Identification of a RNA-Seq Based 8-Long Non-Coding RNA Signature Predicting Survival in Esophageal Cancer

A four-gene signature predicts overall survival of patients with esophageal adenocarcinoma

Background

Esophageal adenocarcinoma (EAC) is an aggressive cancer with poor prognosis. Thus, this study aimed to identify a prognostic molecular signature to predict the overall survival (OS) of patients with EAC.

Methods

The mRNA microarray data sets GSE13898 and GSE26886 were downloaded from the Gene Expression Omnibus (GEO) database. RNA sequencing profile and clinical data of EAC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) between EAC tissues and adjacent non-cancerous tissues were obtained using R software. DEGs associated with prognosis of OS were assessed by univariate Cox analysis, and a prognostic signature was built using stepwise multivariate Cox analysis. Time-dependent receiver operating characteristic (ROC) analysis and stratification analysis were conducted to evaluate its predictive performance. Functional enrichment analysis was performed for genes co-expressed with the signature to explore its biological functions in EAC.

Results

A total of 336 genes were identified to be differentially expressed between EAC tissues and adjacent non-cancerous tissues. After univariate and multivariate Cox regression analysis, four genes (ALAD, ABLIM3, IL17RB and IFI6) were screened out to construct a prognostic signature. According to this signature, patients could be assigned into high-risk and low-risk group with significantly different OS (P=4.92e-05<0.0001). Multivariate Cox regression analysis suggested that the four-gene signature served as an independent factor in OS prediction. In the time-dependent ROC analysis, the areas under the curves (AUCs) were 0.804, 0.792 and 0.695 for 1-, 3- and 5-year survival prediction, respectively, suggesting a good performance. Functional enrichment analysis showed that the signature was mainly clustered in cell proliferation related biological processes or pathways.

Conclusions

The four-gene signature identified in the current study may be a potential prognostic factor for predicting OS of EAC patients.

Ferroptosis-related lncRNAs guiding osteosarcoma prognosis and immune microenvironment

OBJECTIVE

To investigate the ferroptosis-related long non-coding RNAs (FRLncs) implicated in influencing the prognostic and immune microenvironment in osteosarcoma (OS), and to establish a foundational framework for informing clinical decision making pertaining to OS management.

METHODS

Transcriptome data and clinical data pertaining to 86 cases of OS, the GSE19276, GSE16088 and GSE33382 datasets, and a list of ferroptosis-related genes (FRGs) were used to establish a risk prognostic model through comprehensive analysis. The identification of OS-related differentially expressed FRGs was achieved through an integrated analysis encompassing the aforementioned 86 OS transcriptome data and the GSE19276, GSE16088 and GSE33382 datasets. Concurrently, OS-related FRLncs were ascertained via co-expression analysis. To establish a risk prognostic model for OS, Univariate Cox regression analysis and Lasso Cox regression analysis were employed. Subsequently, a comprehensive evaluation was conducted, comprising risk curve analysis, survival analysis, receiver operating characteristic curve analysis and independent prognosis analysis. Model validation with distinct clinical subgroups was performed to assess the applicability of the risk prognostic model to diverse patient categories. Moreover, single sample gene set enrichment analysis (ssGSEA) was conducted to investigate variations in immune cell populations and immune functions within the context of the risk prognostic model. Furthermore, an analysis of immune checkpoint differentials yielded insights into immune checkpoint-related genes linked to OS prognosis. Finally, the risk prognosis model was verified by dividing the samples into train group and test group.

RESULTS

We identified a set of seven FRLncs that exhibit potential as prognostic markers and influence factors of the immune microenvironment in the context of OS. This ensemble encompasses three high-risk FRLncs, denoted as APTR, AC105914.2 and AL139246.5, alongside four low-risk FRLncs, designated as DSCR8, LOH12CR2, AC027307.2 and AC025048.2. Furthermore, our analysis revealed notable down-regulation in the high-risk group across four distinct immune cell types, namely neutrophils, natural killer cells, plasmacytoid dendritic cells and tumor-infiltrating lymphocytes. This down-regulation was also reflected in four key immune functions, antigen-presenting cell (APC)-co-stimulation, checkpoint, cytolytic activity and T cell co-inhibition. Additionally, we identified seven immune checkpoint-associated genes with significant implications for OS prognosis, including CD200R1, HAVCR2, LGALS9, CD27, LAIR1, LAG3 and TNFSF4.

CONCLUSION

The findings of this study have identified FRLncs capable of influencing OS prognosis and immune microenvironment, as well as immune checkpoint-related genes that are linked to OS prognosis. These discoveries establish a substantive foundation for further investigations into OS survival and offer valuable insights for informing clinical decision making in this context.

Identification of the potential regulatory interactions in rheumatoid arthritis through a comprehensive analysis of lncRNA-related ceRNA networks

OBJECTIVE

This study aimed at constructing a network of competing endogenous RNA (ceRNA) in the synovial tissues of rheumatoid arthritis (RA). It seeks to discern potential biomarkers and explore the long non-coding RNA (lncRNA)-microRNA (miRNA)-messenger RNA (mRNA) axes that are intricately linked to the pathophysiological mechanisms underpinning RA, and providing a scientific basis for the pathogenesis and treatment of RA.

METHODS

Microarray data pertaining to RA synovial tissue, GSE103578, GSE128813, and GSE83147, were acquired from the Gene Expression Omnibus (GEO) database ( http://www.ncbi.nlm.nih.gov/geo ). Conducted to discern both differentially expressed lncRNAs (DELncRNAs) and differentially expressed genes (DEGs). A ceRNA network was obtained through key lncRNAs, key miRNAs, and key genes. Further investigations involved co-expression analyses to uncover the lncRNA-miRNA-mRNA axes contributing to the pathogenesis of RA. To delineate the immune-relevant facets of this axis, we conducted an assessment of key genes, emphasizing those with the most substantial immunological correlations, employing the GeneCards database. Finally, gene set enrichment analysis (GSEA) was executed on the identified key lncRNAs to elucidate their functional implications in RA.

RESULTS

The 2 key lncRNAs, 7 key miRNAs and 6 key genes related to the pathogenesis of RA were obtained, as well as 2 key lncRNA-miRNA-mRNA axes (KRTAP5-AS1-hsa-miR-30b-5p-PNN, XIST-hsa-miR-511-3p/hsa-miR-1277-5p-F2RL1). GSEA of two key lncRNAs obtained biological processes and signaling pathways related to RA synovial lesions.

CONCLUSION

The findings of this investigation hold promise in furnishing a foundational framework and guiding future research endeavors aimed at comprehending the etiology and therapeutic interventions for RA.

Prognostic impact of examined lymph-node count for patients with esophageal cancer: development and validation prediction model

Esophageal cancer (EC) is a malignant tumor with high mortality. We aimed to find the optimal examined lymph node (ELN) count threshold and develop a model to predict survival of patients after radical esophagectomy. Two cohorts were analyzed: the training cohort which included 734 EC patients from the Chinese registry and the external testing cohort which included 3208 EC patients from the Surveillance, Epidemiology, and End Results (SEER) registry. Cox proportional hazards regression analysis was used to determine the prognostic value of ELNs. The cut-off point of the ELNs count was determined using R-statistical software. The prediction model was developed using random survival forest (RSF) algorithm. Higher ELNs count was significantly associated with better survival in both cohorts (training cohort: HR = 0.98, CI = 0.97-0.99, P < 0.01; testing cohort: HR = 0.98, CI = 0.98-0.99, P < 0.01) and the cut-off point was 18 (training cohort: P < 0.01; testing cohort: P < 0.01). We developed the RSF model with high prediction accuracy (AUC: training cohort: 87.5; testing cohort: 79.3) and low Brier Score (training cohort: 0.122; testing cohort: 0.152). The ELNs count beyond 18 is associated with better overall survival. The RSF model has preferable clinical capability in terms of individual prognosis assessment in patients after radical esophagectomy.

Identification and validation of an anoikis-related lncRNA signature to predict prognosis and immune landscape in osteosarcoma

Background

Anoikis is a specialized form of programmed apoptosis that occurs in two model epithelial cell lines and plays an important role in tumors. However, the prognostic value of anoikis-related lncRNA (ARLncs) in osteosarcoma (OS) has not been reported.

Methods

Based on GTEx and TARGET RNA sequencing data, we carried out a thorough bioinformatics analysis. The 27 anoikis-related genes were obtained from the Gene Set Enrichment Analysis (GSEA). Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analysis were successively used to screen for prognostic-related ARLncs. To create the prognostic signature of ARLncs, we performed multivariate Cox regression analysis. We calculated the risk score based on the risk coefficient, dividing OS patients into high- and low-risk subgroups. Additionally, the relationship between the OS immune microenvironment and risk prognostic models was investigated using function enrichment, including Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), single-sample gene set enrichment analysis (ssGSEA), and GSEA analysis. Finally, the potential effective drugs in OS were found by immune checkpoint and drug sensitivity screening.

