An eight-long non-coding RNA signature as a candidate prognostic biomarker for bladder cancer

A metabolic reprogramming-related gene signature correlates with prognosis and proliferation of BLCA

Bladder cancer (BLCA) is one of the most frequent urothelium carcinoma, but with poor prognosis due to lack of reliable predictive biomarkers. Metabolic reprogramming involving in various nutrients, and is reported to be closely associated with malignant progression in BLCA. With the use of transcriptome sequencing data profiles of 349 patients from The Cancer Genome Atlas, we established a three-gene glycolysis-related signature to predict the prognosis of BLCA patients. Our signature constructed on the basis of AK3, GALK1 and NUP205 expression, detail features and interactions between these three genes were further explored. We established a nomogram by integrating clinical variables and the risk score. Glycolytic level and proliferation ability were detected to study the role and mechanisms of NUP205 on BLCA. The connections between three genes in our signature were independent. We found our signature gains more value for patients with highly malignant stage. The established nomogram also confirmed that the signature had a eligible clinically predict capacity. After inhibited NUP205 expression, we found the glycolysis level of BLCA cells decreased and proliferation ability suppressed, mainly through AMPK signaling pathway inactivation. Collectively, our study explored a three-gene glycolysis-related signature that predict the prognosis of patients with BLCA, and highlights NUP205 as a potential therapeutic target for inhibiting glycolytic processes and proliferation in BLCA cells.

Construction of a necroptosis-related lncRNA signature for predicting prognosis and revealing the immune microenvironment in bladder cancer

BACKGROUND

Bladder cancer (BCa) is a common malignancy in the urinary system. Necroptosis, a recently discovered form of programmed cell death, is closely associated with the development and progression of various types of tumors. Targeting necroptosis through anti-cancer strategies has shown potential as a therapy for cancer. We aimed to develop a necroptosis-related lncRNAs (NRlncRNAs) risk model that can predict the survival and tumor immunity of BCa patients.

METHODS

We analyzed sequencing data obtained from the TCGA database, and applied least absolute shrinkage and selection operator (LASSO) and Cox regression analysis to identify crucial NRlncRNAs for building a risk model. Using the risk score, we categorized patients into high- and low-risk groups, and assessed the accuracy with the area under the receiver operating characteristic (AUROC) and Kaplan-Meier curves. We performed the RT-qPCR to detect the expression differences of the genes based on the risk model.

RESULTS

We identified a total of 296 NRlncRNAs, and 6 of them were included in the prognostic model. The AUC values for 1-, 3-, and 5-year predictions were 0.675, 0.726 and 0.734, respectively. Our risk model demonstrated excellent predictive performance and served as an independent predictor with high predictive power. Additionally, we performed PCA, TMB, GSEA analyses, and evaluated immune cell infiltration, to reveal significant differences between the high- and low-risk groups in functional signaling pathways, immunological status, and mutation profiles. Finally, we assessed the chemotherapeutic response of several drugs. According to the RT-qPCR results, we found that four NRlncRNAs of the risk model were more highly expressed in BCa cell lines than human immortalized uroepithelial cell line and regulated the occurrence and progression of bladder cancer.

CONCLUSION

We constructed a novel NRlncRNAs-associated risk model, which could predict the prognosis and immune response of BCa patients.

Identification of pyroptosis-related lncRNA subtype and signature predicts the prognosis in bladder cancer

Pyroptosis is a new type of programmed cell death involved in all stages of tumorigenesis. Herein, a comprehensive study was conducted to evaluate the prognostic significance of pyroptosis-related lncRNAs in bladder cancer. Consensus clustering analysis was performed to identify the subclusters of bladder cancer. The prognostic pyroptosis-related lncRNA signature was constructed using LASSO Cox regression analysis. Consensus clustering identified 2 clusters of bladder cancer. Interestingly, significant differences in the ESTIMAE score, immune cell infiltration and immune checkpoint expression were obtained between the 2 clusters. A signature consisting of 11 pyroptosis-related lncRNAs was established and it had a good performance in predicting the overall survival rate of bladder cancer, with an AUC of 0.713. Moreover, pyroptosis-related lncRNA signature acted as a risk factor in bladder cancer. Bladder cancer patients with high-risk score had a higher tumor grade and higher clinical stage. A significant correlation was obtained between the risk score and immune cell infiltration. The expression of most checkpoints was higher in bladder cancer patients with high-risk score. A novel pyroptosis-related lncRNA signature was identified with prognostic value for bladder cancer patients. Pyroptosis-related lncRNAs have a potential role in cancer immunology and may serve as prognostic or therapeutic targets in bladder cancer.

LncRNA FAM225B Regulates PDIA4-Mediated Ovarian Cancer Cell Invasion and Migration via Modulating Transcription Factor DDX17

Objective

This study aimed to explore the roles and mechanisms of lncRNA FAM225B and PDIA4 in ovarian cancer.

Methods

RT-qPCR and Western blot assays were performed to detect the expression levels of the lncRNAs FAM225B, DDX17, and PDIA4 in the serum of patients with ovarian cancer and cell lines. Cells were transfected with lncRNA FAM225B- and PDIA4-related vectors to determine the malignant phenotypes using functional experiments. The mutual binding of lncRNA FAM225B and DDX17 was verified using RNA pull-down and RIP assays.

