Six-lncRNA Immune Prognostic Signature for Cervical Cancer

Pan-cancer analyses identify MIR210HG overexpression, epigenetic regulation and oncogenic role in human tumors and its interaction with the tumor microenvironment

BACKGROUND

Molecular entities showing dysregulation in multiple cancers may hold great biomarker or therapeutic potential. There is accumulating evidence that highlights the dysregulation of a long non-coding RNA, MIR210HG, in various cancers and its oncogenic role. However, a comprehensive analysis of MIR210HG expression pattern, molecular mechanisms, diagnostic or prognostic significance or evaluation of its interaction with tumor microenvironment across various cancers remains unstudied.

METHODS

A systematic pan-cancer analysis was done using multiple public databases and bioinformatic tools to study the molecular role and clinical significance of MIR210HG. We have analyzed expression patterns, genome alteration, transcriptional and epigenetic regulation, correlation with patient survival, immune infiltrates, co-expressed genes, interacting proteins, and pathways associated with MIR210HG.

RESULTS

The Pan cancer expression analysis of MIR210HG through various tumor datasets demonstrated that MIR210HG is significantly upregulated in most cancers and increased with the tumor stage in a subset of them. Furthermore, prognostic analysis revealed high MIR210HG expression is associated with poor overall and disease-free survival in specific cancer types. Genetic alteration analysis showed minimal alterations in the MIR210HG locus, indicating that overexpression in cancers is not due to gene amplification. The exploration of SNPs on MIR210HG suggested possible structural changes that may affect its interactions with the miRNAs. The correlation of MIR210HG with promoter methylation was found to be significantly negative in nature in majority of cancers depicting the possible epigenetic regulation of expression of MIR210HG. Additionally, MIR210HG showed negative correlations with immune cells and thus may have strong impact on the tumor microenvironment. Functional analysis indicates its association with hypoxia, angiogenesis, metastasis, and DNA damage repair processes. MIR210HG was found to interact with several proteins and potentially regulate chromatin modifications and transcriptional regulation.

CONCLUSIONS

A first pan-can cancer analysis of MIR210HG highlights its transcriptional and epigenetic deregulation and oncogenic role in the majority of cancers, its correlation with tumor microenvironment factors such as hypoxia and immune infiltration, and its potential as a prognostic biomarker and therapeutic target in several cancers.

GPR37-centered ceRNA network contributes to metastatic potential in lung adenocarcinoma: Evidence from high-throughput sequencing

The competing endogenous RNA (ceRNA)-based profiling has been extensively studied in carcinogenesis of lung adenocarcinoma (LUAD), while it has seldomly been applied to investigate the metastatic potential of LUAD. This study aims to examine the function and in-depth mechanism of GPR37-centered ceRNA network in LUAD. Cancer tissues and adjacent normal tissues from three LUAD patients were collected for high-throughput sequencing to screen for differentially expressed genes. A PPI network was constructed to screen the key gene GPR37, followed by analysis for the functions and pathways. Clinical data from LUAD patients were integrated with gene expression data in TCGA-LUAD dataset for survival analysis. Based on the miRNAs targeting_GPR37 and lncRNAs targeting_miRNAs, a lncRNA-miRNA-mRNA ceRNA network was established. GPR37 was up-regulated in LUAD tissue samples, and it may be a key gene involved in LUAD progression. GPR37 in LUAD was mainly enriched in the mitosis-related pathways. High GPR37 expression corresponded to poor prognosis in LUAD patients. Meanwhile, GPR37 could be used as an independent factor to predict the prognosis in LUAD patients. LncRNA DLEU1, up-regulated in LUAD tissue samples, may competitively bind to miR-4458 to up-regulate the expression of the miR-4458 downstream target GPR37. DLEU1 was associated with poor prognosis and tumor metastasis in LUAD patients. Altogether, our findings reveal a novel ceRNA network of DLEU1/miR-4458/GPR37 in LUAD growth and metastasis.

Identification of AP002498.1 and LINC01871 as prognostic biomarkers and therapeutic targets for distant metastasis of colorectal adenocarcinoma

BACKGROUND

Increasing evidence suggests that lncRNA (Long non-coding RNA, lncRNA)-mediated ceRNA (competing endogenous RNA, ceRNA) networks are involved in the occurrence and progression of colorectal cancer (CRC). However, the roles of the lncRNA-miRNA-mRNA ceRNA network in distant metastasis of CRC are still unclear.

METHODS

In this study, we constructed a specific ceRNA network to identify potential biomarkers and therapeutic targets for distant metastasis of CRC. Specifically, RNA-Seq data from The Cancer Genome Atlas (TCGA) were used to screen for differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) related to metastasis. After validation and selection by qRT-PCR and univariate and multivariate analysis of the metastasis- and prognosis-related lncRNAs, the regulated microRNAs (miRNAs) and coexpressed mRNAs were used to construct a ceRNA network for distant metastasis of CRC.

RESULTS

Two key distant metastasis-related DElncRNAs, AP002498.1 and LINC01871, were identified by univariate and multivariate analysis in combination with analyses of clinical data and expression levels. Furthermore, lncRNA-associated ceRNA subnetworks were constructed from the predicted miRNAs and 13 coexpressed DEmRNAs (SERPINA1, ITLN1, REG4, L1TD1, IGFALS, MUC5B, CIITA, CXCL9, CXCL10, GBP4, GNLY, IDO1, and NOS2). The AP002498.1- and LINC01871-associated ceRNA subnetworks regulated the expression of the target genes SERPINA1 and MUC5B and GNLY, respectively, through the associated miRNAs.

CONCLUSION

The DElncRNA AP002498.1 and the LINC01871/miR-4644 and miR-185-5p/GNLY axes were identified as being closely associated with distant metastasis and could represent independent prognostic biomarkers or therapeutic targets in colorectal adenocarcinoma.

A novel necroptosis-related long non-coding RNA signature predicts prognosis and immune response in cervical cancer patients

BACKGROUND

Necroptosis has been linked to the development of tumors. Long non-coding RNAs (IncRNAs) have been identified as having a major role in numerous biological and pathological procedures. Despite this, the precise role that necroptosis-related lncRNAs (NRLs) have in cervical cancer (CC) and their potential for predicting its prognosis is still to a large extent unclear.

METHODS

Gene expression RNA-sequencing data, mutational data, and clinical profiles for 309 CC patients were obtained from the Cancer Genome Atlas (TCGA) database. The NRLs were then identified with Pearson correlation analysis followed by splitting of the patients into training and validation sets in a 3:2 ratio. Cox and LASSO regression models were performed to construct a cervical cancer prognostic signature based on NRLs. This 5-NRLs signature was then verified by Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curve, and nomogram for prognostic prediction. Further, a correlation study between the risk score (RS) and immune cell infiltration, immune checkpoint molecules, tumor mutation burden (TMB), and the sensitivity of chemotherapy drug was conducted. To validate the 5-NRLs, a quantitative reverse transcription polymerase chain reaction (qRT-PCR) was finally performed.

RESULTS

The 5-NRLs signature was designed to accurately predict the prognosis of CC. It consists of AC092153.1, AC007686.3, LINC01281, AC009097.2, and RUSC1-AS1 and was found to be highly predictive using ROC and Kaplan-Meier curves. Furthermore, when analyzed through stratified survival analysis, it was confirmed to be an independent risk factor for prognosis. The nomogram and calibration curves further validated its clinical utility. Moreover, distinct differences between two risk groups were observed when examining immune cell infiltration, immune checkpoint molecules, somatic gene alterations and half-inhibitory concentration of anticancer drug.

