Four Prognosis-Associated lncRNAs Serve as Biomarkers in Ovarian Cancer

Long Noncoding RNA VLDLR-AS1 Levels in Serum Correlate with Combat-Related Chronic Mild Traumatic Brain Injury and Depression Symptoms in US Veterans

More than 75% of traumatic brain injuries (TBIs) are mild (mTBI) and military service members often experience repeated combat-related mTBI. The chronic comorbidities concomitant with repetitive mTBI (rmTBI) include depression, post-traumatic stress disorder or neurological dysfunction. This study sought to determine a long noncoding RNA (lncRNA) expression signature in serum samples that correlated with rmTBI years after the incidences. Serum samples were obtained from Long-Term Impact of Military-Relevant Brain-Injury Consortium Chronic Effects of Neurotrauma Consortium (LIMBIC CENC) repository, from participants unexposed to TBI or who had rmTBI. Four lncRNAs were identified as consistently present in all samples, as detected via droplet digital PCR and packaged in exosomes enriched for CNS origin. The results, using qPCR, demonstrated that the lncRNA VLDLR-AS1 levels were significantly lower among individuals with rmTBI compared to those with no lifetime TBI. ROC analysis determined an AUC of 0.74 (95% CI: 0.6124 to 0.8741; p = 0.0012). The optimal cutoff for VLDLR-AS1 was ≤153.8 ng. A secondary analysis of clinical data from LIMBIC CENC was conducted to evaluate the psychological symptom burden, and the results show that lncRNAs VLDLR-AS1 and MALAT1 are correlated with symptoms of depression. In conclusion, lncRNA VLDLR-AS1 may serve as a blood biomarker for identifying chronic rmTBI and depression in patients.

Identification of lncRNAs Deregulated in Epithelial Ovarian Cancer Based on a Gene Expression Profiling Meta-Analysis

Epithelial ovarian cancer (EOC) is one of the deadliest gynecological cancers worldwide, mainly because of its initially asymptomatic nature and consequently late diagnosis. Long non-coding RNAs (lncRNA) are non-coding transcripts of more than 200 nucleotides, whose deregulation is involved in pathologies such as EOC, and are therefore envisaged as future biomarkers. We present a meta-analysis of available gene expression profiling (microarray and RNA sequencing) studies from EOC patients to identify lncRNA genes with diagnostic and prognostic value. In this meta-analysis, we include 46 independent cohorts, along with available expression profiling data from EOC cell lines. Differential expression analyses were conducted to identify those lncRNAs that are deregulated in (i) EOC versus healthy ovary tissue, (ii) unfavorable versus more favorable prognosis, (iii) metastatic versus primary tumors, (iv) chemoresistant versus chemosensitive EOC, and (v) correlation to specific histological subtypes of EOC. From the results of this meta-analysis, we established a panel of lncRNAs that are highly correlated with EOC. The panel includes several lncRNAs that are already known and even functionally characterized in EOC, but also lncRNAs that have not been previously correlated with this cancer, and which are discussed in relation to their putative role in EOC and their potential use as clinically relevant tools.

Construction of Ovarian Cancer Prognostic Model Based on the Investigation of Ferroptosis-Related lncRNA

(1) Background: Ovarian cancer (OV) has the high mortality rate among gynecological cancers worldwide. Inefficient early diagnosis and prognostic prediction of OV leads to poor survival in most patients. OV is associated with ferroptosis, an iron-dependent form of cell death. Ferroptosis, believed to be regulated by long non-coding RNAs (lncRNAs), may have potential applications in anti-cancer treatments. In this study, we aimed to identify ferroptosis-related lncRNA signatures and develop a novel model for predicting OV prognosis. (2) Methods: We downloaded data from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression, and Gene Expression Omnibus (GEO) databases. Prognostic lncRNAs were screened by least absolute shrinkage and selection operator (LASSO)-Cox regression analysis, and a prognostic model was constructed. The model's predictive ability was evaluated by Kaplan-Meier (KM) survival analysis and receiver operating characteristic (ROC) curves. The expression levels of these lncRNAs included in the model were examined in normal and OV cell lines using quantitative reverse transcriptase polymerase chain reaction. (3) Results: We constructed an 18 lncRNA prognostic prediction model for OV based on ferroptosis-related lncRNAs from TCGA patient samples. This model was validated using TCGA and GEO patient samples. KM analysis showed that the prognostic model was able to significantly distinguish between high- and low-risk groups, corresponding to worse and better prognoses. Based on the ROC curves, our model shows stronger prediction precision compared with other traditional clinical factors. Immune cell infiltration, immune checkpoint expression levels, and Tumor Immune Dysfunction and Exclusion analyses are also insightful for OV immunotherapy. (4) Conclusions: The prognostic model constructed in this study has potential for improving our understanding of ferroptosis-related lncRNAs and providing a new tool for prognosis and immune response prediction in patients with OV.