Results

The expression of lncRNAs FAM225B and PDIA4 was decreased in the serum of patients with ovarian cancer and cell lines. Restoration of lncRNA FAM225B or PDIA4 reduced cell proliferation, migration, and invasion abilities and elevated the apoptosis rate, whereas suppression of lncRNA FAM225B or PDIA4 exhibited an inverse trend. RNA pull-down and RIP assays revealed a direct interaction between lncRNA FAM225B and DDX17. ChIP assay revealed a relationship between DDX17 and the PDIA4 promoter. LncRNA FAM225B and DDX17 positively regulate PDIA4 expression. Downregulation of PDIA4 expression counteracts the suppressive effect of lncRNA FAM225B overexpression in ovarian cancer cells.

Conclusion

This research study supports the fact that lncRNA FAM225B in ovarian cancer can upregulate PDIA4 by directly binding to DDX17, inhibiting the activities of ovarian cancer cells.

Crosstalk between autophagy and bladder transitional cell carcinoma by autophagy-related lncRNAs

The aim of this study was to investigate the crosstalk between autophagy and bladder transitional cell carcinoma (TCC) by autophagy-related long noncoding RNAs (lncRNAs). A total of 400 TCC patients from The Cancer Genome Atlas were enrolled in this study. We identified the autophagy-related lncRNA expression profile of the TCC patients and then constructed a prognostic signature using the least absolute shrinkage and selection operation and Cox regression. Risk, survival, and independent prognostic analyses were carried out. Receiver operating characteristic curve, nomogram, and calibration curves were explored. Gene Set Enrichment Analysis was employed to verify the enhanced autophagy-related functions. Finally, we compared the signature with several other lncRNA-based signatures. A 9-autophagy-related lncRNA signature was established by least absolute shrinkage and selection operation-Cox regression that was significantly associated with overall survival in TCC. Among them, 8 of the 9 lncRNAs were protective factors while the remaining was a risk factor. The risk scores calculated by the signature showed significant prognostic value in survival analysis between the high- or low-risk groups. The 5-year survival rate for the high-risk group was 26.0% while the rate for the low-risk group was 56.0% (P < .05). Risk score was the only significant risk factor in the multivariate Cox regression survival analysis (P < .001). A nomogram connecting this signature with clinicopathologic characteristics was assembled. To assess the performance of the nomogram, a C-index (0.71) was calculated, which showed great convergence with an ideal model. The Gene Set Enrichment Analysis results demonstrated 2 major autophagy-related pathways were significantly enhanced in TCC. And this signature performed a similar predictive effect as other publications. The crosstalk between autophagy and TCC is significant, and this 9 autophagy-related lncRNA signature is a great predictor of TCC.

Predicting Recurrence of Non-Muscle-Invasive Bladder Cancer: Current Techniques and Future Trends

Bladder cancer (BC) is the 10th most common cancer globally and has a high mortality rate if not detected early and treated promptly. Non-muscle-invasive BC (NMIBC) is a subclassification of BC associated with high rates of recurrence and progression. Current tools for predicting recurrence and progression on NMIBC use scoring systems based on clinical and histopathological markers. These exclude other potentially useful biomarkers which could provide a more accurate personalized risk assessment. Future trends are likely to use artificial intelligence (AI) to enhance the prediction of recurrence in patients with NMIBC and decrease the use of standard clinical protocols such as cystoscopy and cytology. Here, we provide a comprehensive survey of the most recent studies from the last decade (N = 70 studies), focused on the prediction of patient outcomes in NMIBC, particularly recurrence, using biomarkers such as radiomics, histopathology, clinical, and genomics. The value of individual and combined biomarkers is discussed in detail with the goal of identifying future trends that will lead to the personalized management of NMIBC.

ADH1C inhibits progression of colorectal cancer through the ADH1C/PHGDH /PSAT1/serine metabolic pathway

Colorectal cancer (CRC) is the third most common cancer in men and the second most common cancer in women worldwide. CRC is the second leading cause of cancer-related deaths. Although some progress in the treatment of CRC has been achieved, the molecular mechanism of CRC is still unclear. In this study, alcohol dehydrogenase 1C(ADH1C) was first identified as a target gene closely associated with the development of CRC by the comprehensive application of transcriptomics, proteomics, metabonomics and in silico analysis. The ADH1C mRNA and protein expression in CRC cell lines and tumor tissues was lower than that in normal intestinal epithelial cell lines and healthy tissues. Overexpression of ADH1C inhibited the growth, migration, invasion and colony formation of CRC cell lines and prevented the growth of xenograft tumors in nude mice. The inhibitory effects of ADH1C on CRC cells in vitro were exerted by reducing the expression of PHGDH/PSAT1 and the serine level. This inhibition could be partially reversed by adding serine to the culture medium. These results showed that ADH1C is a potential drug target in CRC.