CONCLUSIONS

The 5-NRLs signature is a novel and valuable tool for evaluating the prognosis of CC patients, providing clinicians with an informed decision-making framework to formulate tailored treatment plans for their patients.

Piperlongumine induces apoptosis and autophagy via the PI3K/Akt/mTOR pathway in KB human cervical cancer cells

Natural products are continuously being researched to develop safe and effective treatment options for cervical cancer, the fourth most common cancer in women. Piperlongumine (PL), an amide alkaloid mainly present in long pepper, exhibits neuroprotective and anti-cancer properties. However, the specific effect of PL in cervical cancer and the relationship between the anti-cancer pathway and autophagy remain unclear. Therefore, we aimed to investigate PL-induced apoptosis in KB human cervical cancer cells and the relationship between apoptosis and autophagy therein. 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide and wound-healing assays showed that PL treatment suppressed KB cell viability and proliferation. Apoptosis was identified through 4',6-diamidino-2-phenylindole and annexin V-propidium iodide staining, increased cleaved-poly (ADP-ribose) polymerase and Bcl-2 associated X levels, and decreased B cell lymphoma 2 levels. Acridine orange staining and increased microtubule-associated protein 1A/1B-light chain 3-II and Beclin-1 levels confirmed autophagy. We determined that KB cell-related autophagy exerted cytoprotective effects using the autophagy inhibitors 3-methyladenine and hydroxychloroquine. PL treatment promoted apoptosis by inhibiting the phosphatidylinositol-3-kinase (PI3K)/protein kinase B/mammalian target of rapamycin pathway in KB cells; inhibiting the pathway using PI3K inhibitors increased autophagy. We suggest that PL is a potential natural anticancer agent for cervical cancer treatment.

LncRNA LINC01871 sponging miR-142-3p to modulate ZYG11B promotes the chemoresistance of colorectal cancer cells by inducing autophagy

BACKGROUND

Colorectal cancer (CRC) is a malignant tumor in the digestive tract. Increasing evidence indicated that chemoresistance leads to a poor prognosis of CRC. Herein, we aimed to uncover the potential mechanism by which long intergenic noncoding RNA-1871 (LINC01871) affects the chemoresistance of CRC cells.

METHODS

Relative level of LINC01871 in CRC tissues was assessed by reverse transcription quantitative PCR (RT-qPCR). Kaplan-Meier analysis was conducted to determine the relevance of LINC01871 and the prognosis of CRC patients. Cell Counting Kit-8 (CCK-8) and colony formation assay were used to evaluate the proliferation of SW480 cells. Expression levels of proteins and their genes were assessed by western blot, immunofluorescence staining and RT-qPCR. In addition, the interaction of LINC01871, miR-142-3p and protein zyg-11 homolog B (ZYG11B) were analyzed via dual-luciferase reporter assays.

RESULTS

LINC01871 was low-expressed in CRC tissues and cell lines. Patients with a low level of LINC01871 showed significantly lower survival rate. pcDNA-LINC01871 significantly reduced the viability of SW480 cells ( P  < 0.01), elevated SW480 cells sensitivity to 5-FU ( P  < 0.01), reduced LC3 punctate aggregates ( P  < 0.01) and downregulated the relative mRNA expression level of autophagy related protein 9A, autophagy related protein 4B and high mobility group box 1 ( P  < 0.01) in SW480 cells. Moreover, LINC01871 was found to sponge miR-142-3p, and ZYG11B was the target of miR-142-3p. MiR-142-3p mimic significantly recovered the effect of pcDNA-LINC001871, whereas pcDNA-ZYG11B reversed the recovery effect of the miR-142-3p mimic.

CONCLUSION

LINC01871/miR-142-3p/ ZYG11B axis regulates the chemoresistance of CRCs by inducing autophagy.

AC010883.5 promotes cell proliferation, invasion, migration, and epithelial-to-mesenchymal transition in cervical cancer by modulating the MAPK signaling pathway

Homo sapiens chromosome 2 clone RP11-339H12 (AC010883.5) is a dysregulated long non-coding RNA (lncRNA) that has never been investigated in cervical cancer (CC). Thus, the potential function and molecular mechanism remain unclear. Our study explored the biological function of AC010883.5 to determine the underlying mechanisms in CC and provide potential therapeutic targets for improving the clinical treatment strategy. We used quantitative real-time polymerase chain reaction to measure mitochondrial RNA levels and western blot to measure the protein levels of target genes. Further, we used Cell Counting Kit-8 and 5-Bromo-2'-deoxyuridine incorporation assays to evaluate cell proliferation in vitro. Cell apoptosis was analyzed by flow cytometry. Cell invasion was analyzed by wound healing and Transwell migration assays was ued to analyze cell migration. Finally, the biological function and mechanism of AC010883.5 in CC growth were evaluated by in vivo xenograft assay. AC010883.5 was enhanced in CC tissues and cell lines, and enhanced AC010883.5 expression accelerated CC cell proliferation, migration, and invasion and induced epithelial-mesenchymal transition in vitro and in vivo. AC010883.5 also activated the mitogen-activated protein kinase (MAPK) signaling pathway by promoting phosphorylation of extracellular signal-regulated kinase 1/2 (i.e., ERK1/2) and MAPK kinase 1/2 (i.e., MEK1/2). Blocking the MAPK signaling pathway could counteract the pro-proliferative, pro-migrative, and pro-invasive effects of AC010883.5 over-expression. We found that the lncRNA, AC010883.5, is an oncogenic molecule involved in CC tumor progression via dysregulation of the MAPK signaling pathway, implying that AC010883.5 could be a tumor progression and therapeutic response biomarker.

Role of noncoding RNAs with emphasis on long noncoding RNAs as cervical cancer biomarkers

Cervical cancer is a significant public health problem in developing countries, as most cases present at an advanced stage. This review aimed to analyze the role of noncoding RNAs as diagnostic and prognostic biomarkers in cervical cancers. Published studies on specific microRNA signatures in body fluids and cervical cancer tissues are highly heterogeneous, and there are no validated assays. The precision of the various immune-associated long noncoding (lncRNA) signatures should be assessed in clinical samples. Even though lncRNAs are tissue and cancer-specific, safe and appropriate methods for delivery to tumor tissues, toxicities and side effects are to be explored. Few studies have evaluated deregulated lncRNA expression levels with clinicopathological factors in a limited number of clinical samples. Prospective studies assessing the diagnostic and prognostic roles of circulating lncRNAs and P-Element-induced wimpy testis interacting PIWI RNAs (Piwil RNAs) in cervical cancer cases are essential. For the clinical application of lnc-RNA-based biomarkers, comprehensive research is needed as the impact of noncoding transcripts on molecular pathways is complex. The standardization and validation of deregulated ncRNAs in noninvasive samples of cervical cancer cases are needed.