Results

A prognostic signature consisting of four ARLncs (AC079612.1, MEF2C-AS1, SNHG6, and TBX2-AS1) was constructed. To assess the regulation patterns of anoikis-related lncRNA genes, we created a risk score model. According to a survival analysis, high-risk patients have a poor prognosis as they progress. By using immune functional analysis, the lower-risk group demonstrated the opposite effects compared with the higher-risk group. GO and KEGG analysis showed that the ARLncs pathways and immune-related pathways were enriched. Immune checkpoints and drug sensitivity analysis might be used to determine the better effects of the higher group.

Conclusion

We identified a novel prognostic model based on a four-ARLncs signature that might serve as potential prognostic indicators that can be used to predict the prognosis of OS patients, and immunotherapy and drugs that may contribute to improving the overall survival of OS patients and advance our understanding of OS.

Ectopic expression of HIV-1 Tat modifies gene expression in cultured B cells: implications for the development of B-cell lymphomas in HIV-1-infected patients

An increased frequency of B-cell lymphomas is observed in human immunodeficiency virus-1 (HIV-1)-infected patients, although HIV-1 does not infect B cells. Development of B-cell lymphomas may be potentially due to the action of the HIV-1 Tat protein, which is actively released from HIV-1-infected cells, on uninfected B cells. The exact mechanism of Tat-induced B-cell lymphomagenesis has not yet been precisely identified. Here, we ectopically expressed either Tat or its TatC22G mutant devoid of transactivation activity in the RPMI 8866 lymphoblastoid B cell line and performed a genome-wide analysis of host gene expression. Stable expression of both Tat and TatC22G led to substantial modifications of the host transcriptome, including pronounced changes in antiviral response and cell cycle pathways. We did not find any strong action of Tat on cell proliferation, but during prolonged culturing, Tat-expressing cells were displaced by non-expressing cells, indicating that Tat expression slightly inhibited cell growth. We also found an increased frequency of chromosome aberrations in cells expressing Tat. Thus, Tat can modify gene expression in cultured B cells, leading to subtle modifications in cellular growth and chromosome instability, which could promote lymphomagenesis over time.

LINC00365 inhibited lung adenocarcinoma progression and glycolysis via sponging miR-429/KCTD12 axis

This study researched the function of long non-coding RNA LINC00365 in lung adenocarcinoma (LAD) progression. LINC00365, miR-429, and KCTD12 expression in the LAD clinical tissues and cells were detcetd by qRT-PCR and Western blot. LINC00365, miR-429, and KCTD12 effects on H1975 cells malignant phenotype were detected by cell counting kit-8 assay, clone formation experiment, Transwell experiment, and glycolysis. Dual luciferase reporter gene assay and RNA pull-down assay were implemented. LINC00365 effect on H1975 cells in vivo growth was detected. LINC00365 was low expressed in the LAD patients and cells, associating with poor outcome. LINC00365 up-regulation attenuated H1975 cells proliferation, migration, invasion, glycolysis and in vivo growth. LINC00365 inhibited KCTD12 expression by sponging miR-429. miR-429 up-regulation and KCTD12 down-regulation partial reversed LINC00365 inhibition on H1975 cells malignant phenotype. Thus, LINC00365 inhibited LAD progression and glycolysis via targeting miR-429/KCTD12 axis. LINC00365 might be a potential candidate for LAD target treatment clinically.

A novel signature to guide osteosarcoma prognosis and immune microenvironment: Cuproptosis-related lncRNA

Objective

Osteosarcoma (OS) is a common bone malignancy with poor prognosis. We aimed to investigate the relationship between cuproptosis-related lncRNAs (CRLncs) and the survival outcomes of patients with OS.

Methods

Transcriptome and clinical data of 86 patients with OS were downloaded from The Cancer Genome Atlas (TCGA). The GSE16088 dataset was downloaded from the Gene Expression Omnibus (GEO) database. The 10 cuproptosis-related genes (CRGs) were obtained from a recently published article on cuproptosis in Science. Combined analysis of OS transcriptome data and the GSE16088 dataset identified differentially expressed CRGs related to OS. Next, pathway enrichment analysis was performed. Co-expression analysis obtained CRLncs related to OS. Univariate COX regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were used to construct the risk prognostic model of CRLncs. The samples were divided evenly into training and test groups to verify the accuracy of the model. Risk curve, survival, receiver operating characteristic (ROC) curve, and independent prognostic analyses were performed. Next, principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) analysis were performed. Single-sample gene set enrichment analysis (ssGSEA) was used to explore the correlation between the risk prognostic models and OS immune microenvironment. Drug sensitivity analysis identified drugs with potential efficacy in OS. Real-time quantitative PCR, Western blotting, and immunohistochemistry analyses verified the expression of CRGs in OS. Real-time quantitative PCR was used to verify the expression of CRLncs in OS.

Results

Six CRLncs that can guide OS prognosis and immune microenvironment were obtained, including three high-risk CRLncs (AL645608.6, AL591767.1, and UNC5B-AS1) and three low-risk CRLncs (CARD8-AS1, AC098487.1, and AC005041.3). Immune cells such as B cells, macrophages, T-helper type 2 (Th2) cells, regulatory T cells (Treg), and immune functions such as APC co-inhibition, checkpoint, and T-cell co-inhibition were significantly downregulated in high-risk groups. In addition, we obtained four drugs with potential efficacy for OS: AUY922, bortezomib, lenalidomide, and Z.LLNle.CHO. The expression of LIPT1, DLAT, and FDX1 at both mRNA and protein levels was significantly elevated in OS cell lines compared with normal osteoblast hFOB1.19. The mRNA expression level of AL591767.1 was decreased in OS, and that of AL645608.6, CARD8-AS1, AC005041.3, AC098487.1, and UNC5B-AS1 was upregulated in OS.

Conclusion

CRLncs that can guide OS prognosis and the immune microenvironment and drugs that may have a potential curative effect on OS obtained in this study provide a theoretical basis for OS survival research and clinical decision-making.

Pyroptosis-Related lncRNAs Predict the Prognosis and Immune Response in Patients With Breast Cancer

Background: Breast cancer (BC) is the most common malignant tumor and the leading cause of cancer-related death in women worldwide. Pyroptosis and long noncoding RNAs (lncRNAs) have been demonstrated to play vital roles in the tumorigenesis and development of BC. However, the clinical significance of pyroptosis-related lncRNAs in BC remains unclear.

Methods: Using the mRNA and lncRNA profiles of BC obtained from TCGA dataset, a risk model based on the pyroptosis-related lncRNAs for prognosis was constructed using univariate and multivariate Cox regression model, and least absolute shrinkage and selection operator. Patients were divided into high- and low-risk groups based on the risk model, and the prognosis value and immune response in different risk groups were analyzed. Furthermore, functional enrichment annotation, therapeutic signature, and tumor mutation burden were performed to evaluate the risk model we established. Moreover, the expression level and clinical significance of the selected pyroptosis-related lncRNAs were further validated in BC samples.

Results: 3,364 pyroptosis-related lncRNAs were identified using Pearson’s correlation analysis. The risk model we constructed comprised 10 pyroptosis-related lncRNAs, which was identified as an independent predictor of overall survival (OS) in BC. The nomogram we constructed based on the clinicopathologic features and risk model yielded favorable performance for prognosis prediction in BC. In terms of immune response and mutation status, patients in the low-risk group had a higher expression of immune checkpoint markers and exhibited higher fractions of activated immune cells, while the high-risk group had a highly percentage of TMB. Further analyses in our cohort BC samples found that RP11-459E5.1 was significantly upregulated, while RP11-1070N10.3 and RP11-817J15.3 were downregulated and significantly associated with worse OS.

Conclusion: The risk model based on the pyroptosis-related lncRNAs we established may be a promising tool for predicting the prognosis and personalized therapeutic response in BC patients.