Characterization and validation of a ferroptosis-related LncRNA signature as a novel prognostic model for lung adenocarcinoma in tumor microenvironment

Ferroptosis is a more relatively recently identified type of programmed cell death, which is associated with tumor progression. However, the mechanism underlying the effect of ferroptosis-related long non-coding RNAs (lncRNAs) in lung adenocarcinoma (LUAD) remains elusive. Therefore, the current study aimed to investigate the role of ferroptosis-related lncRNAs in LUAD and to develop a prognostic model. The clinicopathological characteristics of patients and the gene sequencing data were obtained from The Cancer Genome Atlas, while the ferroptosis-associated mRNAs were downloaded from the FerrDb database. A ferroptosis-related lncRNA signature was established with Least Absolute Shrinkage and Selection Operator Cox regression analysis. Furthermore, the risk scores of ferroptosis-related lncRNAs were calculated and LUAD patients were then assigned to high- and low-risk groups based on the median risk score. The prognostic model was established by K-M plotters and nomograms. Gene set enrichment analysis (GSEA) was performed to evaluate the association between immune responses and ferroptosis-related lncRNAs. A total of 10 ferroptosis-related lncRNAs were identified as independent predictors of LUAD outcome, namely RP11-386M24.3, LINC00592, FENDRR, AC104699.1, AC091132.1, LANCL1-AS1, LINC-PINT, IFNG-AS1, LINC00968 and AC006129.2. The area under the curve verified that the established signatures could determine LUAD prognosis. The nomogram model was used to assess the predictive accuracy of the established signatures. Additionally, GSEA revealed that the 10 ferroptosis-related lncRNAs could be involved in immune responses in LUAD. Overall, the results of the current study may provide novel insights into the development of novel therapies or diagnostic strategies for LUAD.

Identification of TNFAIP6 as a hub gene associated with the progression of glioblastoma by weighted gene co-expression network analysis

This study aims to discover the genetic modules that distinguish glioblastoma multiforme (GBM) from low‐grade glioma (LGG) and identify hub genes. A co‐expression network is constructed using the expression profiles of 28 GBM and LGG patients from the Gene Expression Omnibus database. The authors performed gene ontology (GO) and Kyoto encyclopaedia of genes and genomes (KEGG) analysis on these genes. The maximal clique centrality method was used to identify hub genes. Online tools were employed to confirm the link between hub gene expression and overall patient survival rate. The top 5000 genes with major variance were classified into 18 co‐expression gene modules. GO analysis indicated that abnormal changes in ‘cell migration’ and ‘collagen metabolic process’ were involved in the development of GBM. KEGG analysis suggested that ‘focal adhesion’ and ‘p53 signalling pathway’ regulate the tumour progression. TNFAIP6 was identified as a hub gene, and the expression of TNFAIP6 was increased with the elevation of pathological grade. Survival analysis indicated that the higher the expression of TNFAIP6, the shorter the survival time of patients. The authors identified TNFAIP6 as the hub gene in the progression of GBM, and its high expression indicates the poor prognosis of the patients.

Comprehensive analysis of the PD-L1 and immune infiltrates of N6-methyladenosine related long non-coding RNAs in bladder cancer

Bladder cancer (BLCA) is one of the most frequent genitourinary cancers, with a high rate of morbidity and mortality. The connection of m6A-related lncRNAs with PD-L1 and tumor immune microenvironment (TIME) in BLCA prognosis was extensively investigated in this study, which could suggest novel therapeutic targets for further investigation. 30 m6A-associated lncRNAs with predictive values from the TCGA data set were identified with co-expression analysis. Cluster2 was correlated with a poor prognosis, upregulated PD-L1 expression, and higher immune ratings. Cluster2 had larger amounts of resting CD4 memory-activated T cells, M2 macrophages, neutrophils, and NK cells infiltration. "CHEMOKINE SIGNALING PATHWAY" was the most significantly enriched signaling pathway according to GSEA, which may play an important role in the different immune cell infiltrates between cluster1/2. The risk model for m6A-related lncRNAs could be employed in a prognostic model to predict BLCA prognosis, regardless of other clinical features. Collectively, m6A-related lncRNAs were linked to PD-L1 and TIME, which would dynamically affect the number of tumor-infiltrating immune cells. m6A-related lncRNAs may be key mediators of PD-L1 expression and immune cells infiltration and may strongly affect the TIME of BLCA.

The nomogram based on the 6-lncRNA model can promote the prognosis prediction of patients with breast invasive carcinoma

Long non-coding RNA (lncRNA) is a prognostic biomarker for many types of cancer. Here, we aimed to study the prognostic value of lncRNA in Breast Invasive Carcinoma (BRCA). We downloaded expression profiles from The Cancer Genome Atlas (TCGA) datasets. Subsequently, we screened the differentially expressed genes between normal tissues and tumor tissues. Univariate Cox, LASSO regression, and multivariate Cox regression analysis were used to construct a lncRNA prognostic model. Finally, a nomogram based on the lncRNAs model was developed, and weighted gene co-expression network analysis (WGCNA) was used to predict mRNAs related to the model, and to perform function and pathway enrichment. We constructed a 6-lncRNA prognostic model. Univariate and multivariate Cox regression analysis showed that the 6-lncRNA model could be used as an independent prognostic factor for BRCA patients. We developed a nomogram based on the lncRNAs model and age, and showed good performance in predicting the survival rates of BRCA patients. Also, functional pathway enrichment analysis showed that genes related to the model were enriched in cell cycle-related pathways. Tumor immune infiltration analysis showed that the types of immune cells and their expression levels in the high-risk group were significantly different from those in the low-risk group. In general, the 6-lncRNA prognostic model and nomogram could be used as a practical and reliable prognostic tool for invasive breast cancer.