Long noncoding RNAs: glycolysis regulators in gynaecologic cancers

The three most common gynaecologic cancers that seriously threaten female lives and health are ovarian cancer, cervical cancer, and endometrial cancer. Glycolysis plays a vital role in gynaecologic cancers. Several long noncoding RNAs (lncRNAs) are known to function as oncogenic molecules. LncRNAs impact downstream target genes by acting as ceRNAs, guides, scaffolds, decoys, or signalling molecules. However, the role of glycolysis-related lncRNAs in regulating gynaecologic cancers remains poorly understood. In this review, we emphasize the functional roles of many lncRNAs that have been found to promote glycolysis in gynaecologic cancers and discuss reasonable strategies for future research.

lncRNA DARS-AS1 Modulates TSPAN1-Mediated ITGA2 Hypomethylation by Interaction with miR-194-5p Thus Promoting Ovarian Cancer Progression

Objective

Ovarian cancer (OC) is usually called the "silent killer" due to its asymptomatic characteristics until advanced stages, thus being a significant threat to female health worldwide. In this work, we characterized an oncogenic DARS-AS1 role in OC.

Methods

The aggressiveness behaviors of the OC cell model were examined by CCK-8 assay, transwell invasion assay, flow cytometry, and immunoblotting analysis of apoptosis-related proteins. Interactions of miR-194-5p with lncRNA DARS-AS1 or TSPAN1 and of TSPAN1 with ITGA2 were validated by using a luciferase activity assay and chromatin immunoprecipitation (ChIP) assay.

Results

The OC cell model exhibited overexpressed lncRNA DARS-AS1 compared to normal cells. lncRNA DARS-AS1 knockdown led to reduced OC cell growth and metastasis while inducing the apoptosis in the OC cell model. lncRNA DARS-AS1 positively regulated TSPAN1 expression by binding with miR-194-5p and TSPAN1-mediated ITGA2 hypomethylation in OC cells. Further rescue function studies demonstrated that lncRNA DARS-AS1 affected OC cell viability, migration, invasion, and apoptosis ability by modulating miR-194-5p and TSPAN1 expressions.

Conclusion

Our work demonstrates that lncRNA DARS-AS1 promotes OC progression by modulating TSPAN1 and ITGA2 hypomethylation by binding with miR-194-5p.

Downregulation of lncRNA ASMTL-AS1 in Epithelial Ovarian Cancer Correlates with Worse Prognosis and Cancer Progression

Given the characters of "Silent killer", epithelial ovarian cancer (EOC) usually suffered late diagnosis and poor prognosis. Therefore, this study aimed to explore the prognostic significance of ASMTL-AS1 in EOC and investigated the effect of lncRNA ASMTL-AS1 dysregulation on tumor cellular function. ASMTL-AS1 expression was analyzed in 133 EOC tissues and five kinds of cell lines by RT-qPCR. The expression of ASMTL-AS1 was tested for correlation with clinical data using the chi-square test and clinical follow-up using Kaplan-Meier method with log-rank test. Further, the prognostic parameters in predicting EOC overall survival were assessed by using multivariate Cox proportional hazards analysis. In vitro assays, including MTT assay and transwell assay, were conducted using EOC cell lines with overexpression of ASMTL-AS1. In tumorous tissues and cell lines, ASMTL-AS1 was lowly expressed compared with normal ones. This downregulation was associated with the advanced FIGO stage, positive ascites cytology, and lymph node. In particular, low levels of ASMTL-AS1 were revealed to have a high prognostic impact on EOC. ASMTL-AS1 overexpression strongly decreased cell proliferation, migration, and invasion in vitro partly by moderating miR-1228-3p. This study demonstrates a significant role for lowly expressed ASMTL-AS1 in EOC allowing for the prediction of prognosis for EOC. Considering that ASMTL-AS1 is strongly involved in cell growth and invasion, ASMTL-AS1 may be a promising marker for EOC prognosis and therapy.