Tumor microenvironment characterization in esophageal cancer identifies prognostic relevant immune cell subtypes and gene signatures

Esophageal cancer (ESCA) is a common malignancy in the digestive system with a high mortality rate and poor prognosis. Tumor microenvironment (TME) plays an important role in the tumorigenesis, progression and therapy resistance of ESCA, whereas its role in predicting clinical outcomes has not been fully elucidated. In this study, we comprehensively estimated the TME infiltration patterns of 164 ESCA patients using Gene Set Variation Analysis (GSVA) and identified 4 key immune cells (natural killer T cell, immature B cell, natural killer cell, and type 1 T helper cell) associated with the prognosis of ESCA patients. Besides, two TME groups were defined based on the TME patterns with different clinical outcomes. According to the expression gene set between two TME groups, we built a model to calculate TMEscore based on the single-sample gene-set enrichment analysis (ssGSEA) algorithm. TMEscore systematically correlated the TME groups with genomic characteristics and clinicopathologic features. In conclusion, our data provide a novel TMEscore which can be regarded as a reliable index for predicting the clinical outcomes of ESCA.

Single-Cell Sequencing of Hepatocellular Carcinoma Reveals Cell Interactions and Cell Heterogeneity in the Microenvironment

BACKGROUND

Hepatocellular carcinoma (HCC) is the main histological subtype of liver cancer, which has the characteristics of poor prognosis and high fatality rate. Single-cell sequencing can provide quantitative and unbiased characterization of cell heterogeneity by analyzing the molecular profile of the whole genome of thousands of single cells. Thus, the purpose of this study was to identify novel prognostic markers for HCC based on single-cell sequencing data.

METHODS

Single-cell sequencing of 21 HCC samples and 256 normal liver tissue samples in the GSE124395 dataset was collected from the Gene Expression Omnibus (GEO) database. The quality-controlled cells were grouped by unsupervised cluster analysis and identified the marker genes of each cell cluster. Hereafter, these cell clusters were annotated by singleR and CellMarker according to the expression patterns of the marker genes. Pseudotime analysis was performed to construct the trajectory of cell evolution and to define hub genes in the evolution process. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to explore the potential regulatory mechanism of hub genes in HCC. Next, the differential expression of hub genes and the correlation of the expression of these genes with patients' survival and diagnosis were investigated in The Cancer Genome Atlas (TCGA) database.

RESULTS

A total of 9 clusters corresponding to 9 cell types, including NKT cells, hepatocytes, endothelial cells, Kupffer cells, EPCAM+ cells, cancer cells, plasma cells (B cells), immature B cells, and myofibroblasts were identified. We screened 63 key genes related to cell differentiation through trajectory analysis, which were enriched in the process of coagulation. Ultimately, we identified 10 survival-related hub genes in the TCGA database, namely ALDOB, APOC3, APOH, CYP2E1, CYP3A4, GC, HRG, LINC01554, PDK4, and TXN.

CONCLUSION

In conclusion, ALDOB, APOC3, APOH, CYP2E1, CYP3A4, GC, HRG, LINC01554, PDK4, and TXN may serve as hub genes in the diagnosis and prognosis for HCC.

Development and validation of a ferroptosis-related prognostic model for the prediction of progression-free survival and immune microenvironment in patients with papillary thyroid carcinoma

BACKGROUND

Ferroptosis is an iron-dependent and regulated cell death that has been widely reported in a variety of malignancies. The overall survival of papillary thyroid cancer (PTC) is excellent, but the identification of patients with poor prognosis still faces challenges. Nevertheless, whether ferroptosis-related genes (FRGs) can be used to screen high-risk patients is not clear.

METHODS

We obtained the clinical data of patients with PTC and FRGs from the UCSC Xena platform and the FerrDb respectively. Differentially expressed genes (DEGs) of FRGs were obtained from the entire The Cancer Genome Atlas (TCGA). Subsequently, the entire TCGA dataset was randomly split into two subsets: training and test datasets. Based on DEGs, we constructed a predictive model which was tested in the test dataset and the entire TCGA dataset to predict progression-free survival (PFS). Patients were categorized into high- or low-risk groups based on their median risk score. We analyzed differences in some aspects, including pathway enrichment analysis, single-sample Gene Set Enrichment Analysis (ssGSEA), tumor microenvironment (TME), human leukocyte antigen (HLA) genes, and tumor mutation burden (TMB) analyses, between high-risk and low-risk groups.

RESULTS

A predictive model with three FRGs (HSPA5, AURKA, and TSC22D3) was constructed. Patients in the high-risk group had worse PFS compared with patients in the low-risk group. Functional analysis results revealed that ssGSEA, immune cell infiltration, TME, HLA, and TMB were closely associated with ferroptosis.

CONCLUSION

The prognostic model constructed in this study can effectively predict PFS for patients with PTC.

Development and validation of a three-immune-related gene signature prognostic risk model in papillary thyroid carcinoma

PURPOSE

Increasing evidence indicates that there is a correlation between papillary thyroid carcinoma (PTC) prognosis and the immune signature. Our goal was to construct a new prognostic tool based on immune genes to achieve more accurate prognosis predictions and earlier diagnoses of PTC.

METHODS

The 493 PTCs samples and 58 tumor-adjacent normal tissues were obtained from The Cancer Genome Atlas database (TCGA). Immune genes were obtained from the ImmPort database. First, this cohort was randomly divided into training cohort and testing cohort. Second, the differentially expressed (DE) immune genes from the training set were used to construct the prognostic model. Then, the testing and entire data cohorts were used to validate the model, and the data were analyzed to determine the correlation of the clinical prognostic model with immune cell infiltration and expression profiles of human leukocyte antigen (HLA) genes. Finally, an analysis of the gene ontology (GO) annotation was performed.

RESULTS

A total of 189 upregulated and 128 downregulated DE immune genes were identified. We developed and validated a three-immune gene model for PTC that includes Hsp70, NOX5, and FGF23. This model was demonstrated to be an independent prognostic variable. In addition, the overall immune activity of the high-risk group was higher than that of the low-risk group.

CONCLUSIONS

We developed and validated a three-immune gene model for PTC that includes HSPA1A, NOX5, and FGF23. This model can be used as a validated tool to predict outcomes in PTC.

Long Noncoding RNA LINC01554 as a Novel Biomarker for Diagnosis and Prognosis Prediction of Epithelial Ovarian Cancer

Objective

This study was aimed at exploring the diagnostic and prognostic value of long noncoding RNA LINC01554 (LINC01554) in epithelial ovarian cancer (EOC) patients. Patients and Methods. The expressions of LINC01554 in 161 EOC patients were analyzed using RT-PCR. The area under the ROC curve (AUC) was used to estimate the effectiveness of LINC01554 for prediction. The chi-square test was performed to explore the association between LINC01554 expressions and clinical characteristics in EOC patients. Kaplan-Meier assays were conducted for the examination of the influence of LINC01554 expression on the overall survival of EOC patients. Multivariate analyses were carried out to further determine prognostic values of LINC01554 expression in EOC patients.

Results

LINC01554 expressions were strongly downregulated in EOC specimens compared with matched nontumor specimens (p < 0.01). Importantly, LINC01554 provided a high diagnostic performance for the detection of EOC specimens (AUC = 0.7827; p < 0.001). Low expression of LINC01554 was distinctly associated with the FIGO stage (p = 0.034) and distant metastasis (p = 0.007). The assays of survival data (five years) revealed that the 5-year overall survival of the low LINC01554 expression group was distinctly shorter than that of the high LINC01554 expression group (p = 0.0017). Finally, in the multivariate Cox model, LINC01554 expression (RR = 2.863, 95% CI: 1.185-4.421, p = 0.014) was demonstrated to be an independent prognostic factor for overall survival of EOC patients.

Conclusions

Our findings suggested that LINC01554 is an important EOC-related lncRNA, providing a potential diagnostic, prognostic biomarker and therapeutic target for EOC patients.