Prognostic value of immune-related lncRNA pairs in patients with bladder cancer

BACKGROUND

The characteristics of immune-related long non-coding ribonucleic acids (ir-lncRNAs), regardless of their specific levels, have important implications for the prognosis of patients with bladder cancer.

METHODS

Based on The Cancer Genome Atlas database, original transcript data were analyzed. The ir-lncRNAs were obtained using a coexpression method, and their differentially expressed pairs (DE-ir-lncRNAs) were identified by univariate analysis. The lncRNA pairs were verified using a Lasso regression test. Thereafter, receiver operating characteristic curves were generated, and an optimal risk model was established. The clinical value of the model was verified through the analysis of patient survival rates, clinicopathological characteristics, presence of tumor-infiltrating immune cells, and chemotherapy efficacy evaluation.

RESULTS

In total, 49 pairs of DE-ir-lncRNAs were identified, of which 21 were included in the Cox regression model. A risk regression model was established on the premise of not involving the specific expression value of the transcripts.

CONCLUSIONS

The method and model used in this study have important clinical predictive value for bladder cancer and other malignant tumors.

Exosomal-mediated transfer of APCDD1L-AS1 induces 5-fluorouracil resistance in oral squamous cell carcinoma via miR-1224-5p/nuclear receptor binding SET domain protein 2 (NSD2) axis

Oral squamous cell carcinoma (OSCC) poses a threat to public health worldwide. LncRNA APCDD1L-AS1 has been reported to participate in tumorigenesis and development of acquired chemoresistance. However, the role of APCDD1L-AS1 in 5-fluorouracil (5-FU) resistance regulation within OSCC is still obscure. In this study, 5-FU-resistant cell models were established with OSCC cell lines (HSC-3 and HN-4). Gene expressions and protein levels were detected by RT-qPCR and Western blotting, respectively. CCK-8, colony forming, and flow cytometry were utilized to measure IC50 value, cell viability, and cell apoptosis of 5-FU-resistant OSCC cells. Dual-luciferase reporter assay and RIP assay were applied to identify the associations between miR-1224-5p and APCDD1L-AS1 or NSD2. Herein, high APCDD1L-AS1 expression was shown in OSCC tissues and cells resistant to 5-FU and related to the worse prognosis of OSCC patients. APCDD1L-AS1 knockdown impaired 5-FU resistance in 5-FU-resistant OSCC cells by reducing IC₅₀ value, suppressing cell viability, and accelerating cell apoptosis. Besides, extracellular APCDD1L-AS1 could be transferred to sensitive cells via exosome incorporation, thereby transmitting 5-FU resistance in OSCC cells. Besides, miR-1224-5p was a molecular target of APCDD1L-AS1 and directly targeted NSD2 in 5-FU-resistant cells. MiR-1224-5p exhibited a much lower level in 5-FU-resistant tissues and increased 5-FU sensitivity in 5-FU-resistant OSCC cells. Moreover, NSD2 upregulation neutralized the influence of blocking APCDD1L-AS1 in HSC-3/5-FU and HN-4/5-FU cells on 5-FU resistance. To sum up, our study demonstrated that exosomal APCDD1L-AS1 conferred resistance to 5-FU in HSC-3/5-FU and HN-4/5-FU cells via the miR-1224-5p/NSD2 axis, thus providing a novel target for OSCC chemoresistance.

Effect of Aberrant Long Noncoding RNA on the Prognosis of Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is a kind of lethal cancer. Although there are mature treatment methods, there is still a lack of rigorous and scientific means for cancer diagnosis. Long noncoding RNAs (lncRNAs) are a kind of noncoding RNA (ncRNA). Recent studies find that alteration of lncRNA expression is related to the occurrence of many cancers. In order to find lncRNAs which can effectively predict the prognosis of ccRCC, RNA-seq count data and clinical information were downloaded from TCGA-KIRC, and gene expression profiles from 530 patients were included. Then, K-means was used for clustering, and the number of clusters was determined to be 5. The R-package "edgeR" was used to perform differential expression analysis. Subsequently, a risk model composed of 10 lncRNA biomarkers significantly related to prognosis was identified via Cox and LASSO regression analyses. Then, patients were divided into two groups according to the model-based risk score, and then, GSEA pathway enrichment was performed. The results showed that metabolism- and mTOR-related pathways were activated while immune-related pathways were inhibited in the high-risk patients. Combined with previous studies, it is believed that these 10 lncRNAs are potential targets for the treatment of ccRCC. In addition, Cox regression analysis was used to verify the independence of the risk model, and as results revealed, the risk model can be used to independently predict the prognosis of patients. In conclusion, our study found 10 lncRNAs related to the prognosis of ccRCC and provided new ideas for clinical diagnosis and drug development.

Scoring System Based on RNA Modification Writer-Related Genes to Predict Overall Survival and Therapeutic Response in Bladder Cancer

Introduction

It’s widely reported the “writer” enzymes mediated RNA adenosine modifications which is known as a crucial mechanism of epigenetic regulation in development of tumor and the immunologic response in many kinds of cancers. However, the potential roles of these writer genes in the progression of bladder cancer (BLCA) remain unclear.