Exploration of Immunogenic Cell Death-Associated Genes for Predicting Prognosis and Immunological Characteristics in Cervical Squamous Cell Carcinoma

Background. The tumor microenvironment (TME) has gradually entered the vision of researchers and is becoming a vital part of the occurrence of cervical squamous cell carcinoma (CSCC). However, understanding the specific composition of TME still confront enormous challenges, particularly immune and stromal components. Methods. In this study, we performed an unsupervised cluster analysis to determine the immunogenic cell death-associated subtype of CSCC patients. The differences in immune status, genomic alteration, and clinical outcomes between each subtype were compared. Subsequently, we screened vital prognostic factors. The HPA database was employed to verify the protein localization and the expression level between cancer and adjacent tissues. Results. CSCC patients were divided into three subtypes according to the expression of immunogenic cell death-associated genes. Cluster C has the highest survival rate because of the lower activation of tumor-related pathways. The immune score and stromal score of patients with Cluster B were the highest, so it may be considered that stromal tissue inhibits the anti-tumor effect of immunocytes. In addition, we constructed a risk score based on immunogenic cell death-associated genes to screen for vital markers. We systematically revealed the genomic alteration of vital markers. Conclusions. We have established a novel immunogenic cell death-associated risk scoring system in CSCC, and the expression of immunogenic cell death-associated genes may be a valuable biomarker for immunotherapy strategies. Our work may contribute to the development of new immunomodulators and develop new precision immunotherapies for CSCC.

Identification of platinum resistance-related gene signature for prognosis and immune analysis in bladder cancer

Purpose: Currently, there is limited knowledge about platinum resistance-related long non-coding RNAs (lncRNAs) in bladder cancer. We aim to identify platinum resistance-related lncRNAs and construct a risk model for accurate prognostic prediction of bladder cancer. Methods: Transcriptomic and clinical data were extracted from The Cancer Genome Atlas (TCGA) database, and platinum resistance-related genes were obtained from HGSOC-Platinum. The platinum resistance-related lncRNAs were obtained by the Spearman correlation analysis. Then, we constructed a risk score model through Cox regression analysis and the LASSO algorithm. The model was verified by analyzing the median risk score, Kaplan-Meier curve, receiver operating characteristic (ROC) curve, and heatmap. We also developed a nomogram and examined the relationship between the risk score model, immune landscape, and drug sensitivity. Lastly, we assessed the differential expression of PRR-lncRNAs in the cisplatin-resistant bladder cancer cell line and the normal bladder cancer cell line using qRT-PCR. Results: We developed and validated an eight-platinum resistance-related lncRNA risk model for bladder cancer. The risk model showed independent prognostic significance in univariate and multivariate Cox analyses. Based on multivariate analysis, we developed a nomogram. The modified model is both good predictive and clinically relevant after evaluation. Furthermore, immune-related and drug-sensitivity analyses also showed significant differential expression between high and low-risk groups. The qRT-PCR demonstrated that most of the lncRNAs were upregulated in cisplatin-resistance cancerous tissues than in control tissues. Conclusion: We have developed a predictive model based on eight platinum resistance-related lncRNAs, which could add meaningful information to clinical decision-making.

Eight Aging-Related Genes Prognostic Signature for Cervical Cancer

This study searched for aging-related genes (ARGs) to predict the prognosis of patients with cervical cancer (CC). All data were obtained from Molecular Signatures Database, Cancer Genome Atlas, Gene Expression Integration, and Genotype Organization Expression. The R software was used to screen out the differentially expressed ARGs (DE-ARGs) between CC and normal tissues. A protein-protein interaction network was established by the DE-ARGs. The univariate and multivariate Cox regression analyses were conducted on the first extracted Molecular Complex Detection component, and a prognostic model was constructed. The prognostic model was further validated in the testing set and GSE44001 dataset. Prognosis was analyzed by Kaplan-Meier curves, and accuracy of the prognostic model was assessed by receiver operating characteristic area under the curve analysis. An independent prognostic analysis of risk score and some clinicopathological factors of CC was also performed. The copy-number variant (CNV) and single-nucleotide variant (SNV) of prognostic ARGs were analyzed by the BioPortal database. A clinical practical nomogram was established to predict individual survival probability. Finally, we carried out cell experiment to further verify the prognostic model. An eight-ARG prognostic signature for CC was constructed. High-risk CC patients had significantly shorter overall survival than low-risk patients. The receiver operating characteristic (ROC) curve validated the good performance of the signature in survival prediction. The Figo_stage and risk score served as independent prognostic factors. The eight ARGs mainly enriched in growth factor regulation and cell cycle pathway, and the deep deletion of FN1 was the most common CNV. An eight-ARG prognostic signature for CC was successfully constructed.

The crucial role of LncRNA MIR210HG involved in the regulation of human cancer and other disease

Long noncoding RNAs (lncRNAs) have evoked considerable interest in recent years due to their critical functions in the regulation of disease processes. Abnormal expression of lncRNAs is found in multiple diseases, and lncRNAs have been exploited for diverse medical applications. The lncRNA MIR210HG is a recently discovered lncRNA that is widely dysregulated in human disease. MIR210HG was described to have biological functions with potential roles in disease development, including cell proliferation, invasion, migration, and energy metabolism. And MIR210HG dysregulation was confirmed to have promising clinical values in disease diagnosis, treatment, and prognosis. In this review, we systematically summarize the expression profiles, roles, underlying mechanisms, and clinical applications of MIR210HG in human disease.

Identification and Validation a Necroptosis-Related Prognostic Signature in Cervical Cancer

Necroptosis is a promising novel target for cervical cancer therapy. Nevertheless, differentially expressed necroptosis-related genes (NRGs) in cervical cancer and their associations with prognosis are far from fully clarified. In this study, differentially expressed NRGs (DE-NRGs) were screened out and their bio-function was elucidated. Subsequently, a prognostic scoring model based on the regression coefficients of the screened out NRGs and their corresponding mRNA expressions were constructed and validated. Finally, the survival probability of cervical cancer patients based on the constructed prognostic scoring model in 3 and 5 years was predicted and assessed. We found 17 DE-NRGs in cervical cancer tissues which were closely related to cancer progression, and most of them were significantly highly expressed. Furthermore, 3 NRG were confirmed as the prognostic signature genes from 17 DE-NRGs by regression analysis. Overall survival predicted through our prognostic scoring model was lower in the high-risk group than in the low-risk group (p < 0.05) in both the TCGA cohort and the external GEO44001 validation cohort. What's more, the prediction performance of our prognostic scoring models well verified by the ROC curve, and the risk score calculated could act as an independent prognostic factor for cervical cancer patients. The calibration curve and C-index (0.776) of the nomogram analysis suggested that the predictive performance of the nomogram was satisfactory. Our study identified and validated a necroptosis-related prognostic signature in cervical cancer, which could well predict the prognosis for cervical cancer patients.

A risk prediction model mediated by genes of APOD/APOC1/SQLE associates with prognosis in cervical cancer

Cervical cancer is one of the most common gynecological malignancies. Due to the high heterogeneity of cervical cancer accelerating cancer progression, it is necessary to identify new prognostic markers and treatment regimens for cervical cancer to improve patients' survival rates. We purpose to construct and verify a risk prediction model for cervical cancer patients. Based on the analysis of data from the Gene Expression Omnibus database (GEO) and The Cancer Genome Atlas (TCGA), differences of genes in normal and cancer samples were analyzed and then used analysis of WGCNA along with consistent clustering to construct single-factor + multi-factor risk models. After regression analysis, the target genes were obtained as prognostic genes and prognostic risk models were constructed, and the validity of the risk model was confirmed using the receiver operating characteristic curve (ROC) and Kaplan-Meier curve. Subsequently, the above model was verified on the GSE44001 data validation followed by independent prognostic analysis. Enrichment analysis was conducted by grouping the high and low risks of the model. In addition, differences in immune analysis (immune infiltration, immunotherapy), drug sensitivity, and other levels were counted by the high and low risks groups. In our study, three prognostic genes including APOD, APOC1, and SQLE were obtained, and a risk model was constructed along with validation based on the above-mentioned analysis. According to the model, immune correlation and immunotherapy analyses were carried out, which will provide a theoretical basis and reference value for the exploration and treatment of cervical cancer.