Correlation of autophagy-related genes for predicting clinical prognosis in colorectal cancer

Aim: Autophagy plays a controversial role in cancer. The role of autophagy-related genes (ARGs) in colorectal cancer (CRC) was evaluated based on publicly available data from The Cancer Genome Atlas and the Human Autophagy Database. Materials & methods: After collecting CRC-related transcript and clinical data and a list of ARGs from public databases, the Wilcoxon test was used to identify the differentially expressed ARGs between CRC and paired normal tissues. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were used to identify the major biological properties and pathways associated with these genes. Univariate Cox regression was used to identify the prognosis-associated ARGs, and a forest plot was used to visualize the results. Kaplan-Meier analysis of the 5-year survival rate was performed. Univariate and multivariate Cox analyses were used to verify the impact of the prognosis-associated ARGs. Results: A total of 36 differentially expressed genes (16 upregulated and 20 downregulated in CRC) were obtained from among 206 ARGs. There were 53 enriched pathways, including the p53 signaling pathway, platinum drug resistance, apoptosis, EGFR tyrosine kinase inhibitor resistance and ErbB signaling pathway (p- and q-values <0.05). Kaplan-Meier analysis showed that the 5-year survival rate was 46.0% (95% CI: 0.335-0.631) and 76.0% (95% CI: 0.651-0.886) in the high- and low-risk groups, respectively. The high-risk patients had worse survival probability (p = 6.256 × 10-5). Independent-samples t-tests revealed that MAP1LC3C expression was higher in patients aged ≤65 than >65 (p = 0.022); RAB7A expression was higher in patients aged ≤65 than >65 (p = 7.31 × 10-4), higher in M1 than M0 (p = 0.042), higher in N1-3 than N0 (p = 0.002) and higher in stage III and IV than I and II (p = 0.042); risk score was higher in N1-3 than N0 (p = 0.001) and in stage III and IV than I and II (p = 0.002); and WIPI2 expression was higher in M1 than M0 (p = 0.002), higher in N1-3 than N0 (p = 2.059 × 10-7) and higher in stage III and IV than I and II (p = 2.299 × 10-7). There were no differences in risk score between males and females (p = 0.593), T1-2 and T3-4 (p = 0.082) or M0 and M1 (p = 0.072). Univariate and multivariate Cox analyses showed that RAB7A was a lower-risk gene, while MAP1LC3C, WIPI2, DAPK1, ULK3 and PELP1 were high-risk genes. Conclusion: Certain ARGs are potential prognostic molecular markers of poor prognosis in CRC. Additionally, the p53 signaling pathway, platinum drug resistance, apoptosis, EGFR tyrosine kinase inhibitor resistance and ErbB signaling pathway may be critical pathways regulated by ARGs in CRC.

LncRNA GIHCG Promotes the Development of Esophageal Cancer by Modulating miR-29b-3p/ANO1 Axis

Background

Esophageal cancer is one of the most frequent cancers with a higher mortality worldwide. Although many long non-coding RNAs (LncRNAs) are reported to play important roles in the progression of esophageal cancer, the function of lncRNA GIHCG in esophageal cancer remains unclear.

Methods

The expression of GIHCG in esophageal cancer tissues and cancer cell lines was detected by qRT-PCR. Cell proliferation was evaluated by Cell Counting Kit-8 (CCK-8) assay, EdU staining assay and colony formation assay. Cell invasion and migration were measured by transwell assay. Cell apoptosis was detected by a flow cytometer. Luciferase reporter assay and RIP assay were used to determine the interaction between GIHCG and miR-29b-3p, and their subsequent regulation of anoctamin 1 (ANO1). The expression of ANO1 in esophageal cancer tissues and cell lines was detected by Western blot. The effect of GIHCG/miR-29b-3p in tumor formation was assessed by the xenograft nude mice model in vivo.

Results

GIHCG was significantly upregulated in esophageal cancer tissues and relevant cancer cell lines. Downregulation of GIHCG significantly inhibited the growth, colony formation, invasion, migration and induced apoptosis of esophageal cancer cells in vitro. Bioinformatic analysis and RIP assay determined that GIHCG was a sponge of miR-29b-3p, and ANO1 was a direct target of miR-29b-3p. Moreover, functional experiments showed that GIHCG upregulated ANO1 expression by directly sponging miR-29b-3p. Furthermore, in vivo experiment revealed that knockdown of GIHCG significantly inhibited tumor growth in nude mice.

Conclusion

Our study revealed that lncRNA GIHCG promoted the progression of esophageal cancer by targeting the miR-29b-3p/ANO1 axis, suggesting that GIHCG might be a novel therapeutic target for esophageal cancer.

Extracellular Matrix-Related Six-lncRNA Signature as a Novel Prognostic Biomarker for Bladder Cancer

Introduction

Bladder cancer (BC) is the fourth-commones cancer and the sixth-leading cause of cancer-related death among men. However, a lack of reliable biomarkers remains a problem forprognosis and treatment of BC. lncRNAs have been shown to play important roles in various cancers, and have emerged as promising biomarkers for cancer prognosis and treatment.

Methods

In this study, using univariate and multivariate Cox regression analysis, we examined the differential expression profiles of 1,651 lncRNAs in the TCGA BLCA cohort and created a prognostic gene signature composed of six lncRNAs (for SNHG12, MAFG-DT, ASMTL-AS1, LINC02321, LINC01322, and LINC00922), designed the SMALLL signature.

Results

The SMALLL signature displayed significant prognostic power for overall survival for BC patients in multiple cohorts. Gene Ontology analysis showed that genes coexpressed with the SMALLL signature were associated with the extracellular matrix network, and immune cell–infiltration analysis showed that activated naïve B cells, regulatory T cells, M0 macrophages, eosinophils, resting memory CD4 T cells and resting NK cells were significantly different in high- and low-risk groups. We also confirmed differential expression of the lncRNAs of the SMALLL signature in BC tissue and paracancer normal tissue by qRT-PCR analysis. Cell-invasion and -migration experiments showed that MAFG-AS1, ASMTL-AS1, LINC02321, and LINC00922 significantly affected cell invasion and migration.

Conclusion

Our study revealed that the lncRNA signature is an important predictive factor of prognosis and provides a promising biomarker for BC.

Exosomal long non-coding RNA UCA1 functions as growth inhibitor in esophageal cancer

PURPOSE

Esophageal cancer is a highly lethal and broad-spreading malignant tumor worldwide. Exosome-carrying lncRNAs play an essential role in the pathogenesis of various cancers.

RESULTS

The results revealed that the expression of UCA1 was decreased in esophageal cancer tissues and plasma exosomes. UCA1 was enriched in exosomes, and exosomal UCA1 was a promising biomarker for the diagnosis of esophageal cancer with 86.7% sensitivity and 70.2% specificity. Overexpression of UCA1 played anticancer roles in esophageal cancer cells through inhibiting cell proliferation, invasion and migration, and colony formation. Also, exosomal UCA1 was taken up by esophageal cancer cells and inhibited the progression of esophageal cancer in vitro and tumor growth in vivo. Furthermore, exosomal UCA1 could directly target miRNA-613 in esophageal cancer cells.

CONCLUSIONS

The results suggested that exosomal UCA1 inhibits tumorigenesis and progression of esophageal cancer in vitro and in vivo, and might be a promising biomarker for esophageal cancer.

PATIENT AND METHODS

In this study, we determined the expression of UCA1 in esophageal cancer tissues, plasma exosomes of patients with esophageal cancer. We determined the potential of exosomal UCA1 as a biomarker and its effect on the pathogenesis and progression of esophageal cancer in vitro and in vivo.

High Expression of miR-206 Predicts Adverse Outcomes: A Potential Therapeutic Target for Esophageal Cancer

BACKGROUND

MicroRNA-206 (miR-206) inhibits cell proliferation, invasion and migration in a variety of tumors, but the prognostic value of its Esophageal Cancer (EC) remains unclear.

OBJECTIVE

To study the role of miR-206 in EC.

METHODS

The datasets of RNA-Seq, miRNA-Seq, methylation, copy number variation (CNV), and clinical follow-up information were download from The Cancer Genome Atlas (TCGA). After integration and standardization, the prognostic value and potential function of miR-206 were analyzed. The important roles of miR-206 expression in EC genetic and epigenetic mechanisms were analyzed by RNA-Seq, miRNA-Seq, and methylation data. The potential mechanism of CNV in different miR-206 expression groups was analyzed using GISTIC.

RESULTS

High expression of miR-206 was associated with poor outcome of EC (OS: p=0.005, AUC=0.69, N=178). Transforming growth factor β (TGF-β) signaling pathway, Wnt signaling pathway, mitogen-activated protein kinases (MAPK) signaling pathway, mammalian target of rapamycin (mTOR) signaling pathway were inhibited in high expression group. the aberrant methylation sites in the high and low expression groups were mainly distributed in the promoter region containing CpG islands, and there were different copy number patterns in the H and L samples, and the genes in the differential copy number were mainly enriched in cancer-related pathways, such as thyroid cancer, central carbon metabolism.

CONCLUSION

This study explored the unique genomic and epigenetic landscape associated with the expression of miR-206, provided evidence of mir-206 as a prognostic biomarker or a potential therapeutic target for EC patients.

