple metabolism-related genes that are closely associated with colorectal cancer (CRC). Nevertheless, none of these genes could accurately diagnose or predict CRC. The prognosis of CRC has been made by previous prognostic models constructed by using multiple genes, however, the predictive function of multi-gene prognostic models using metabolic genes for the CRC prognosis remains unexplored. In this study, we used the TCGA-CRC cohort as the test dataset and the GSE39582 cohort as the experimental dataset. Firstly, we constructed a prognostic model using metabolic genes from the TCGA-CRC cohort, which were also associated with CRC prognosis. We analyzed the advantages of the prognostic model in the prognosis of CRC and its regulatory mechanism of the genes associated with the model. Secondly, the outcome of the TCGA-CRC cohort analysis was validated using the GSE39582 cohort. We found that the prognostic model can be employed as an independent prognostic risk factor for estimating the CRC survival rate. Besides, compared with traditional clinical pathology, it can precisely predict CRC prognosis as well. The high-risk group of the prognostic model showed a substantially lower survival rate as compared to the low-risk group. In addition, gene enrichment analysis of metabolic genes showed that genes in the prognostic model are enriched in metabolism and cancer-related pathways, which may explain its underlying mechanism. Our study identified a novel metabolic profile containing 11 genes for prognostic prediction of CRC. The prognostic model may unravel the imbalanced metabolic microenvironment, and it might promote the development of biomarkers for predicting treatment response and streamlining metabolic therapy in CRC.

Unraveling Immune-Related lncRNAs in Breast Cancer Molecular Subtypes

Breast cancer (BRCA) is the most leading cause of cancer worldwide. It is a heterogeneous disease with at least five molecular subtypes including luminal A, luminal B, basal-like, HER2-enriched, and normal-like. These five molecular subtypes are usually stratified according to their mRNA profile patterns; however, ncRNAs are increasingly being used for this purpose. Among the ncRNAs class, the long non-coding RNAs (lncRNAs) are molecules with more than 200 nucleotides with versatile regulatory roles; and high tissue-specific expression profiles. The heterogeneity of BRCA can also be reflected regarding tumor microenvironment immune cells composition, which can directly impact a patient's prognosis and therapy response. Using BRCA immunogenomics data from a previous study, we propose here a bioinformatics approach to include lncRNAs complexity in BRCA molecular and immune subtype. RNA-seq data from The Cancer Genome Atlas (TCGA) BRCA cohort was analyzed, and signal-to-noise ratio metrics were applied to create these subtype-specific signatures. Five immune-related signatures were generated with approximately ten specific lncRNAs, which were then functionally analyzed using GSEA enrichment and survival analysis. We highlighted here some lncRNAs in each subtype. LINC01871 is related to immune response activation and favorable overall survival in basal-like samples; EBLN3P is related to immune response suppression and progression in luminal B, MEG3, XXYLT1-AS2, and LINC02613 were related with immune response activation in luminal A, HER2-enriched and normal-like subtypes, respectively. In this way, we emphasize the need to know better the role of lncRNAs as regulators of immune response to provide new perspectives regarding diagnosis, prognosis and therapeutical targets in BRCA molecular subtypes.

Long non-coding RNAs in colorectal cancer: Novel oncogenic mechanisms and promising clinical applications

Colorectal cancer (CRC) is the third most common malignancy and ranks as the second leading cause of cancer-related deaths worldwide. Despite the improvements in CRC diagnosis and treatment approaches, a considerable proportion of CRC patients still suffers from poor prognosis due to late disease detections and lack of personalized disease managements. Recent evidences have not only provided important molecular insights into their mechanistic behaviors but also indicated that identification of cancer-specific long non-coding RNAs (LncRNAs) could benefit earlier disease detections and improve treatment outcomes in patients suffering from CRC. LncRNAs have raised extensive attentions as they participate in various hallmarks of CRC. The mechanistic evidence gleaned in the recent decade clearly reveals that lncRNAs exert their oncogenic roles by regulating autophagy, epigenetic modifications, enhancing stem phenotype and modifying tumor microenvironment. In view of their pleiotropic functional roles in malignant progression, and their frequently dysregulated expression in CRC patients, they have great potential to be reliable diagnostic and prognostic biomarkers, as well as therapeutic targets for CRC. In the present review, we will focus on the oncogenic roles of lncRNAs and related mechanisms in CRC as well as discuss their clinical potential in the early diagnosis, prognostic prediction and therapeutic translation in patients with this malignancy.