Identification of A Risk Signature Based on Lactic Acid Metabolism-Related LncRNAs in Patients With Esophageal Squamous Cell Carcinoma

Lactic acid, formerly thought of as a byproduct of glycolysis or a metabolic waste produced, has now been identified as a key regulator of cancer growth, maintenance, and progression. However, the results of investigations on lactic acid metabolism-related long non-coding RNAs (LRLs) in esophageal squamous cell carcinoma (ESCC) remain inconclusive. In this study, univariate Cox regression analysis was carried out in the TCGA cohort, and 9 lncRNAs were shown to be significantly associated with prognosis. Least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate Cox regression analysis were then used in the GEO cohort. 6 LRLs were identified as independent prognostic factors for ESCC patients used to construct a prognostic risk-related signature subsequently. Two groups were formed based on the middle value of risk scores: a low-risk group and a high-risk group. Following that, we conducted Kaplan-Meier survival analysis, which revealed that the high-risk group had a lower survival probability than the low-risk group in both GEO and TCGA cohorts. On multivariate Cox regression analysis, the prognostic signature was shown to be independent prognostic factor, and it was found to be a better predictor of the prognosis of ESCC patients than the currently widely used grading and staging approaches. The established nomogram can be conveniently applied in the clinic to predict the 1-, 3-, and 5- year survival rates of patients. There was a significant link found between the 6 LRLs-based prognostic signature and immune-cell infiltration, tumor microenvironment (TME), tumor somatic mutational status, and chemotherapeutic treatment sensitivity in the study population. Finally, we used GTEx RNA-seq data and qRT-PCR experiments to verify the expression levels of 6 LRLs. In conclusion, we constructed a prognostic signature which could predict the prognosis and immunotherapy response of ESCC patients.

PTH2R is related to cell proliferation and migration in ovarian cancer: a multi-omics analysis of bioinformatics and experiments

Background

Ovarian cancer is a common gynecological disease and seriously endangers women's health. Currently, there is still a lack of effective molecular markers for the diagnosis and treatment of ovarian cancer. The present study aimed to investigate the molecular markers associated with ovarian cancer.

Methods

The molecular and gene related to ovarian cancer were extracted from GEO database and TCGA database by bioinformatics, and the related genes and functions were further analyzed. The results were verified by qPCR, WB, CCK-8 and Transwell experiments.

Results

Data analysis showed that PTH2R gene was highly expressed in tumors, and 51 HUB genes were obtained. Finally, experimental verification showed that PTH2R gene was highly expressed in ovarian cancer, and PTH2R gene was involved in the proliferation, invasion and metastasis of ovarian cancer cells.

Conclusions

After experimental verification, we found that knocking down the expression of PTH2R can inhibit the proliferation, invasion and migration of tumor cells.PTH2R is expected to become a new molecular marker for ovarian cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-022-02566-2.

Derivation, Comprehensive Analysis, and Assay Validation of a Pyroptosis-Related lncRNA Prognostic Signature in Patients With Ovarian Cancer

Background

Pyroptosis is regulated by long non-coding RNAs (lncRNAs) in ovarian cancer (OC). Therefore, a comprehensive analysis of pyroptosis-related lncRNAs (PRLs) in OC is crucial for developing therapeutic strategies and survival prediction.