Bioinformatics analysis of LINC01554 and its co‑expressed genes in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related morbidity and mortality globally. Despite the remarkable improvements in comprehensive HCC treatment, the underlying mechanistic details of HCC remain elusive. We screened HCC patients for differentially expressed genes (DEGs) using the Gene Expression Omnibus (GSE113850) and The Cancer Genome Atlas (TCGA) datasets. LINC01554 expression in 40 paired samples was determined by quantitative reverse transcription polymerase chain reaction (RT-qPCR), and its clinical significance was assessed. LINC01554 was found to have a gain-of-function role in HCC in vitro. Additionally, the bioinformatics analysis of the genes co-expressed with LINC01554 was performed using the Co-LncRNA website, and potential molecular mechanisms were investigated using the Gene Ontology and Kyoto Encyclopaedia of Genes and Genomes resources and validated by in vitro experiments. A total of 229 DEGs were identified from the GSE113850 dataset. Among the identified DEGs, three long non-coding RNAs (lncRNAs) (DIO3OS, LINC01554, and LINC01093) with |logFC| ≥2 and P<0.05 were screened. A total of 148 lncRNAs with |logFC| ≥1 and P<0.05 were identified from TCGA dataset. Low LINC01554 expression levels were significantly correlated with overall survival, pathological stage, hepatitis B infection, tumour size, portal vein tumour thrombus, and TNM stage. Using gain-of-function assays, we further showed that LINC01554 inhibited the proliferation, migration, and invasion of the HCCLM9 and SK-Hep1 cells and promoted G0/G1 arrest, but it did not significantly affect apoptosis. Western blotting revealed that LINC01554 overexpression resulted in increased ZO-1 and E-cadherin expression levels, but decreased N-cadherin and vimentin expression levels. Moreover, LINC01554 overexpression inhibited Akt, p-Akt, β-catenin, and p-Gsk3β expression. Our results showed that LINC01554 repressed HCC cell invasiveness and epithelial-to-mesenchymal transition partly by inhibiting Wnt and PI3K-Akt signalling in vitro. Taken together, our findings provide new insights into the molecular mechanisms underlying HCC tumourigenesis and implicate LINC01554 as a potential target for HCC therapy.

Potential five-mRNA signature model for the prediction of prognosis in patients with papillary thyroid carcinoma

Although the mortality rate of papillary thyroid carcinoma (PTC) is relatively low, the recurrence rates of PTC remain high. The high recurrence rates are related to the difficulties in treatment. Gene expression profiles has provided novel insights into potential therapeutic targets and molecular biomarkers of PTC. The aim of the present study was to identify mRNA signatures which may categorize PTCs into high-and low-risk subgroups and aid with the predictions for prognoses. The mRNA expression profiles of PTC and normal thyroid tissue samples were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed mRNAs were identified using the ‘EdgeR’ software package. Gene signatures associated with the overall survival of PTC were selected, and enrichment analysis was performed to explore the biological pathways and functions of the prognostic mRNAs using the Database for Visualization, Annotation and Integration Discovery. A signature model was established to investigate a specific and robust risk stratification for PTC. A total of 1,085 differentially expressed mRNAs were identified between the PTC and normal thyroid tissue samples. Among them, 361 mRNAs were associated with overall survival (P<0.05). A 5-mRNA prognostic signature for PTC (ADRA1B, RIPPLY3, PCOLCE, TEKT1 and SALL3) was identified to classify the patients into high-and low-risk subgroups. These prognostic mRNAs were enriched in Gene Ontology terms such as ‘calcium ion binding’, ‘enzyme inhibitor activity’, ‘carbohydrate binding’, ‘transcriptional activator activity’, ‘RNA polymerase II core promoter proximal region sequence-specific binding’ and ‘glutathione transferase activity’, and Kyoto Encyclopedia of Genes and Genomes signaling pathways such as ‘pertussis’, ‘ascorbate and aldarate metabolism’, ‘systemic lupus erythematosus’, ‘drug metabolism-cytochrome P450 and ‘complement and coagulation cascades’. The 5-mRNA signature model may be useful during consultations with patients with PTC to improve the prediction of their prognosis. In addition, the prognostic signature identified in the present study may reveal novel therapeutic targets for patients with PTC.

Genome-Wide Profiling of Alternative Splicing Signature Reveals Prognostic Predictor for Esophageal Carcinoma

Background

Alternative splicing (AS) is a molecular event that drives protein diversity through the generation of multiple mRNA isoforms. Growing evidence demonstrates that dysregulation of AS is associated with tumorigenesis. However, an integrated analysis in identifying the AS biomarkers attributed to esophageal carcinoma (ESCA) is largely unexplored.

Methods

AS percent-splice-in (PSI) data were obtained from the TCGA SpliceSeq database. Univariate and multivariate Cox regression analysis was successively performed to identify the overall survival (OS)-associated AS events, followed by the construction of AS predictor through different splicing patterns. Then, a nomogram that combines the final AS predictor and clinicopathological characteristics was established. Finally, a splicing regulatory network was created according to the correlation between the AS events and the splicing factors (SF).

Results

We identified a total of 2389 AS events with the potential to be used as prognostic markers that are associated with the OS of ESCA patients. Based on splicing patterns, we then built eight AS predictors that are highly capable in distinguishing high- and low-risk patients, and in predicting ESCA prognosis. Notably, the area under curve (AUC) value for the exon skip (ES) prognostic predictor was shown to reach a score of 0.885, indicating that ES has the highest prediction strength in predicting ESCA prognosis. In addition, a nomogram that comprises the pathological stage and risk group was shown to be highly efficient in predicting the survival possibility of ESCA patients. Lastly, the splicing correlation network analysis revealed the opposite roles of splicing factors (SFs) in ESCA.

Conclusion

In this study, the AS events may provide reliable biomarkers for the prognosis of ESCA. The splicing correlation networks could provide new insights in the identification of potential regulatory mechanisms during the ESCA development.

Risk Scoring System based on lncRNA Expression for Predicting Survival in Hepatocellular Carcinoma with Cirrhosis

Objective:

This study aims to explore the roles of long non-coding RNAs (lncRNAs) for predicting survival in hepatocellular carcinoma (HCC) patients with cirrhosis.

Methods:

A set of lncRNAs differentially expressed between HCC patients with or without cirrhosis was identified using expression profiles of The Cancer Genome Atlas database, and these lncRNAs were screened for their risk scoring system to predict recurrence-free survival (RFS) or overall survival (OS). Predictive ability of risk scoring systems was confirmed using uni- and multivariate Cox analyses while adjusting for clinical features. Predictive lncRNAs were analyzed by functional enrichment analysis.

Results:

Our screen identified 22 lncRNAs that were upregulated in the presence of cirrhosis and 59 that were downregulated. To predict OS of HCC patients with cirrhosis, a risk scoring system was developed with four lncRNAs (LINC02086, LINC00880, LINC01549 and AC136475.3); to predict RFS in these patients, the risk scoring system contained five lncRNAs (SH3RF3-AS1, AC104117.3, AC136475.3, LINC00239 and MRPL23-AS1). All risk scoring systems were associated with an area under the receiver operating characteristic curve > 0.7. Based on uni- and multivariate Cox analyses, the risk scoring system could serve as a significant independent predictor for OS in HCC patients with cirrhosis. Functional enrichment analysis suggested that the lncRNAs in the risk scoring systems are involved primarily in the pathway of Wnt signal and cytokine-cytokine receptor interaction.

Conclusion:

Risk scoring systems based on lncRNAs can effectively predict OS of HCC patients with cirrhosis. The system should be further developed and validated in larger, preferably multi-site patient populations.

Down-regulation of Long Non-coding RNA LINC01554 in Hepatocellular Cancer and its Clinical Significance

Background: For high morbidity rate but lack of early accurate screening, hepatocellular cancer (HCC) manifests as the fourth leading cause of cancer related death worldwide. Accumulating evidence demonstrated that a series of long noncoding RNA (lncRNA) have strong association with pathogenesis and clinical evaluation of HCC. LINC01554, one kind of lncRNA, has been found specifically enriched in liver tissue. However, the relationship between LINC01554 expression and HCC tumorigenesis remains unclear.

Methods: The relative LINC01554 expression was measured in HCC tissues of 138 patients and several HCC cell lines using quantitative real-time PCR. Patients were grouped according to individual LINC01554 expression. Then, the potential association between LINC01554 expression in HCC tissues and clinical characteristics as well as prognostic information of patients was evaluated.

Results: Compared to correspongding adjacent liver tissues, the LINC01554 expression in HCC was significantly down-regulated (P=0.001). And its expression levels in HCC cell lines were also remarkably lower than that in normal human hepatocyte cell line (P<0.001). Besides, the expression level of LINC01554 was significantly related to tumor size, multiple lesions, TNM stages, tumor recurrence rate as well as long-term survival in HCC patients (P<0.05).

Conclusion: The research revealed that LINC01554 was down-regulated in HCC and it could be used for the accurate diagnosis and prognostic prediction of HCC patients.