An immune infiltration-related long non-coding RNAs signature predicts prognosis for hepatocellular carcinoma

Background: With a high incidence and dismal survival rate, hepatocellular carcinoma (HCC) tops the list of the world's most frequent malignant tumors. Immunotherapy is a new approach to cancer treatment, and its effect on prolonging overall survival (OS) varies from patient to patient. For a more effective prognosis and treatment of HCC, we are committed to identifying immune infiltration-related long non-coding RNAs (IIRLs) with prognostic value in hepatocellular carcinoma. Methods: In our study, we calculated immune scores of 369 hepatocellular carcinoma samples from the Cancer Genome Atlas (TCGA) database by using an estimation algorithm, and obtained long non-coding RNAs (lncRNAs) associated with immune infiltration by using Weighted Gene Co-expression Network analysis (WGCNA). For training cohort, univariate Cox, least absolute shrinkage and selection operator (Lasso) and multivariate Cox regression analysis were used to determine prognostic IIRLs, we established a prognostic IIRLs signature. By testing cohort and entire cohort, we confirmed that the signature is practical. The prognosis of people with different clinicopathological stages and risk scores were predicted by the nomogram we constructed. In addition, Immune cell infiltration analysis and prediction of therapeutic drugs were performed. Results: 93 IIRLs were obtained by WGCNA. Furthermore, the prognostic value of these IIRLs were evaluated by using univariate Cox, Lasso and multivariate Cox analysis. Four IIRLs were used to create a signature with a prognosis. Time-related receiver operating characteristic (ROC) curve revealed that this model had an acceptable prognostic value for HCC patients. By using univariate and multivariate Cox regression analysis, this risk score has been shown to be an independent prognostic factor for HCC. The nomogram we made showed good predictions. Except for that, the treatment with immune checkpoint inhibitors (ICI) was likely to be more effective for low-risk patients. Conclusion: Based on four IIRLs, a prognostic signature was created in this research showed good accuracy in predicting OS. This study also provided valuable references for Immunotherapy of hepatocellular carcinoma.

Dysregulated long non-coding RNA in Sjögren's disease impacts both interferon and adaptive immune responses

OBJECTIVE

Sjögren's disease (SjD) is an autoimmune disease characterised by inflammatory destruction of exocrine glands. Patients with autoantibodies to Ro/SSA (SjDRo+) exhibit more severe disease. Long non-coding RNAs (lncRNAs) are a functionally diverse class of non-protein-coding RNAs whose role in autoimmune disease pathology has not been well characterised.

METHODS

Whole blood RNA-sequencing (RNA-seq) was performed on SjD cases (n=23 Ro/SSA negative (SjDRo-); n=27 Ro/SSA positive (SjDRo+) and healthy controls (HCs; n=27). Bioinformatics and pathway analyses of differentially expressed (DE) transcripts (log2 fold change ≥2 or ≤0.5; padj<0.05) were used to predict lncRNA function. LINC01871 was characterised by RNA-seq analyses of HSB-2 cells with CRISPR-targeted LINC01871 deletion (LINC01871-/ -) and in vitro stimulation assays.

RESULTS

Whole blood RNA-seq revealed autoantibody-specific transcription profiles and disproportionate downregulation of DE transcripts in SjD cases relative to HCs. Sixteen DE lncRNAs exhibited correlated expression with the interferon (IFN)-regulated gene, RSAD2, in SjDRo+ (r≥0.65 or ≤-0.6); four antisense lncRNAs exhibited IFN-regulated expression in immune cell lines. LINC01871 was upregulated in all SjD cases. RNA-seq and pathway analyses of LINC01871-/ - cells implicated roles in cytotoxic function, differentiation and IFNγ induction. LINC01871 was induced by IFNγ in a myeloid cell line and regulated by calcineurin/NFAT pathway and T cell receptor (TCR) signalling in primary human T cells.

CONCLUSION

LINC01871 influences expression of many immune cell genes and growth factors, is IFNγ inducible, and regulated by calcineurin signalling and TCR ligand engagement. Altered LINC01871 expression may influence the dysregulated T cell inflammatory pathways implicated in SjD.

Identification and validation of an immune-related lncRNAs signature to predict the overall survival of ovarian cancer

Ovarian cancer (OC) is the most lethal gynecological cancer in women. Studies had reported that immune-related lncRNAs signatures were valuable in predicting the survival and prognosis of patients with various cancers. In our study, the prognostic value of immune-related lncRNAs was investigated in OC patients from TCGA-RNA-seq cohort (n=378) and HG-U133_Plus_2 cohort (n=590), respectively. Pearson correlation analysis was implemented to screen the immune-related lncRNA and then univariate Cox regression analysis was performed to explore their prognostic value in OC patients. Five prognostic immune-related lncRNAs were identified as prognostic lncRNAs. Besides, they were inputted into a LASSO Cox regression to establish and validate an immune-related lncRNA prognostic signature in TCGA-RNA-Seq cohort and HG-U133_Plus_2 cohort, respectively. Based on the best cut-off value of risk score, patients were divided into high- and low-risk groups. Survival analysis suggested that patients in the high-risk group had a worse overall survival (OS) than those in the low-risk group in both cohorts. The association between clinicopathological feathers and risk score was then evaluated by using stratification analysis. Moreover, we constructed a nomogram based on risk score, age and stage, which had a strong ability to forecast the OS of the OC patients. The influence of risk score on immune infiltration and immunotherapy response were assessed and the results suggested that patients with high-risk score might recruit multiple immune cells and stromal cells, leading to facilitating immune surveillance evasive. Ultimately, we demonstrated that the risk model was associated with chemotherapy response of multiple antitumor drugs, especially for paclitaxel, metformin and veliparib, which are commonly used in treating OC patients. In conclusion, we constructed a novel immune-related lncRNA signature, which had a potential prognostic value for OC patients and might facilitate personalized counselling for immunotherapy and chemotherapy.

Integrated analysis of immune- and apoptosis-related lncRNA-miRNA-mRNA regulatory network in children with Henoch Schönlein purpura nephritis

BACKGROUND

Long noncoding RNAs (lncRNAs) play important roles in the regulation of immunological and apoptotic function. This study aimed to explore the critical immune- and apoptosis-related lncRNAs in the occurrence and development of Henoch-Schönlein purpura nephritis (HSPN) in children.

METHODS

Differential analysis was employed to identify the differentially expressed lncRNAs, as well as the immune- and apoptosis-related mRNAs in children with HSPN. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to validate the immunological and apoptotic roles of the differentially expressed immune- and apoptosis-related lncRNAs and mRNAs. Spearman's correlation analysis was performed to analyze the differentially expressed lncRNAs and immune- and apoptosis-related messenger RNAs (mRNAs). Based on the competing endogenous RNA (ceRNA) mechanism, the immune- and apoptosis-related lncRNA-microRNA (miRNA)-mRNA regulatory network was then constructed in children with HSPN. The expression levels of the lncRNAs in the lncRNA-miRNA-mRNA regulatory network were further confirmed by quantitative real-time polymerase chain in the peripheral blood samples of children with HSPN.