Materials and Methods

We comprehensively described the alterations of 26 RNA modification writer genes in BLCA from the genetic and transcriptional fields and identified writer-related genes from four independent datasets. Utilizing least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression, we constructed a ten writer-related gene signature. After that, we confirmed the predictive and prognostic value of this signature on another six independent datasets and established a nomogram to forecast the overall survival (OS) and mortality odds of BLCA patients clinically.

Results

The writer-related genes signature showed good performance in predicting the OS for BLCA patients. Moreover, the writer-related gene signature was related to EMT-related pathways and immune characteristics. Furthermore, the immune cell infiltration levels of CD8 T cells, cytotoxic cells, M1/2 macrophage cells and tumor mutation burden might be able to predict which patients will benefit from immunotherapy. This could also be reflected by the writer-related gene signature.

Conclusions

This signature might play an important role in precision individualized immunotherapy. The present work highlights the crucial clinical implications of RNA modifications and may help developing individualized therapeutic strategies for patients with BLCA.

Identification of immune-related genes that predict prognosis and risk of bladder cancer: bioinformatics analysis of TCGA database

Background: Bladder cancer (BLCA) is the major tumor of the urinary system, and immune-related genes (IRGs) contribute significantly to its initiation and prognosis.

Results: A total of 51 prognostic IRGs significantly associated with overall survival were identified. Functional enrichment analysis revealed that these genes were actively involved in tumor-related functions and pathways. Using multivariate Cox regression analysis, we detected 11 optimal IRGs (ADIPOQ, PPY, NAMPT, TAP1, AHNAK, OLR1, PDGFRA, IL34, MMP9, RAC3, and SH3BP2). We validated the prognostic value of this signature in two validation cohorts: GSE13507 (n = 165) and GSE32894 (n = 224). Furthermore, we performed a western blot and found that the expression of these IRGs matched their mRNA expression in TCGA. Moreover, correlations between risk score and immune-cell infiltration indicated that the prognostic signature reflected infiltration by several types of immune cells.

Conclusion: We identified and validated an 11-IRG-based risk signature that may be a reliable tool to evaluate the prognosis of BLCA patients and help to devise individualized immunotherapies.

Methods: Bioinformatics analysis was performed using TCGA and ImmPort databases. Cox regression was used to identify prognostic signatures. Two external GEO cohorts and western blotting of samples were performed to validate the IRG signature.

m6A-Related lncRNA to Develop Prognostic Signature and Predict the Immune Landscape in Bladder Cancer

Abnormal m6A methylation plays a significant role in cancer progression. Increasingly, researchers have focused on developing lncRNA signatures to evaluate the prognosis of cancer patients. The specific function of m6A-related lncRNAs in the prognosis of bladder cancer patients and the immune microenvironment of bladder cancer remains elusive. Herein, we performed a comprehensive analysis of m6A-related lncRNA prognostic values and their association with the immune microenvironment in bladder cancer using the TCGA dataset. A total of 9 m6A-related lncRNAs were dramatically correlated with overall survival outcomes in bladder cancer. Two molecular subtypes (cluster 1 and cluster 2) were identified by consensus clustering for 9 m6A-related prognostic lncRNAs. Cluster 1 was significantly correlated with poor prognosis, advanced clinical stage, higher PD-L1 expression, a higher ESTIMATEScore and immuneScore, and distinct immune cell infiltration. GSEA revealed the enrichment of apoptosis and the JAK-STAT signaling pathway in cluster 2. A prognostic risk score was constructed using 9 m6A-related prognostic lncRNAs, which functioned as an independent prognostic factor for bladder cancer. Moreover, bladder cancer patients in the low-risk score group had a higher pN stage, pT stage, and clinical stage and a lower tumor grade and immuneScore. The risk score was correlated with the infiltration levels of certain immune cells, including B cells, plasma cells, follicular helper T cells, regulatory T cells, resting NK cells, neutrophils, M0 macrophages, M1 macrophages, and M2 macrophages. Collectively, our study elucidated the important role of m6A-related lncRNAs in the prognosis of bladder cancer patients and in the bladder cancer immune microenvironment. The results suggest that the components of the m6A-related prognostic lncRNA signature might serve as a crucial mediator of the immune microenvironment in bladder cancer, representing promising therapeutic targets for improving immunotherapeutic efficacy.

Identification of a Prognostic Signature Associated With the Homeobox Gene Family for Bladder Cancer