Application of Machine Learning in Developing a Novelty Five-Pseudogene Signature to Predict Prognosis of Head and Neck Squamous Cell Carcinoma: A New Aspect of "Junk Genes" in Biomedical Practice

Head and neck squamous cell carcinoma (HNSCC) is the sixth malignancy, which is characterized by poor prognosis or high mortality because of the lack of predicting markers. Aberrant cancer pseudogenes have been found predictive for prognosis. We aim to identify a pseudogene-based prognosis signature for HNSCC by machine learning. RNA-seq data were downloaded from The Cancer Genome Atlas, and 700 differentially-expressed pseudogenes were identified. The survival-related pseudogenes were screened through COX-regression analysis, which includes univariate regression, least absolute shrinkage and selection operator regression, and multivariate regression, and a five-pseudogene signature was constructed. The value of prediction for the signature was validated in multiple subgroups in terms of survival. Gene set enrichment analysis (GSEA) and coexpression analysis were used to determine the underlying biological functions. Seven hundred dysregulated pseudogenes were identified, and the five-pseudogene signature can distinguish the low-risk and high-risk patients for both training and testing sets and predicted prognosis with high sensitivity and specificity. Furthermore, the signature was applicable to patients of different genders, ages, stages, and grades. Coexpression analysis revealed that the five-pseudogene is associated with immune system. GSEA showed cancer-related biological process and pathways the five-pseudogene involved in. The five-pseudogene signature is not only a novel marker for prognosis but also a promising signature for monitoring therapeutic schedule. Therefore, our findings may have potential clinical significance.

The LINC00365/SCGB2A1 (Mammaglobin B) Axis Down-Regulates NF-κB Signaling and Is Associated with the Progression of Gastric Cancer

PURPOSE

A lack of early diagnostic biomarkers and therapeutic targets has led to poor prognosis for gastric cancer patients. However, the analysis of cancer-associated genomic data has been shown to be effective in identifying potential markers. Recently, the long non-coding RNA LINC00365 and SCGB2A1 gene (as known as mammaglobin B) were predicted to be co-expressed in gastric cancer based on the Gene Expression Omnibus database. However, their precise role in gastric cancer tumors is still not clear.

METHODS

The expressions of LINC00365 and SCGB2A1 in gastric cancer tissues were investigated using qPCR and their expressions were detected in a gastric cancer tissue microarray by in situ hybridization and immunohistochemical staining. The functions of LINC00365 in BGC-823 and MGC-803 gastric cancer cells were tested using the MTT assay, flow cytometry, colony formation assay, EDU staining, immunofluorescence and luciferase assay.

RESULTS

We found that LINC00365 and SCGB2A1 mRNA were both expressed at low levels in 30 cases of gastric cancer. Gastric cancer tissue microarray analysis indicated that LINC00365 and SCGB2A1 were expressed at low levels in tumor tissue, and low expression of both factors correlated with shorter survival time. Functional studies showed that LINC00365 overexpression significantly inhibited gastric cancer cell viability through the impairment of proliferation rather than the promotion of apoptosis. Furthermore, overexpressed LINC00365 upregulated SCGB2A1 in gastric cancer cell lines. Immuno-fluorescence and luciferase assay analysis indicated that LINC00365 overexpression inhibited the NF-κB pro-survival signaling pathway. Consistent with the effects of LINC00365, SCGB2A1 upregulation also reduced cell survival and inactivated NF-κB.

CONCLUSION

Collectively, our findings revealed that SCGB2A1 may be the target coding protein regulated by LINC00365 in gastric cancer. LINC00365 and SCGB2A1 may function as tumor suppressors and may serve as potential prognostic and therapeutic markers in gastric cancer treatment.

Expression scoring of a small-nucleolar-RNA signature identified by machine learning serves as a prognostic predictor for head and neck cancer

Head and neck squamous cell carcinoma (HNSCC) is a common malignancy with high mortality and poor prognosis due to a lack of predictive markers. Increasing evidence has demonstrated small nucleolar RNAs (snoRNAs) play an important role in tumorigenesis. The aim of this study was to identify a prognostic snoRNA signature of HNSCC. Survival-related snoRNAs were screened by Cox regression analysis (univariate, least absolute shrinkage and selection operator, and multivariate). The predictive value was validated in different subgroups. The biological functions were explored by coexpression analysis and gene set enrichment analysis (GSEA). One hundred and thirteen survival-related snoRNAs were identified, and a five-snoRNA signature predicted prognosis with high sensitivity and specificity. Furthermore, the signature was applicable to patients of different sexes, ages, stages, grades, and anatomic subdivisions. Coexpression analysis and GSEA revealed the five-snoRNA are involved in regulating malignant phenotype and DNA/RNA editing. This five-snoRNA signature is not only a promising predictor of prognosis and survival but also a potential biomarker for patient stratification management.

LINC00365-SCGB2A1 axis inhibits the viability of breast cancer through targeting NF-κB signaling

Breast cancer is the most common high-grade malignancy in women. The lack of therapeutic targets has limited the treatment of breast cancer. Recently, long noncoding RNAs (lncRNAs) have been demonstrated to be dysregulated in various types of cancer. However, the specific mechanisms by which lncRNAs influence breast cancer have remained largely unclear. To bridge this research gap, the present study focused on the lncRNA LINC00365, which is expressed at a low level in breast cancer. Secretoglobin family 2A member 1 (SCGB2A1) was identified as a potential target protein regulated by LINC00365. The results of the present study demonstrated that the overexpression of LINC00365 and SCGB2A1 inhibited cell viability and induced cell apoptosis through the inhibition of the NF-κB signaling pathway in breast cancer cells. These findings indicated that LINC00365 may serve a crucial role in breast cancer and may be considered as a novel target for the clinical treatment of breast cancer.

Knockdown of long non-coding RNA LINC00176 suppresses ovarian cancer progression by BCL3-mediated down-regulation of ceruloplasmin

Ovarian cancer is a common malignancy among women with some clinically approved diagnostic coding gene biomarkers. However, long non-coding RNAs (lncRNAs) have been indicated to play an important role in controlling tumorigenesis of ovarian cancer. Hereby, the aim of the study was to uncover the function of lncRNA LINC00176 in the development and progression of ovarian cancer by regulating ceruloplasmin (CP). Bioinformatics prediction in combination with RT-qPCR analysis for the expression pattern of LINC00176 revealed that LINC00176 was highly expressed in ovarian cancer tissues as well as in ovarian cancer cell lines, respectively. LINC00176 was predominantly localized in the nucleus. Delivery of si-LINC00176, oe-LINC00176, si-BCL3 and si-CP plasmids was conducted to explore the effects of LINC00176 on ovarian cancer. Promoted proliferation, migration and invasion along with reduced apoptosis were observed in cells treated with oe-LINC00176, while si-BCL3 and si-CP were able to block the promoting effects. Investigations with regard to the correlation between LINC00176 and promoter region of CP turned out to be positive via B-cell CLL/lymphoma 3 (BCL3) by means of dual-luciferase reporter gene assay, ChIP and RIP assays. Furthermore, oncogenic properties of the LINC00176/BCL3/CP axis were also demonstrated by tumour formation in vivo generated upon injecting cells in nude mice. Our results demonstrate that restored LINC00176 initiates tumorigenesis in ovarian cancer by increasing CP expression via recruiting BCL3, the mechanism of which represented a potential and promising therapeutic target for the disease.

The clinical significance and potential molecular mechanism of integrin subunit beta 4 in laryngeal squamous cell carcinoma

The relationship between integrin beta 4 (ITGB4) expression and laryngeal squamous cell carcinoma (LSCC) remains unclarified. The object of the present study was to explore the clinical significance and potential molecular mechanism of ITGB4 in LSCC. The protein level of ITGB4 was significantly higher in 46 LSCC patients than in 26 non-LSCC tissues detected by in-house immunohistochemistry. Consistently, ITGB4 mRNA level was also greatly upregulated based on microarray and RNA-seq data (standard mean difference, SMD = 1.62, 95 % CI: 1.23-2.00). And the area under curves (AUC) of summary receiver operator characteristic (SROC) was 0.87 (95 % CI: 0.84-0.90) based on 172 cases of LSCC and 59 cases of non-cancerous controls. Ninety genes were intersected by the ITGB4 related genes and LSCC differential expressed genes (DEGs) from all available microarray and RNA-seq datasets. Based on Gene Ontology (GO) analysis, the top terms of biological process (BP), cellular component (CC) and molecular function (MF) for the 90 ITGB4 related DEGs were extracellular matrix organization, basement membrane and extracellular matrix structural constituent, respectively. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis showed that ITGB4 related DEGs mainly participated in the pathways of ECM-receptor interaction, Focal adhesion and Small cell lung cancer. Moreover, the Protein-Protein Interaction (PPI) network indicated that ITGA3, ITGA5, ITGB4, MET, LAMA3, and COL4A1 might be the core genes of LSCC development related to ITGB4. In conclusion, high ITGB4 expression may lead to the occurrence and development of LSCC via various signaling pathways.