RESULTS

By intersecting the differentially expressed immune-related and apoptosis-related genes through GO and KEGG analyses, a total of 43 genes were identified in children with HSPN, and 100 lncRNAs highly correlated with the above genes were identified by correlation analysis. The immune- and apoptosis-related lncRNA-miRNA-mRNA regulatory network was then established based on ceRNA mechanism. Dysregulation of a total of 11 lncRNAs were discovered, including upregulated SNHG3, LINC00152, TUG1, GAS5, FGD5-AS1, DLEU2, and SCARNA9; and downregulated SNHG1, NEAT1, DISC1-IT1, and PVT1. The validation conducted in the clinical samples also suggested that the above lncRNAs in the specific regulatory network may act as potential biomarkers with prognosis in children with HSPN.

CONCLUSIONS

LncRNAs may play essential regulatory roles in the occurrence and development of HSPN in children, and the immune- and apoptosis-related lncRNA-miRNA-mRNA regulatory network might be the underlying molecular mechanism that dissects the disease pathogenesis. In addition, the dysregulated lncRNAs in the regulatory network may be novel biomarkers for the diagnosis and therapy of HSPN in children.

Signature of seven cuproptosis-related lncRNAs as a novel biomarker to predict prognosis and therapeutic response in cervical cancer

Background: Given the high incidence and high mortality of cervical cancer (CC) among women in developing countries, identifying reliable biomarkers for the prediction of prognosis and therapeutic response is crucial. We constructed a prognostic signature of cuproptosis-related long non-coding RNAs (lncRNAs) as a reference for individualized clinical treatment.

Methods: A total of seven cuproptosis-related lncRNAs closely related to the prognosis of patients with CC were identified and used to construct a prognostic signature via least absolute shrinkage and selection operator regression analysis in the training set. The predictive performance of the signature was evaluated by Kaplan–Meier (K-M) analysis, receiver operating characteristic (ROC) analysis, and univariate and multivariate Cox analyses. Functional enrichment analysis and single-sample gene set enrichment analysis were conducted to explore the potential mechanisms of the prognostic signature, and a lncRNA–microRNA–mRNA network was created to investigate the underlying regulatory relationships between lncRNAs and cuproptosis in CC. The associations between the prognostic signature and response to immunotherapy and targeted therapy were also assessed. Finally, the prognostic value of the signature was validated using the CC tissues with clinical information in my own center.

Results: A prognostic signature was developed based on seven cuproptosis-related lncRNAs, including five protective factors (AL441992.1, LINC01305, AL354833.2, CNNM3-DT, and SCAT2) and two risk factors (AL354733.3 and AC009902.2). The ROC curves confirmed the superior predictive performance of the signature compared with conventional clinicopathological characteristics in CC. The ion transport-related molecular function and various immune-related biological processes differed significantly between the two risk groups according to functional enrichment analysis. Furthermore, we discovered that individuals in the high-risk group were more likely to respond to immunotherapy and targeted therapies including trametinib and cetuximab than those in the low-risk group. Finally, CC tissues with clinical data from my own center further verify the robustness of the seven-lncRNA risk signature.

Conclusion: We generated a cuproptosis-related lncRNA risk signature that could be used to predict prognosis of CC patients. Moreover, the signature could be used to predict response to immunotherapy and chemotherapy and thus could assist clinicians in making personalized treatment plans for CC patients.

Ferroptosis-related lncRNA signature predicts prognosis and immunotherapy efficacy in cutaneous melanoma

Purpose

Ferroptosis-related lncRNAs are promising biomarkers for predicting the prognosis of many cancers. However, a ferroptosis-related signature to predict the prognosis of cutaneous melanoma (CM) has not been identified. The purpose of this study was to construct a ferroptosis-related lncRNA signature to predict prognosis and immunotherapy efficacy in CM.

Methods

Ferroptosis-related differentially expressed genes (FDEGs) and lncRNAs (FDELs) were identified using TCGA, GTEx, and FerrDb datasets. We performed Cox and LASSO regressions to identify key FDELs, and constructed a risk score to stratify patients into high- and low-risk groups. The lncRNA signature was evaluated using the areas under the receiver operating characteristic curves (AUCs) and Kaplan-Meier analyses in the training, testing, and entire cohorts. Multivariate Cox regression analyses including the lncRNA signature and common clinicopathological characteristics were performed to identify independent predictors of overall survival (OS). A nomogram was developed for clinical use. We performed gene set enrichment analyses (GSEA) to identify significantly enriched pathways. Differences in the tumor microenvironment (TME) between the 2 groups were assessed using 7 algorithms. To predict the efficacy of immune checkpoint inhibitors (ICI), we analyzed the association between PD1 and CTLA4 expression and the risk score. Finally, differences in Tumor Mutational Burden (TMB) and molecular drugs Sensitivity between the 2 groups were performed.

Results

We identified 5 lncRNAs (AATBC, AC145423.2, LINC01871, AC125807.2, and AC245041.1) to construct the risk score. The AUC of the lncRNA signature was 0.743 in the training cohort and was validated in the testing and entire cohorts. Kaplan-Meier analyses revealed that the high-risk group had poorer prognosis. Multivariate Cox regression showed that the lncRNA signature was an independent predictor of OS with higher accuracy than traditional clinicopathological features. The 1-, 3-, and 5-year survival probabilities for CM patients were 92.7%, 57.2%, and 40.2% with an AUC of 0.804, indicating a good accuracy and reliability of the nomogram. GSEA showed that the high-risk group had lower ferroptosis and immune response. TME analyses confirmed that the high-risk group had lower immune cell infiltration (e.g., CD8+ T cells, CD4+ memory-activated T cells, and M1 macrophages) and lower immune functions (e.g., immune checkpoint activation). Low-risk patients whose disease expressed PD1 or CTLA4 were likely to respond better to ICIs. The analysis demonstrated that the TMB had significantly difference between low- and high- risk groups. Chemotherapy drugs, such as sorafenib, Imatinib, ABT.888 (Veliparib), Docetaxel, and Paclitaxel showed Significant differences in the estimated IC50 between the two risk groups.

Conclusion

Our novel ferroptosis-related lncRNA signature was able to accurately predict the prognosis and ICI outcomes of CM patients. These ferroptosis-related lncRNAs might be potential biomarkers and therapeutic targets for CM.

Construction of a Six-Gene Prognostic Risk Model Related to Hypoxia and Angiogenesis for Cervical Cancer

Background: The prognosis of cervical cancer (CC) is poor and not accurately reflected by the primary tumor node metastasis staging system. Our study aimed to develop a novel survival-prediction model.

Methods: Hallmarks of CC were quantified using single-sample gene set enrichment analysis and univariate Cox proportional hazards analysis. We linked gene expression, hypoxia, and angiogenesis using weighted gene co-expression network analysis (WGCNA). Univariate and multivariate Cox regression was combined with the random forest algorithm to construct a prognostic model. We further evaluated the survival predictive power of the gene signature using Kaplan-Meier analysis and receiver operating characteristic (ROC) curves.