Background: Bladder cancer (BLCA) is a common malignant tumor of the genitourinary system, and there is a lack of specific, reliable, and non-invasive tumor biomarker tests for diagnosis and prognosis evaluation. Homeobox genes play a vital role in BLCA tumorigenesis and development, but few studies have focused on the prognostic value of homeobox genes in BLCA. In this study, we aim to develop a prognostic signature associated with the homeobox gene family for BLCA. Methods: The RNA sequencing data, clinical data, and probe annotation files of BLCA patients were downloaded from the Gene Expression Omnibus database and the University of California, Santa Cruz (UCSC), Xena Browser. First, differentially expressed homeobox gene screening between tumor and normal samples was performed using the "limma" and robust rank aggregation (RRA) methods. The mutation data were obtained with the "TCGAmutation" package and visualized with the "maftools" package. Kaplan-Meier curves were plotted with the "survminer" package. Then, a signature was constructed by logistic regression analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using "clusterProfiler." Furthermore, the infiltration level of each immune cell type was estimated using the single-sample gene set enrichment analysis (ssGSEA) algorithm. Finally, the performance of the signature was evaluated by receiver-operating characteristic (ROC) curve and calibration curve analyses. Results: Six genes were selected to construct this prognostic model: TSHZ3, ZFHX4, ZEB2, MEIS1, ISL1, and HOXC4. We divided the BLCA cohort into high- and low-risk groups based on the median risk score calculated with the novel signature. The overall survival (OS) rate of the high-risk group was significantly lower than that of the low-risk group. The infiltration levels of almost all immune cells were significantly higher in the high-risk group than in the low-risk group. The average risk score for the group that responded to immunotherapy was significantly lower than that of the group that did not. Conclusion: We constructed a risk prediction signature with six homeobox genes, which showed good accuracy and consistency in predicting the patient's prognosis and response to immunotherapy. Therefore, this signature can be a potential biomarker and treatment target for BLCA patients.

Identification of a Ferroptosis-Related LncRNA Signature as a Novel Prognosis Model for Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) is a highly heterogeneous malignancy, which makes prognosis prediction of LUAD very challenging. Ferroptosis is an iron-dependent cell death mechanism that is important in the survival of tumor cells. Long non-coding RNAs (lncRNAs) are considered to be key regulators of LUAD development and are involved in ferroptosis of tumor cells, and ferroptosis-related lncRNAs have gradually emerged as new targets for LUAD treatment and prognosis. It is essential to determine the prognostic value of ferroptosis-related lncRNAs in LUAD. In this study, we obtained RNA sequencing (RNA-seq) data and corresponding clinical information of LUAD patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database and ferroptosis-related lncRNAs by co-expression analysis. The best predictors associated with LUAD prognosis, including C5orf64, LINC01800, LINC00968, LINC01352, PGM5-AS1, LINC02097, DEPDC1-AS1, WWC2-AS2, SATB2-AS1, LINC00628, LINC01537, LMO7DN, were identified by Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analysis, and the LUAD risk prediction model was successfully constructed. Kaplan-Meier analysis, receiver operating characteristic (ROC) time curve analysis and univariate and multivariate Cox regression analysis and further demonstrated that the model has excellent robustness and predictive ability. Further, based on the risk prediction model, functional enrichment analysis revealed that 12 prognostic indicators involved a variety of cellular functions and signaling pathways, and the immune status was different in the high-risk and low-risk groups. In conclusion, a risk model of 12 ferroptosis related lncRNAs has important prognostic value for LUAD and may be ferroptosis-related therapeutic targets in the clinic.

Development and Validation of a Prognostic Model for Predicting Overall Survival in Patients With Bladder Cancer: A SEER-Based Study

OBJECTIVE

To establish a prognostic model for Bladder cancer (BLCA) based on demographic information, the American Joint Commission on Cancer (AJCC) 7th staging system, and additional treatment using the surveillance, epidemiology, and end results (SEER) database.

METHODS

Cases with BLCA diagnosed from 2010-2015 were collected from the SEER database, while patient records with incomplete information on pre-specified variables were excluded. All eligible cases were included in the full analysis set, which was then split into training set and test set with a 1:1 ratio. Univariate and multivariate Cox regression analyses were conducted to identify prognostic factors for overall survival (OS) in BLCA patients. With selected independent prognosticators, a nomogram was mapped to predict OS for BLCA. The nomogram was evaluated using receiver operating characteristic (ROC) analysis and calibration plot in both the training and test sets. The area under curve [AUC] of the nomogram was calculated and compared with clinicopathological indicators using the full analysis set. Statistical analyses were conducted using the R software, where P-value <0.05 was considered significant.

RESULTS

The results indicated that age, race, sex, marital status, histology, tumor-node-metastasis (TNM) stages based on the AJCC 7th edition, and additional chemotherapy were independent prognostic factors for OS in patients with BLCA. Patients receiving chemotherapy tend to have better survival outcomes than those without. The proposed nomogram showed decent classification (AUCs >0.8) and prediction accuracy in both the training and test sets. Additionally, the AUC of the nomogram was observed to be better than that of conventional clinical indicators.

CONCLUSIONS

The proposed nomogram incorporated independent prognostic factors including age, race, sex, marital status, histology, tumor-node-metastasis (TNM) stages, and additional chemotherapy. Patients with BLCA benefit from chemotherapy on overall survival. The nomogram-based prognostic model could predict overall survival for patients with BLCA with accurate stratification, which is superior to clinicopathological factors.