Bioinformatics analysis of esophageal cancer unveils an integrated mRNA-lncRNA signature for predicting prognosis

Esophageal cancer (ESCA) carries a poor prognosis among gastrointestinal malignancies. The present study developed a signature based on mRNAs and long non-coding RNAs (lncRNAs) to predict prognosis in ESCA by using The Cancer Genome Atlas database. By using least absolute shrinkage and selection operator penalized regression, a set of RNAs (three mRNAs and two lncRNAs) was identified and used to build a risk score system of ESCA prognosis, which was used to stratify patients having considerable diverse survival in the training set [hazard ratio (HR), 3.932; 95% CI, 1.555–9.944; P<0.002] into high- and low-risk groups. The authentication of the results was achieved through the test set (HR, 3.150; 95% CI, 1.113–8.918; P<0.02) and the entire set (HR, 3.181; 95% CI, 1.686–6.006; P<0.0002). The results from multivariate Cox proportional hazard regression analysis in the entire set suggested that the prognostic significance of this signature may be independent of patients' clinicopathological characteristics. Furthermore, this signature was associated with several molecular signaling pathways of cancer according to Gene Set Enrichment Analysis. In addition, a nomogram was built and the risk score and TNM stage were integrated to estimate the 1- and 3-year overall survival rates. The results from the present study demonstrated that the integrated mRNA-lncRNA signature may be considered as a novel biomarker for the prognosis of ESCA.

LINC00365 promotes colorectal cancer cell progression through the Wnt/β-catenin signaling pathway

In the past decade, substantial evidence established that long noncoding RNAs are serious about mediating the evolution of malignancies. In previous studies, LINC00365, which has not been reported in colorectal cancer (CRC), was selected using the bioinformatics analysis in GSE109454 and GSE41655 data sets. However, the function and mechanism of LINC00365 are still obscure. In our study, LINC00365 was found upregulated in CRC specimens and intimately connected with the prognosis of patients with CRC. In addition, LINC00365 overexpression enhances the cell abilities of proliferation, migration, and invasion in vitro. Meanwhile, mechanistic studies showed that LINC00365 might involve in CRC cell progression by mediating the Wnt/β-catenin pathway. Furthermore, LINC00365 upregulation increased CDK1 protein expression. In conclusion, this study suggests that LINC00365 acts as a vital part in facilitating CRC progression and might play as a therapeutic target for patients with CRC.

Analysis of Survival-Related lncRNA Landscape Identifies A Role for LINC01537 in Energy Metabolism and Lung Cancer Progression

Many long non-coding RNAs (lncRNAs) have emerged as good biomarkers and potential therapeutic targets for various cancers. We aimed to get a detailed understanding of the lncRNA landscape that is associated with lung cancer survival. A comparative analysis between our RNA sequencing (RNA-seq) data and TCGA datasets was conducted to reveal lncRNAs with significant correlations with lung cancer survival and then the association of the most promising lncRNA was validated in a cohort of 243 lung cancer patients. Comparing RNA-seq data with TCGA ones, 84 dysregulated lncRNAs were identified in lung cancer tissues, among which 10 lncRNAs were significantly associated with lung cancer survival. LINC01537 was the most significant one (p = 2.95 × 10⁻⁶). Validation analysis confirmed the downregulation of LINC01537 in lung cancer. LINC01537 was observed to inhibit tumor growth and metastasis. It also increased cellular sensitivity to nilotinib. PDE2A (phosphodiesterase 2A) was further identified to be a target of LINC01537 and it was seen that LINC01537 promoted PDE2A expression via RNA–RNA interaction to stabilize PDE2A mRNA and thus echoed effects of PDE2A on energy metabolism including both Warburg effect and mitochondrial respiration. Other regulators of tumor energy metabolism were also affected by LINC01537. These results elucidate a suppressed role of LINC01537 in lung cancer development involving tumor metabolic reprogramming, and we believe that it might be a biomarker for cancer survival prediction and therapy.

LINC01554-Mediated Glucose Metabolism Reprogramming Suppresses Tumorigenicity in Hepatocellular Carcinoma via Downregulating PKM2 Expression and Inhibiting Akt/mTOR Signaling Pathway

Background and Aims: Cancer cells prefer aerobic glycolysis to maintain growth advantages, but the role of long non-coding RNAs (lncRNAs) in glycometabolism still remains unclear. Here we identified one cytoplasmic lncRNA LINC01554 as a significantly downregulated lncRNA in hepatocellular carcinoma (HCC) and aimed to investigate its role in cellular glucose metabolism in the development and progression of HCC.

Methods: Quantitative real-time PCR was used to determine the expression level of LINC01554. Downregulation of LINC01554 by miR-365a at transcriptional level was assessed by luciferase reporter assay. Subcellular fractionation assay and RNA fluorescence in situ hybridization were performed to detect the subcellular localization of LINC01554. RNA pull-down assay, mass spectrometry, and RNA immunoprecipitation assay were used to identify the underlying molecular mechanisms. The tumor-suppressive function of LINC01554 was determined by both in vitro assay and nude mice xenograft model.

Results: LINC01554 was frequently downregulated in HCC, which was significantly associated with tumor invasion (P = 0.005), tumor size (P = 0.041), tumor staging (P = 0.023) and shorter survival (P = 0.035) of HCC patients. Luciferase reporter assay unraveled that LINC01554 was negatively regulated by miR-365a. Subcellular fractionation assay and RNA FISH revealed the cytoplasmic predominance of LINC01554 in MIHA cells and HCC clinical samples. Ectopic expression of LINC01554 inhibited HCC cell growth, colony formation in soft agar, foci formation, and tumor formation in nude mice. LINC01554 promoted the ubiquitin-mediated degradation of PKM2 and inhibited Akt/mTOR signaling pathway to abolish aerobic glycolysis in HCC cells. Further study found that LINC01554-knockout could effectively reverse the tumor-suppressive effect of LINC01554.

Conclusions: Our results identify LINC01554 as a novel tumor suppressor in HCC and unravel its underlying molecular mechanism in reprogramming cellular glucose metabolism. LINC01554 could possibly serve as a novel prognostic biomarker and provide the rationale for HCC therapy.

Identification of cancer prognosis-associated lncRNAs based on the miRNA-TF co-regulatory motifs and dosage sensitivity

By integrating dosage sensitivity and motif regulation data, we established a framework and identified a total of 33 cancer prognosis-associated lncRNAs.

Construction and analysis of dysregulated lncRNA-associated ceRNA network in colorectal cancer

Colorectal cancer (CRC) is one of the most frequently diagnosed digestive system cancer. The aim of the present study was to investigate the interactions among messenger RNAs (mRNAs), microRNAs (miRNAs), and long noncoding RNAs (lncRNAs) in CRC to reveal the mechanisms of CRC. Differentially expressed genes (DEGs) were identified from public gene expression data sets. One thousand eighty-one common dysregulated mRNAs in two data sets were identified. Gene function analysis and protein-protein interaction network analysis indicated that these DEGs might play important roles in CRC. LINC00365 was selected through coding- noncoding network analysis and its expression was validated upregulated in 22 paired clinical samples and four CRC cell lines. A competing endogenous RNA network composed of 70 miRNAs, nine mRNAs, and LINC00365 was constructed. Eight of nine mRNAs were validated upregulated in The Cancer Genome Atlas data set. Our results suggested that LINC00365 was an oncogene in CRC and it could regulate the expression of several mRNAs through sponging miRNAs.

PTPRF as a novel tumor suppressor through deactivation of ERK1/2 signaling in gastric adenocarcinoma

Background

Protein tyrosine phosphatase, receptor type F (PTPRF) is an important phosphatase playing roles in regulating cell growth, differentiation and oncogenic transformation. Overexpression of PTPRF has been observed in non-small cell lung cancer, but its clinical significance in other malignancies is still unknown.

Methods

We explored the expression pattern of PTPRF in gastric adenocarcinoma by using RT-qPCR and immunohistochemistry staining. The clinical significance of PTPRF was evaluated by univariate and multivariate analyses. Furthermore, the signaling pathways downstream of PTPRF was investigated by knockdown and overexpression assays combined with cellular studies.

Results

We found a remarkable down-regulation of PTPRF in gastric adenocarcinomas, which was significantly associated with advanced tumor TNM stages. Survival analysis showed that lower PTPRF level indicated a poorer overall survival of gastric adenocarcinoma patients. By conducting knockdown and overexpression studies in gastric adenocarcinoma cells, we revealed the role of PTPRF on inhibiting extracellular signal-regulated kinase-1/2 (ERK1/2) phosphorylation and its downstream signaling. Consistent with clinical findings, cellular results demonstrated that overexpressing PTPRF can significantly inhibit tumor migration and invasion, while silencing PTPRF showed opposite effects.