Results: Hypoxia and angiogenesis were the leading risk factors contributing to poor overall survival (OS) of patients with CC. We identified 109 candidate genes using WGCNA and univariate Cox regression. Our established prognostic model contained six genes (MOCSI, PPP1R14A, ESM1, DES, ITGA5, and SERPINF1). Kaplan-Meier analysis indicated that high-risk patients had worse OS (hazard ratio = 4.63, p < 0.001). Our model had high predictive power according to the ROC curve. The C-index indicated that the risk score was a better predictor of survival than other clinicopathological variables. Additionally, univariate and multivariate Cox regressions indicated that the risk score was the only independent risk factor for poor OS. The risk score was also an independent predictor in the validation set (GSE52903). Bivariate survival prediction suggested that patients exhibited poor prognosis if they had high z-scores for hypoxia or angiogenesis and high risk scores.

Conclusions: We established a six-gene survival prediction model associated with hypoxia and angiogenesis. This novel model accurately predicts survival and also provides potential therapeutic targets.

Identification of Immune Infiltration-Related ceRNAs as Novel Biomarkers for Prognosis of Patients With Primary Open-Angle Glaucoma

Glaucoma is the leading cause of irreversible blindness globally; hence, relevant clinical biomarkers are necessary to enable diagnosis, early detection, and development of novel therapies. The differentially expressed genes were annotated and visualized using Gene Ontology and Kyoto Encyclopedia. In addition, a competitive endogenous ribonucleic acids network was constructed using Cytoscape, which explained the regulation of gene expression in glaucoma. The CIBERSORT algorithm was employed to analyze the immune microenvironment. We validated that the core genes could predict glaucoma occurrence and development and identified potential molecular mechanism pathways, which were associated with immune infiltration and participated in endogenous regulation networks. Our data may partially explain the pathogenesis of glaucoma and they provide potential theoretical support for targeted therapy.

Multi-Omics Approaches Identify Necroptosis‑Related Prognostic Signature and Associated Regulatory Axis in Cervical Cancer

Background

Cervical cancer is the fourth most frequent malignancy among women globally, with approximately 604,000 new cases and 341,000 deaths per year. Necroptosis is a newly discovered mechanism of cell death involved in biological behaviors of cancer.

Methods

LASSO Cox regression analysis was conducted to construct a prognostic necroptosis-related signature. lncRNA–miRNA–mRNA regulatory axis was constructed with a ceRNA network. qRT-PCR was performed to verify our result.

Results

A total of 54 necroptosis-related genes were differentially expressed in cervical cancer (all p < 0.05). We also summarized genetic mutation landscape of necroptosis-related genes in cervical cancer. We then developed a necroptosis-related prognostic signature including 13 necroptosis-related genes (ATRX, AXL, DDX58, IDH1, ITPK1, MAP3K7, SLC39A7, TARDBP, TNF, TNFRSF1A, TNFRSF1B, TNFSF10, TRIM11) for cervical cancer. Cervical cancer patients with high riskscore had a poor overall survival (HR = 2.128, p = 0.00194) with an AUC of 0.725, 0.763 and 0.637 in 3-year, 5-year, and 10-year ROC curve. Consensus clustering analysis revealed that all cervical cancer cohort could be divided into three subtypes, which was correlated with different prognosis and immune infiltration (p < 0.05). A PPI network revealed TNF as the hub gene and TNF expression was correlated with immune infiltration (all p < 0.05), microsatellite instability (p < 0.012) and drug sensitivity (p < 0.05). The ceRNA network was performed and identified a lncRNA NUTM2B-AS1/miR-361-5p/TNF regulatory axis for cervical cancer. qRT-PCR result also suggested that TNF was upregulated in cervical cancer (p < 0.001) and associated with a poor overall survival (p = 0.007). Univariate and multivariate analysis demonstrated TNF expression, lymph node metastasis and clinical stage were prognosis factors of cervical cancer patients (p < 0.05).

Conclusion

We developed a necroptosis-related prognostic signature including 13 necroptosis-related genes for cervical cancer. Moreover, we also identified a lncRNA NUTM2B-AS1/miR-361-5p/TNF regulatory axis, which may play a vital role in the progression of cervical cancer. Further studies should be conducted to verify these results.

Signature involved in immune-related lncRNA pairs for predicting the immune landscape of cervical cancer

Background

Immune-related long non-coding RNAs (irlncRNAs) are known to hold great promise as superior biomarkers for cervical cancer-related immunotherapeutic response and the tumor immune microenvironment. Here, we constructed a prognostic signature based on irlncRNA pairs (IRLPs).

Methods

The samples were downloaded from The Cancer Genome Atlas and the Genotype-Tissue Expression databases. The least absolute shrinkage and selection operator Cox regression was performed to construct the prognostic model. Receiver operating characteristic (ROC) curve and nomogram were plotted to validate accuracy of the model. Next, we estimated the immune cell infiltration and the correlation between risk score and the expression of genes related to immune checkpoint. Finally, we calculated the score of the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm and the half maximal inhibitory concentration of the chemotherapeutic agent to evaluate the response to immunotherapy and chemotherapy.

Results

We constructed a prognostic signature that consisted of 11 irlncRNAs. The area under the curve values of the 1-, 3-, and 5-year ROC curves were 0.844, 0.891, and 0.871, respectively. The expression of CTLA-4, HAVCR2, IDO1, LAG3, and PDCD1 were negatively correlated with risk scores. The score of TIDE in the high-risk group was significantly higher than in the low-risk group ( P < 0.01). Patients in the low-risk subgroup were more sensitive to chemotherapeutic agents, such as axitinib and docetaxel, whereas patients in the low-risk subgroup were more sensitive to mitomycin C.

Conclusion

Our study highlighted the value of the 11 IRLPs signatures to predict the prognosis and the response to immunotherapy and chemotherapeutics for patients with cervical cancer.

A Novel Ferroptosis-Related lncRNA Prognostic Model and Immune Infiltration Features in Skin Cutaneous Melanoma

Background: Skin cutaneous melanoma (SKCM) is an aggressive malignant skin tumor. Ferroptosis is an iron-dependent cell death that may mobilize tumor-infiltrating immunity against cancer. The potential mechanism of long non-coding RNAs (lncRNAs) in ferroptosis in SKCM is not clear. In this study, the prognostic and treatment value of ferroptosis-related lncRNAs was explored in SKCM, and a prognostic model was established.

Methods: We first explored the mutation state of ferroptosis-related genes in SKCM samples from The Cancer Genome Atlas database. Then, we utilized consensus clustering analysis to divide the samples into three clusters based on gene expression and evaluated their immune infiltration using gene-set enrichment analysis (GSEA) ESTIMATE and single-sample gene-set enrichment analysis (ssGSEA) algorithms. In addition, we applied univariate Cox analysis to screen prognostic lncRNAs and then validated their prognostic value by Kaplan–Meier (K-M) and transcripts per kilobase million (TPM) value analyses. Finally, we constructed an 18-ferroptosis-related lncRNA prognostic model by multivariate Cox analysis, and SKCM patients were allocated into different risk groups based on the median risk score. The prognostic value of the model was evaluated by K-M and time-dependent receiver operating characteristic (ROC) analyses. Additionally, the immunophenoscore (IPS) in different risk groups was detected.

Results: The top three mutated ferroptosis genes were TP53, ACSL5, and TF. The SKCM patients in the cluster C had the highest ferroptosis-related gene expression with the richest immune infiltration. Based on the 18 prognosis-related lncRNAs, we constructed a prognostic model of SKCM patients. Patients at low risk had a better prognosis and higher IPS.