Immune Infiltration Subtypes Characterization and Identification of Prognosis-Related lncRNAs in Adenocarcinoma of the Esophagogastric Junction

The incidence of adenocarcinoma of the esophagogastric junction (AEG) has markedly increased worldwide. However, the precise etiology of AEG is still unclear, and the therapeutic options thus remain limited. Growing evidence has implicated long non-coding RNAs (lncRNAs) in cancer immunomodulation. This study aimed to examine the tumor immune infiltration status and assess the prognostic value of immune-related lncRNAs in AEG. Using the ESTIMATE method and single-sample GSEA, we first evaluated the infiltration level of 28 immune cell types in AEG samples obtained from the TCGA dataset (N=201). Patients were assigned into high- and low-immune infiltration subtypes based on the immune cell infiltration’s enrichment score. GSEA and mutation pattern analysis revealed that these two immune infiltration subtypes had distinct phenotypes. We identified 1470 differentially expressed lncRNAs in two immune infiltration subtypes. From these differentially expressed lncRNAs, six prognosis-related lncRNAs were selected using the Cox regression analysis. Subsequently, an immune risk signature was constructed based on combining the values of the six prognosis-associated lncRNAs expression levels and multiple regression coefficients. To determine the risk model’s prognostic capability, we performed a series of survival analyses with Kaplan–Meier methods, Cox proportional hazards regression models, and the area under receiver operating characteristic (ROC) curve. The results indicated that the immune-related risk signature could be an independent prognostic factor with a significant predictive value in patients with AEG. Furthermore, the immune-related risk signature can effectively predict the response to immunotherapy and chemotherapy in AEG patients. In conclusion, the proposed immune-related lncRNA prognostic signature is reliable and has high survival predictive value for patients with AEG and is a promising potential biomarker for immunotherapy.

Identification of a Nine Immune-Related lncRNA Signature as a Novel Diagnostic Biomarker for Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) ranks fifth among common cancers and is the second most common cause of cancer-related mortality worldwide. This study is aimed at identifying an immune-related long noncoding RNA (lncRNA) signature as a potential biomarker with prognostic value to improve early diagnosis and provide potential therapeutic targets for HCC patients. The subjects of this study were HCC samples with complete transcriptome data and clinical information downloaded from The Cancer Genome Atlas (TCGA) database. We then extracted the immune-related mRNA and lncRNA expression profiles. Based on the expression profiles of immune-related lncRNAs, we identified a nine-lncRNA signature that was related to the progression of HCC. The risk score was calculated based on the expression level of the nine lncRNAs of each sample, which divided patients into high-risk and low-risk groups. We found that the increased risk score was associated with a poor prognosis of HCC patients. To assess the accuracy of the survival model, we calculated a receiver operating characteristic (ROC) for validation. The curve showed that the area under the curve (AUC) for the risk score was 0.792. Besides, both principal component analysis (PCA) and gene set enrichment analysis (GSEA) were further used for functional annotation. We found that the distribution patterns were different between the low-risk and high-risk groups in PCA, and the underlying mechanism by which the nine lncRNAs promoted the progression of HCC involved an abnormal immune status. Finally, we analyzed the infiltration of twenty-nine kinds of immune cells and the activation of immune function in HCC using the ssGSEA algorithm. The results showed that aDCs, iDCs, macrophages, Tfh, Th1, Treg, and NK cells were correlated with the progress of HCC patients. And the immune functions including APC costimulation, CCR, check point, HLA, MHC class I, and Type II IFN responses were also significantly different between the high-risk and low-risk groups. In conclusion, our study identified a nine-lncRNA signature with potential prognostic value for patients with HCC, which could be used as a new biomarker for the diagnosis and immunotherapy of HCC.

Identification of a novel immune microenvironment signature predicting survival and therapeutic options for bladder cancer

Few studies have investigated the potential of tumor immune microenvironment genes as indicators of urinary bladder cancer. Here, we sought to establish an immune-related gene signature for determining prognosis and treatment options. We developed a ten-gene tumor immune microenvironment signature and evaluated its prognostic capacity on internal and external cohorts. Multivariate Cox regression and nomogram analyses revealed the prognostic risk model as an independent and effective indicator of prognosis. We observed lower proportions of CD8+ T cells, dendritic cells, regulatory T cells, higher proportions of macrophages and neutrophils in high UBC risk group. UBC tissues with high-risk score tend to exhibit high TP53 and RB1 mutation rates, high PD1/PD-L1 expression and poor-survival basal squamous subtypes, while those with low-risk score tend to have high FGFR3 mutation rates and luminal papillary subtypes. Unexpectedly, we found a highly significant positive correlation between glycolytic genes and risk score, highlighting metabolic competition in tumor ecosystem and potential therapeutic avenues. Our study thus revealed a tumor immune microenvironment signature for predicting prognostic and response to immune checkpoint inhibitors against bladder cancer. Prospective studies are required to further test the predictive capacity of this model.

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.

Identification of immune-related LncRNA for predicting prognosis and immunotherapeutic response in bladder cancer

Long noncoding RNAs (lncRNAs) have multiple functions in the cancer immunity response and the tumor microenvironment. To investigate the immune-related lncRNA (IRlncRNA) signature for predicting prognosis and immunotherapeutic response in bladder cancer (BLCA), we extracted BLCA data from The Cancer Genome Atlas (TCGA) database. Finally, a total of 405 cases were enrolled and 8 prognostic IRlncRNAs (MIR181A2HG, AC114730.3, LINC00892, PTPRD-AS1, LINC01013, MRPL23-AS1,LINC01395, AC002454.1) were identified in the training set. Risk scores were calculated to divide patients into high-risk and low-risk groups, and the high-risk patients tended to have a poor overall survival (OS). Multivariate Cox regression analysis confirmed that the IRlncRNA signature could be an independent prognostic factor. The results were subsequently confirmed in the validating set. Additionally, this 8-IRlncRNA classifier was related to recurrence free survival (RFS) of BLCA. Functional characterization revealed this signature mediated immune-related phenotype. This signature was also associated with immune cell infiltration (i.e., macrophages M0, M2, Tregs, CD8 T cells, and neutrophils) and immune checkpoint inhibitors (ICIs) immunotherapy-related biomarkers [mismatch repair (MMR) genes, tumor mutation burden (TMB) and immune checkpoint genes]. The present study highlighted the value of the 8-IRlncRNA signature as a predictor of prognosis and immunotherapeutic response in BLCA.