Identifying biomolecules and constructing a prognostic risk prediction model for recurrence in osteosarcoma

INTRODUCTION

Osteosarcoma is a high-morbidity bone cancer with an unsatisfactory prognosis. The aim of this study is to develop novel potential prognostic biomarkers and construct a prognostic risk prediction model for recurrence in osteosarcoma.

METHODS

By analyzing microarray data, univariate and multivariate Cox regression analyses were performed to screen prognostic RNA signatures and to build a prognostic model. The RNA signatures were validated using Kaplan-Meier curves. Then, we developed and validated a nomogram combining age, recurrence, metastatic, and Prognostic score (PS) models to predict the individual's overall survival at the 3- and 5-year points. Pathway enrichment of RNA was conducted based on the significant co-expressed RNAs.

RESULTS

A total of 319 mRNAs and 14 lncRNAs were identified in the microarray data. One lncRNA (LINC00957) and six mRNAs (METL1, CA9, B3GALT4, ALDH1A1, LAMB3, and ITGB4) were identified as RNA signatures and showed good performances in survival prediction for both the training and validation cohorts. Cox regression analysis showed that the seven RNA signatures could independently predict overall survival. Furthermore, age, recurrence, metastatic, and PS models were identified as independent prognostic factors via univariate and multivariate Cox analyses (P < 0.05) and included in the prognostic nomogram. The C-index values for the 3- and 5-year overall survival predictions of the nomogram were 0.809 and 0.740, respectively.

CONCLUSIONS

The current study provides the novel potential of seven RNA candidates as prognostic biomarkers. Nomograms were constructed to provide accurate and individualized survival prediction for recurrence in osteosarcoma patients.

Upregulation of Long Non-Coding RNA GCC2-AS1 Facilitates Malignant Phenotypes and Correlated With Unfavorable Prognosis for Lung Adenocarcinoma

Background

The role played by long noncoding RNA GCC2-AS1 in primary malignant tumors remains poorly understood. This study aimed to determine the expression levels and evaluate the clinical significance and biological effects of GCC2-AS1 in lung adenocarcinoma (LUAD).

Methods

We used data obtained from tissue samples and the TCGA database to determine the levels of GCC2-AS1 expression LUAD patients, and the prognostic value of those levels. Functional experiments were performed to investigate the effect of GCC2-AS1 on LUAD cells. Genes that were differentially expressed in GCC2-AS1-low and -high groups were analyzed by an enrichment analysis. Additionally, a nomogram model was created and subgroup analyses were performed to further determine the prognostic value of GCC2-AS1 in LUAD.

Results

GCC2-AS1 expression was significantly upregulated in lung adenocarcinoma tissues as compared with normal tissues. Depletion of GCC2-AS1 inhibited the proliferation and invasion of LUAD cells in vitro. An elevated level of GCC2-AS1 was strongly correlated with shorter overall survival time and was identified as an independent prognostic marker for LUAD patients. Enrichment analyses conducted using GO, KEGG, and GSEA databases were performed to identify biological pathways that might involve GCC2-AS1. Several subgroups were found to have a significant prognostic value for patients in the GCC2-AS1-low and -high groups.

Conclusions

Our findings suggest that GCC2-AS1 can serve as a diagnostic and prognostic biomarker for LUAD patients.

Identification of Epithelial-Mesenchymal Transition-Related lncRNA With Prognosis and Molecular Subtypes in Clear Cell Renal Cell Carcinoma

Epithelial–mesenchymal transition (EMT), a reversible cellular program, is critically important in tumor progression and is regulated by a family of transcription factors, induction factors, and an array of signaling pathway genes. The prognostic role and biological functions of EMT-related lncRNAs in ccRCC are largely unknown. In the present study, we analyzed the gene expression data and clinical information retrieved from The Cancer Genome Atlas (TCGA) database (N=512) and International Cancer Genome Consortium (ICGC) database (N=90) which served as training and external validation dataset, respectively. Then, we constructed an EMT-related lncRNA risk signature based on the comprehensive analysis of the EMT-related lncRNA expression data and clinical information. The Kaplan-Meier curve analysis revealed that patients in the low-risk and high-risk groups exhibited significant divergence in the overall survival (OS) and disease-free survival (DFS) of ccRCC, as was confirmed in the validation dataset. The Cox regression analysis of the clinical factors and risk signature in the OS and DFS demonstrated that the risk signature can be utilized as an independent prognostic predictor. Moreover, we developed an individualized prognosis prediction model relying on the nomogram and receive operator curve (ROC) analysis based on the independent factors. The Gene Set Enrichment Analysis (GSEA) indicated that patients in the low-risk group were associated with adherens junction, focal adhesion, MAPK signaling pathway, pathways in cancer, and renal cell carcinoma pathway. In addition, we identified three robust subtypes (named C1, C2 and C3) of ccRCC with distinct clinical characteristics and prognostic role in the TCGA dataset and ICGC dataset. Among them, C1 was associated with a better survival outcome, whereas C2 and C3 was associated with a worse survival outcome and have more advanced-stage patients. Moreover, C2 was more likely to respond to immunotherapy and was sensitive to chemo drugs, this may provide insights to clinicians to develop an individualized treatment. Collectively, this work developed a reliable EMT-related lncRNA risk signature that can independently predict the OS and DFS of ccRCC. Besides, we identified three stable molecular subtypes based on the EMT-related lncRNA expression, which may comprehensively be vital in elucidating the underlying molecular mechanism of ccRCC.