Conclusion: Our findings revealed that ferroptosis-related lncRNAs were expected to become potential biomarkers and indicators of prognosis and immunotherapy treatment targets of SKCM.

A Novel Gene Prognostic Signature Based on Differential DNA Methylation in Breast Cancer

Background: DNA methylation played essential roles in regulating gene expression. The impact of DNA methylation status on the occurrence and development of cancers has been well demonstrated. However, little is known about its prognostic role in breast cancer (BC).

Materials: The Illumina Human Methylation450 array (450k array) data of BC was downloaded from the UCSC xena database. Transcriptomic data of BC was downloaded from the Cancer Genome Atlas (TCGA) database. Firstly, we used univariate and multivariate Cox regression analysis to screen out independent prognostic CpGs, and then we identified methylation-associated prognosis subgroups by consensus clustering. Next, a methylation prognostic model was developed using multivariate Cox analysis and was validated with the Illumina Human Methylation27 array (27k array) dataset of BC. We then screened out differentially expressed genes (DEGs) between methylation high-risk and low-risk groups and constructed a methylation-based gene prognostic signature. Further, we validated the gene signature with three subgroups of the TCGA-BRCA dataset and an external dataset GSE146558 from the Gene Expression Omnibus (GEO) database.

Results: We established a methylation prognostic signature and a methylation-based gene prognostic signature, and there was a close positive correlation between them. The gene prognostic signature involved six genes: IRF2, KCNJ11, ZDHHC9, LRP11, PCMT1, and TMEM70. We verified their expression in mRNA and protein levels in BC. Both methylation and methylation-based gene prognostic signatures showed good prognostic stratification ability. The AUC values of 3-years, 5-years overall survival (OS) were 0.737, 0.744 in the methylation signature and 0.725, 0.715 in the gene signature, respectively. In the validation groups, high-risk patients were confirmed to have poorer OS. The AUC values of 3 years were 0.757, 0.735, 0.733 in the three subgroups of TCGA dataset and 0.635 in GSE146558 dataset.

Conclusion: This study revealed the DNA methylation landscape and established promising methylation and methylation-based gene prognostic signatures that could serve as potential prognostic biomarkers and therapeutic targets.

Expression profile of RNA binding protein in cervical cancer using bioinformatics approach

BACKGROUND

It has been demonstrated by studies globally that RNA binding proteins (RBPs) took part in the development of cervical cancer (CC). Few studies concentrated on the correlation between RBPs and overall survival of CC patients. We retrieved significant DEGs (differently expressed genes, RNA binding proteins) correlated to the process of cervical cancer development.

METHODS

Expressions level of genes in cervical cancer and normal tissue samples were obtained from GTEx and TCGA database. Differently expressed RNA binding proteins (DEGs) were retrieved by Wilcoxon sum-rank test. ClusterProfiler package worked in R software was used to perform GO and KEGG enrichment analyses. Univariate proportional hazard cox regression and multivariate proportional hazard cox regressions were applied to identify DEGs equipped with prognostic value and other clinical independent risk factors. ROC curve was drawn for comparing the survival predict feasibility of risk score with other risk factors in CC patients. Nomogram was drawn to exhibit the prediction model and validated by C-index and calibration curve. Correlations between differentially expressed RNA binding proteins (DEGs) and other clinical features were investigated by t test or Cruskal Wallis analysis. Correlation between Immune and DEGs in cervical cancer was investigated by ssGSEA.

RESULTS

347 differentially expressed RBPs (DEGs) were retrieved from cervical cancer tissue and normal tissue samples. GO enrichment analysis showed that these DEGs involved in RNA splicing, catabolic process and metabolism. Cox regression model showed that there were ten DEGs significantly associated with overall survival of cervical cancer patients. WDR43 (HR = 0.423, P = 0.008), RBM38 (HR = 0.533, P < 0.001), RNASEH2A (HR = 0.474, P = 0.002) and HENMT1 (HR = 0.720, P = 0.071) played protective roles in survival among these ten genes. Stage (Stage IV vs Stage I HR = 3.434, P < 0.001) and risk score (HR = 1.214, P < 0.001) were sorted as independent prognostic risk factors based on multivariate cox regression. ROC curve validated that risk score was preferable to predict survival of CC patients than other risk factors. Additionally, we found some of these ten predictor DEGs were correlated significantly in statistic with tumor grade or stage, clinical T stage, clinical N stage, pathology or risk score (all P < 0.05). Part of immune cells and immune functions showed a lower activity in high risk group than low risk group which is stratified by median risk score.

CONCLUSION

Our discovery showed that many RNA binding proteins involved in the progress of cervical cancer, which could probably serve as prognostic biomarkers and accelerate the discovery of treatment targets for CC patients.

Molecular Markers to Predict Prognosis and Treatment Response in Uterine Cervical Cancer

Uterine cervical cancer is one of the leading causes of cancer-related mortality in women worldwide. Each year, over half a million new cases are estimated, resulting in more than 300,000 deaths. While less-invasive, fertility-preserving surgical procedures can be offered to women in early stages, treatment for locally advanced disease may include radical hysterectomy, primary chemoradiotherapy (CRT) or a combination of these modalities. Concurrent platinum-based chemoradiotherapy regimens remain the first-line treatments for locally advanced cervical cancer. Despite achievements such as the introduction of angiogenesis inhibitors, and more recently immunotherapies, the overall survival of women with persistent, recurrent or metastatic disease has not been extended significantly in the last decades. Furthermore, a broad spectrum of molecular markers to predict therapy response and survival and to identify patients with high- and low-risk constellations is missing. Implementation of these markers, however, may help to further improve treatment and to develop new targeted therapies. This review aims to provide comprehensive insights into the complex mechanisms of cervical cancer pathogenesis within the context of molecular markers for predicting treatment response and prognosis.

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.

Inhibition of the long non-coding RNA UNC5B-AS1/miR-4455/RSPO4 axis reduces cervical cancer growth in vitro and in vivo

BACKGROUND

Long non-coding RNAs (lncRNAs) are significant regulatory factors for the initiation and development of numerous malignant tumors, including cervical cancer (CC). The expression of lncRNA unc-5 netrin receptor B antisense RNA 1 (UNC5B-AS1, also known as UASR1) is up-regulated in tissues of cervical squamous cell carcinoma and endocervical adenocarcinoma compared to in normal tissues based on the GEPIA database. In the present study, we explored the functions of UNC5B-AS1 and its underlying mechanism with respect to CC development.

METHODS

A real-time quantitative polymerase chain reaction was applied for the detection of UNC5B-AS1 expression in CC cells. Cell counting kit-8, colony formation and transwell assays, as well as western blot and flow cytometry analyses, were employed to detect the biological effects of UNC5B-AS1 knockdown on malignant phenotypes of CC cells in vitro. In addition, the combination between microRNA-4455 (miR-4455) and UNC5B-AS1 or R-spondin 4 (RSPO4) was explored by RNA immunoprecipitation, luciferase reporter and RNA pulldown assays. A tumor xenograft nude mice model was established to explore the effect of UNC5B-AS1 depletion or miR-4455 overexpression on tumor growth.

RESULTS

UNC5B-AS1 is up-regulated in CC tissues and cells. The knockdown of UNC5B-AS1 inhibits CC cell proliferation, migration and invasion and promotes CC cell apoptosis. Mechanistically, UNC5B-AS1 binds with miR-4455 to up-regulate RSPO4 expression. RSPO4 is targeted by miR-4455 and its expression is negatively regulated by miR-4455 expression. In vivo assays revealed that UNC5B-AS1 depletion or miR-4455 overexpression inhibits tumor growth by regulating RSPO4 expression.