LINC01235-TWIST2 feedback loop facilitates epithelial-mesenchymal transition in gastric cancer by inhibiting THBS2

Although the anomalous expression of long non-coding RNAs (lncRNAs) has been extensively investigated in numerous carcinomas including gastric cancer (GC), their function remains unclear. The aim of our study was to explore the role of LINC01235 in GC. We used real-time quantitative PCR (RT-qPCR) to measure the expression of LINC01235 and twist family bHLH transcription factor 2 (TWIST2) in GC tissues. Scratch and transwell assays were performed to evaluate cellular capacity for migration and invasion. Gene relationships were explored by Weighted Gene Co-Expression Network Analysis (WGCNA). We measured TWIST2, thrombospondin 2 (THBS2) and epithelial-mesenchymal transition (EMT)-related proteins with western blot. We also used Pearson correlation analysis and the Kaplan-Meier method to detect associations among genes and overall survival. We found that LINC01235 was upregulated in GC tissues and cells. LINC01235 down-regulation restricted migration and invasion. Interestingly, we found the LINC01235-TWIST2-THBS2 axis induced EMT. Additionally, TWIST2 upregulated LINC01235 transcription in luciferase and chromatin immunoprecipitation (ChIP) assays. Bioinformatics analysis showed that microRNA (miR)-6852-5p might be a key gene involved in the regulation of TWIST2 by LINC01235. The LINC01235-TWIST2 positive feedback loop mainly affected migration and invasion of GC cells, which suggests it may serve as a potential therapeutic target in gastric cancer.

m6A RNA methylation regulators can contribute to malignant progression and impact the prognosis of bladder cancer

N6-methyladenosine (m6A) is the most common form of messenger RNA (mRNA) modification. An increasing number of studies have proven that m6A RNA methylation regulators are overexpressed in many cancers and participate in the development of cancer through the dynamic regulation of m6A RNA methylation regulators. However, the prognostic role of m6A RNA methylation regulators in bladder cancer (BC) is poorly understood. In the present study, we downloaded the mRNA expression data from The Cancer Genome Atlas (TCGA) database and the corresponding clinical and prognostic information. The relationship between m6A RNA methylation regulators and clinicopathological variables of BC patients was assessed by the Kolmogorov–Smirnov test. The expression of the m6A RNA methylation regulators was differentially associated with different clinicopathological variables of BC patients. The least absolute shrinkage and selection operator (LASSO) Cox regression model was then applied to identify three m6A RNA methylation regulators. The risk signature was constructed as follows: 0.164FTO − (0.081YTHDC1+0.032WTAP). Based on the risk signature, the risk score of each patient was calculated, and the patients were divided into a high-risk group and a low-risk group. The overall survival (OS) rate of the high-risk group was significantly lower than that of the low-risk group. The risk signature was not only an independent prognostic marker for BC patients but also a predictor of clinicopathological variables. In conclusion, m6A RNA methylation regulators can participate in the malignant progression of BC, and a risk signature with three selected m6A RNA methylation regulators may be a promising prognostic biomarker to guide personalized treatment for BC patients.

AR-induced ZEB1-AS1 represents poor prognosis in cholangiocarcinoma and facilitates tumor stemness, proliferation and invasion through mediating miR-133b/HOXB8

Zinc finger E-box binding homeobox 1 antisense 1 (ZEB1-AS1) has displayed vital regulatory function in various tumors. However, the biological function of ZEB1-AS1 in cholangiocarcinoma (CCA) remains unclear. In this study, we confirmed that ZEB1-AS1 expression was increased in CCA tissues and cells, respectively. Upregulated ZEB1-AS1 was related to lymph node invasion, advanced TNM stage and poor survival of CCA patients. ZEB1-AS1 exhibited high sensitivity and specificity to be an independent poor prognostic factor of patients with CCA. Functionally, knocking down ZEB1-AS1 attenuated tumor cell stemness, restrained cellular viability in vitro and in vivo, and inhibited CCA cell migration and invasion by reversing epithelial-mesenchymal transition. For the mechanism, androgen receptor (AR) directly promoted ZEB1-AS1 expression, and further ZEB1-AS1 increased oncogene homeobox B8 (HOXB8) by sponging miR-133b. In addition, malignant phenotypes of CCA promoted by ZEB1-AS1 dysregulation were rescued separately through interfering miR-133b and HOXB8, suggesting AR/ZEB1-AS1/miR-133b/HOXB8 exerted crucial functions in tumorigenesis and progression of CCA.