Inhibition of CDK1 Reverses the Resistance of 5-Fu in Colorectal Cancer

Introduction

Although the survival rate of colorectal cancer (CRC) patients can be improved by surgery, radiotherapy, and chemotherapy, the resistance to 5-fluorouracil (5-Fu) affects the effect of chemotherapy and the prognosis of patients. An increasing number of studies showed that 5-Fu resistance was the main reason for the failure of colorectal cancer treatment. The poor prognosis of colorectal cancer greatly harms people’s health. This study aimed to clarify the correlation between cyclin-dependent kinase 1 (CDK1) and 5-Fu-induced tumor resistance.

Materials and Methods

Cell proliferation and invasion experiments showed that down-regulation of CDK1 inhibited fluorouracil-resistant CRC cell proliferation. The expression level of CDK1 was detected in 5-Fu-resistant CRC cells in vitro. Tumor growth was inhibited by down-regulation of CDK1 in tumor xenograft mouse models.

Results

We found that CDK1 was highly expressed in tumor tissues, especially in fluorouracil-resistant tissues. We also confirmed that the differential expression of 5-Fu in tumor tissues was related to tumor site, lymph node metastasis and stage. CDK1 promoted migration, invasion and inhibited apoptosis in 5-Fu-resistant CRC cells. Down-regulation of CDK1 inhibited fluorouracil-resistant CRC cell proliferation and tumorigenesis in vivo.

Conclusion

High expression of CDK1 may lead to poor clinical prognosis, and inhibition of CDK1 enhances 5-Fu sensitivity in CRC. Our research suggested that CDK1 may be used to predict 5-Fu efficacy and as a therapeutic target for CRC.

Up-regulated Wnt1-inducible signaling pathway protein 1 correlates with poor prognosis and drug resistance by reducing DNA repair in gastric cancer

BACKGROUND

Wnt1-inducible signaling pathway protein 1 (WISP1) is upregulated in several types of human cancer, and has been implicated in cancer progression. However, its clinical implications in gastric cancer (GC) remain unclear.

AIM

To explore the expression pattern and clinical significance of WISP1 in GC.

METHODS

Public data portals, including Oncomine, The Cancer Genome Atlas database, Coexpedia, and Kaplan-Meier plotter, were analyzed for the expression and clinical significance of WISP1 mRNA levels in GC. One hundred and fifty patients who underwent surgery for GC between February 2010 and October 2012 at the Affiliated Hospital of Jiangnan University were selected for validation study. WISP1 levels were measured at both the mRNA and protein levels by RT-qPCR, Western blot analysis, and immunohistochemistry (IHC). In addition, the in situ expression of WISP1 in the GC tissues was determined by IHC, and the patients were accordingly classified into high- and low-expression groups. The correlation of WISP1 expression status with patient prognosis was then determined by univariate and multivariate Cox regression analyses. WISP1 was knocked down by RNA interference. The 50% inhibitory concentration of oxaliplatin was detected by CellTiter-Blue assay.

RESULTS

WISP1 levels at both the mRNA and protein levels were remarkably upregulated in GC tissues compared to normal tissues. Moreover, IHC revealed that WISP1 expression was associated with T stage and chemotherapy outcome, but not with lymph node metastasis, age, gender, histological grade, or histological type. GC patients with high WISP1 expression showed a poor overall survival. Multivariate survival analysis indicated that WISP1 was an important prognostic factor for GC patients. Mechanistically, knock-down of WISP1 expression enhanced sensitivity to oxaliplatin by reducing DNA repair and enhancing DNA damage.

CONCLUSION

Significantly upregulated WISP1 expression is associated with cancer progression, chemotherapy outcome, and prognosis in GC. Mechanistically, knock-down of WISP1 expression enhances oxaliplatin sensitivity by reducing DNA repair and enhancing DNA damage. WISP1 may be a potential therapeutic target for GC treatment or a potential biomarker for diagnosis and prognosis.