CONCLUSIONS

Inhibition of UNC5B-AS1/miR-4455/RSPO4 reduces CC growth both in vitro and in vivo, furnishing new insights into molecular studies on UNC5B-AS1 with respect to CC development.

The promotion of cervical cancer progression by signal transducer and activator of transcription 1-induced up-regulation of lncRNA MEOX2-AS1 as a competing endogenous RNA through miR-143-3p/VDAC1 pathway

Long non-coding RNAs (lncRNAs) are the new regulators and biomarkers for various tumors. However, in cervical cancer (CC), the potential roles of lncRNAs are not well characterized. This research aimed at exploring the roles of MEOX2 antisense RNA 1(MEOX2-AS1) in CC progression and the underlying mechanisms. The examination of MEOX2-AS1 levels in CC specimens and cell lines was conducted by RT-PCR. Loss-of-function experiments were performed for the assays of proliferation, migration, and invasion of CC cells after various treatments. Animal experiments were applied for the determination of the effects of MEOX2-AS1 in vivo. Bioinformatics analysis, together with dual-luciferase reporter assays, was applied to demonstrate the possible relationships among MEOX2-AS1, miR-143-3p and VDAC1. In the paper, we reported that MEOX2-AS1 levels were distinctly upregulated in CC cells and tissues, and higher MEOX2-AS1 expressions indicated a poor clinical outcome. Besides, STAT1 could activate transcriptions of MEOX2-AS1 by binding directly to its promoter region. The silence of MEOX2-AS1 suppressed the metastatic and proliferative ability of CC cells, as revealed by functional assays. Mechanistically, MEOX2-AS1 sponged miR-143-3p to regulate VDAC1 expressions. Furthermore, miR-143-3p inhibitor reversed the anti-proliferation and anti-metastasis effect of MEOX2-AS1 knockdown. Overall, the data indicated that the MEOX2-AS1/miR-143-3p/VDAC1 pathway participated in CC progression, making it a novel therapeutic target for CC cures.

Unraveling Immune-Related lncRNAs in Breast Cancer Molecular Subtypes

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

Identification of an immune-based mRNA-lncRNA signature for overall survival in cervical squamous cell carcinoma

Aim: To better predict the survival of cervical squamous cell carcinoma (CESC) patients, we aimed to construct a signature according to different immune infiltration. Methods: We downloaded the RNA sequences of CESC patients from the Cancer Genome Atlas database. By using single-sample gene set enrichment analysis, we separated the samples into high- and low-immunity groups. Then we separated the samples into training and testing datasets and performed the following analyses: univariate, least absolute shrinkage and selection operator analysis, multivariate Cox regression analyses and weighted gene coexpression network analysis using R software. Gene ontology and Kyoto Encyclopedia of Genes and Genomes studies were performed using the Database for Annotation, Visualization and Integrated Discovery website. Results & conclusion: We finally identified a signature with three mRNAs and two lncRNAs: ADGRG5, HSH2D, ZMAT4, RBAKDN and LINC00200. In short, our study constructed an mRNA-lncRNA signature related to immune infiltration to better predict the survival of CESC patients.

Construction of a Prognostic Gene Signature Associated with Immune Infiltration in Glioma: A Comprehensive Analysis Based on the CGGA

Background

Tumor microenvironment (TME) is closely related to the progression of glioma and the therapeutic effect of drugs on this cancer. The aim of this study was to develop a signature associated with the tumor immune microenvironment using machine learning.

Methods

We downloaded the transcriptomic and clinical data of glioma patients from the Chinese Glioma Genome Atlas (CGGA) databases (mRNAseq_693). The single-sample Gene Set Enrichment Analysis (ssGSEA) database was used to quantify the relative abundance of immune cells. We divided patients into two different infiltration groups via unsupervised clustering analysis of immune cells and then selected differentially expressed genes (DEGs) between the two groups. Survival-related genes were determined using Cox regression analysis. We next randomly divided patients into a training set and a testing set at a ratio of 7 : 3. By integrating the DEGs into least absolute shrinkage and selection operator (LASSO) regression analysis in the training set, we were able to construct a 15-gene signature, which was validated in the testing and total sets. We further validated the signature in the mRNAseq_325 dataset of CGGA.

Results

We identified 74 DEGs associated with tumor immune infiltration, 70 of which were significantly associated with overall survival (OS). An immune-related gene signature was established, consisting of 15 key genes: adenosine triphosphate (ATP)-binding cassette subfamily C member 3 (ABCC3), collagen type IV alpha 1 chain (COL4A1), podoplanin (PDPN), annexin A1 (ANXA1), COL4A2, insulin-like growth factor binding protein 2 (IGFBP2), serpin family A member 3 (SERPINA3), CXXC-type zinc finger protein 11 (CXXC11), junctophilin 3 (JPH3), secretogranin III (SCG3), secreted protein acidic and rich in cysteine (SPARC)-related modular calcium-binding protein 1 (SMOC1), Cluster of Differentiation 14 (CD14), COL1A1, S100 calcium-binding protein A4 (S100A4), and transforming growth factor beta 1 (TGF-β1). The OS of patients in the high-risk group was worse than that of patients in the low-risk group. GSEA showed that interleukin-6 (IL-6)/Janus kinase (JAK)/signal transducer and activator of transcription (STAT3) signaling, interferon gamma (IFN-γ) response, angiogenesis, and coagulation were more highly enriched in the high-risk group and that oxidative phosphorylation was more highly enriched in the low-risk group.

Conclusion

We constructed a stable gene signature associated with immune infiltration to predict the survival rates of glioma patients.

Immunogenomic Identification for Predicting the Prognosis of Cervical Cancer Patients

Cervical cancer is primarily caused by the infection of high-risk human papillomavirus (hrHPV). Moreover, tumor immune microenvironment plays a significant role in the tumorigenesis of cervical cancer. Therefore, it is necessary to comprehensively identify predictive biomarkers from immunogenomics associated with cervical cancer prognosis. The Cancer Genome Atlas (TCGA) public database has stored abundant sequencing or microarray data, and clinical data, offering a feasible and reliable approach for this study. In the present study, gene profile and clinical data were downloaded from TCGA, and the Immunology Database and Analysis Portal (ImmPort) database. Wilcoxon-test was used to compare the difference in gene expression. Univariate analysis was adopted to identify immune-related genes (IRGs) and transcription factors (TFs) correlated with survival. A prognostic prediction model was established by multivariate cox analysis. The regulatory network was constructed and visualized by correlation analysis and Cytoscape, respectively. Gene functional enrichment analysis was performed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). A total of 204 differentially expressed IRGs were identified, and 22 of them were significantly associated with the survival of cervical cancer. These 22 IRGs were actively involved in the JAK-STAT pathway. A prognostic model based on 10 IRGs (APOD, TFRC, GRN, CSK, HDAC1, NFATC4, BMP6, IL17RD, IL3RA, and LEPR) performed moderately and steadily in squamous cell carcinoma (SCC) patients with FIGO stage I, regardless of the age and grade. Taken together, a risk score model consisting of 10 novel genes capable of predicting survival in SCC patients was identified. Moreover, the regulatory network of IRGs associated with survival (SIRGs) and their TFs provided potential molecular targets.