Conclusion

In conclusion, patients with lower PTPRF expression in gastric adenocarcinoma tissues were more predisposed to advanced tumor stage and unfavorable prognosis.

Decoding the LncRNA transcriptome of esophageal cancer: identification of clinically relevant LncRNAs

AIM

LncRNAs may act as promising biomarkers in esophageal cancer (EC). Here, we illustrate the LncRNA profile and their clinical relevance in EC.

PATIENTS & METHODS

In this study, we utilized the Cancer Genome Atlas RNA-sequencing and clinical data from 186 patients and 13 normal samples. Various statistical and gene set enrichment analysis (GSEA) were performed to identify the biomarkers.

RESULTS

In a differential expression analysis, we identified a total of 127 LncRNAs with more differentially expressed in EC compared with normal and showed their function using guilt-by-association analysis. We generated a LncRNAs prognostic signature for EC. Using Cox regression analysis, we showed the prognostic ability of LncRNAs' prognostic signature in training and test-cohort (p-value < 0.01).

CONCLUSION

In summary, we explored the LncRNA expression profile and their clinical utility in EC patients.

Protein-coding genes combined with long noncoding RNA as a novel transcriptome molecular staging model to predict the survival of patients with esophageal squamous cell carcinoma

BACKGROUND

Esophageal squamous cell carcinoma (ESCC) is the predominant subtype of esophageal carcinoma in China. This study was to develop a staging model to predict outcomes of patients with ESCC.

METHODS

Using Cox regression analysis, principal component analysis (PCA), partitioning clustering, Kaplan-Meier analysis, receiver operating characteristic (ROC) curve analysis, and classification and regression tree (CART) analysis, we mined the Gene Expression Omnibus database to determine the expression profiles of genes in 179 patients with ESCC from GSE63624 and GSE63622 dataset.

RESULTS

Univariate cox regression analysis of the GSE63624 dataset revealed that 2404 protein-coding genes (PCGs) and 635 long non-coding RNAs (lncRNAs) were associated with the survival of patients with ESCC. PCA categorized these PCGs and lncRNAs into three principal components (PCs), which were used to cluster the patients into three groups. ROC analysis demonstrated that the predictive ability of PCG-lncRNA PCs when applied to new patients was better than that of the tumor-node-metastasis staging (area under ROC curve [AUC]: 0.69 vs. 0.65, P < 0.05). Accordingly, we constructed a molecular disaggregated model comprising one lncRNA and two PCGs, which we designated as the LSB staging model using CART analysis in the GSE63624 dataset. This LSB staging model classified the GSE63622 dataset of patients into three different groups, and its effectiveness was validated by analysis of another cohort of 105 patients.

CONCLUSIONS

The LSB staging model has clinical significance for the prognosis prediction of patients with ESCC and may serve as a three-gene staging microarray.

In silico analysis of the potential mechanism of telocinobufagin on breast cancer MCF-7 cells

BACKGROUNDS AND AIMS

The extractives from a ChanSu, traditional Chinese medicine, have been discovered to possess anti-inflammatory and tumor-suppressing abilities. However, the molecular mechanism of telocinobufagin, a compound extracted from ChanSu, on breast cancer cells has not been clarified. The aim of this study is to investigate the underlying mechanism of telocinobufagin on breast cancer cells.

METHODS AND MATERIALS

The differentially expressed genes after telocinobufagin treatment on breast cancer cells were searched and downloaded from Gene Expression Omnibus (GEO), ArrayExpress and literatures. Bioinformatics tools were applied to further explore the potential mechanism of telocinobufagin in breast cancer using the Kyoto Encyclopedia of genes and genomes (KEGG) pathway, Gene ontology (GO) enrichment, panther, and protein-protein interaction analyses. To better comprehend the role of telocinobufagin in breast cancer, we also queried the Connectivity Map using the gene expression profiles of telocinobufagin treatment.

RESULTS

One GEO accession (GSE85871) provided 1251 differentially expressed genes after telocinobufagin treatment on MCF-7 cells. The pathway of neuroactive ligand-receptor interaction, cell adhesion molecules (CAMs), intestinal immune network for IgA production, hematopoietic cell lineage and calcium signaling pathway were the key pathways from KEGG analysis. IGF1 and KSR1, owning to higher protein levels in breast cancer tissues, IGF1 and KSR1 could be the hub genes related to telocinobufagin treatment. It was indicated that the molecular mechanism of telocinobufagin resembled that of fenspiride.

CONCLUSIONS

Telocinobufagin might regulate neuroactive ligand-receptor interaction pathway to exert its influences in breast cancer MCF-7 cells, and its molecular mechanism might share some similarities with fenspiride. This study only presented a comprehensive picture of the role of telocinobufagin in breast cancer MCF-7 cells using big data. However, more thorough and deeper researches are required to add to the validity of this study.

The crucial role of multiomic approach in cancer research and clinically relevant outcomes

Cancer with heavily economic and social burden is the hot point in the field of medical research. Some remarkable achievements have been made; however, the exact mechanisms of tumor initiation and development remain unclear. Cancer is a complex, whole-body disease that involves multiple abnormalities in the levels of DNA, RNA, protein, metabolite and medical imaging. Biological omics including genomics, transcriptomics, proteomics, metabolomics and radiomics aims to systematically understand carcinogenesis in different biological levels, which is driving the shift of cancer research paradigm from single parameter model to multi-parameter systematical model. The rapid development of various omics technologies is driving one to conveniently get multi-omics data, which accelerates predictive, preventive and personalized medicine (PPPM) practice allowing prediction of response with substantially increased accuracy, stratification of particular patients and eventual personalization of medicine. This review article describes the methodology, advances, and clinically relevant outcomes of different "omics" technologies in cancer research, and especially emphasizes the importance and scientific merit of integrating multi-omics in cancer research and clinically relevant outcomes.

LncRNA and mRNA integration network reconstruction reveals novel key regulators in esophageal squamous-cell carcinoma

Many experimental and computational studies have identified key protein coding genes in initiation and progression of esophageal squamous cell carcinoma (ESCC). However, the number of researches that tried to reveal the role of long non-coding RNAs (lncRNAs) in ESCC has been limited. LncRNAs are one of the important regulators of cancers which are transcribed dominantly in the genome and in various conditions. The main goal of this study was to use a systems biology approach to predict novel lncRNAs as well as protein coding genes associated with ESCC and assess their prognostic values. By using microarray expression data for mRNAs and lncRNAs from a large number of ESCC patients, we utilized "Weighted Gene Co-expression Network Analysis" (WGCNA) method to make a big coding-non-coding gene co-expression network, and discovered important functional modules. Gene set enrichment and pathway analysis revealed major biological processes and pathways involved in these modules. After selecting some protein coding genes involved in biological processes and pathways related to cancer, we used "LncTar", a computational tool to predict potential interactions between these genes and lncRNAs. By combining interaction results with Pearson correlations, we introduced some novel lncRNAs with putative key regulatory roles in the network. Survival analysis with Kaplan-Meier estimator and Log-rank test statistic confirmed that most of the introduced genes are associated with poor prognosis in ESCC. Overall, our study reveals novel protein coding genes and lncRNAs associated with ESCC, along with their predicted interactions. Based on the promising results of survival analysis, these genes can be used as good estimators of patients' survival, or even can be analyzed further as new potential signatures or targets for the therapy of ESCC disease.

Human hepatic gene expression signature of non-alcoholic fatty liver disease progression, a meta-analysis

Non-alcoholic fatty liver disease (NAFLD) is a wide-spread chronic liver condition that places patients at risk of developing cardiovascular diseases and may progress to cirrhosis or hepatocellular carcinoma if untreated. Challenges in clinical and basic research are caused by poor understanding of NAFLD mechanisms. The purpose of current study is to describe molecular changes occurring in human liver during NAFLD progression by defining a reproducible gene expression signature. We conduct a systematic meta-analysis of published human gene expression studies on liver biopsies and bariatric surgery samples of NAFLD patients. We relate gene expression levels with histology scores using regression models and identify a set of genes showing consistent-sign associations with NAFLD progression that are replicated in at least three independent studies. The analysis reveals genes that have not been previously characterized in the context of NAFLD such as HORMAD2 and LINC01554. In addition, we highlight biomarker opportunities for risk stratification and known drugs that could be used as tool compounds to study NAFLD in model systems. We identify gaps in current knowledge of molecular mechanisms of NAFLD progression and discuss ways to address them. Finally, we provide an extensive data supplement containing meta-analysis results in a computer-readable format.