Methods

Based on public database raw data, mutations in the landscape of pyroptosis-related genes (PRGs) in patients with OC were investigated thoroughly. PRLs were identified by calculating Pearson correlation coefficients. Cox and LASSO regression analyses were performed on PRLs to screen for lncRNAs participating in the risk signature. Furthermore, receiver operating characteristic (ROC) curves, Kaplan-Meier survival analyses, decision curve analysis (DCA) curves, and calibration curves were used to confirm the clinical benefits. To assess the ability of the risk signature to independently predict prognosis, it was included in a Cox regression analysis with clinicopathological parameters. Two nomograms were constructed to facilitate clinical application. In addition, potential biological functions of the risk signature were investigated using gene function annotation. Subsequently, immune-related landscapes and BRCA1/2 mutations were compared in different risk groups using diverse bioinformatics algorithms. Finally, we conducted a meta-analysis and in-vitro assays on alternative lncRNAs.

Results

A total of 374 patients with OC were randomized into training and validation cohorts (7:3). A total of 250 PRLs were selected from all the lncRNAs. Subsequently, a risk signature (DICER1-AS1, MIR600HG, AC083880.1, AC109322.1, AC007991.4, IL6R-AS1, AL365361.1, and AC022098.2) was constructed to distinguish the risk of patient survival. The ROC curve, K-M analysis, DCA curve, and calibration curve indicated excellent predictive performance for determining overall survival (OS) based on the risk signature in each cohort (p < 0.05). The Cox regression analysis indicated that the risk signature was an independent prognostic factor for OS (p < 0.05). Moreover, significant differences in the immune response and BRCA1 mutations were identified in different groups distinguished by the risk signature (p < 0.05). Interestingly, in-vitro assays showed that an alternative lncRNA (DICER1-AS1) could promote OC cell proliferation.

Conclusion

The PRL risk signature could independently predict overall survival and guide treatment in patients with OC.

Integrated clinical characteristics and omics analysis identifies a ferroptosis and iron-metabolism-related lncRNA signature for predicting prognosis and therapeutic responses in ovarian cancer

Background

Ferroptosis and iron-metabolism are regulated by Long non-coding RNAs (lncRNAs) in ovarian cancer (OC). Therefore, a comprehensive analysis of ferroptosis and iron-metabolism related lncRNAs (FIRLs) in OC is crucial for proposing therapeutic strategies and survival prediction.

Methods

In multi-omics data from OC patients, FIRLs were identified by calculating Pearson correlation coefficients with ferroptosis and iron-metabolism related genes (FIRGs). Cox-Lasso regression analysis was performed on the FIRLs to screen further the lncRNAs participating in FIRLs signature. In addition, all patients were divided into two robust risk subtypes using the FIRLs signature. Receiver operator characteristic (ROC) curve, Kaplan–Meier analysis, decision curve analysis (DCA), Cox regression analysis and calibration curve were used to confirm the clinical benefits of FIRLs signature. Meanwhile, two nomograms were constructed to facilitate clinical application. Moreover, the potential biological functions of the signature were investigated by genes function annotation. Finally, immune microenvironment, chemotherapeutic sensitivity, and the response of PARP inhibitors were compared in different risk groups using diversiform bioinformatics algorithms.

Results

The raw data were randomized into a training set (n = 264) and a testing set (n = 110). According to Pearson coefficients between FIRGs and lncRNAs, 1075 FIRLs were screened for univariate Cox regression analysis, and then LASSO regression analysis was used to construct 8-FIRLs signature. It is worth mentioning that a variety of analytical methods indicated excellent predictive performance for overall survival (OS) of FIRLs signature (p < 0.05). The multivariate Cox regression analysis showed that FIRLs signature was an independent prognostic factor for OS (p < 0.05). Moreover, significant differences in the abundance of immune cells, immune-related pathways, and drug response were excavated in different risk subtypes (p < 0.05).

Conclusion

The FIRLs signature can independently predict overall survival and therapeutic effect in OC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13048-022-00944-y.

Long Intergenic Noncoding RNA OIN1 Promotes Ovarian Cancer Growth by Modulating Apoptosis-Related Gene Expression

Patients with advanced ovarian cancer usually exhibit high mortality rates, thus more efficient therapeutic strategies are expected to be developed. Recent transcriptomic studies revealed that long intergenic noncoding RNAs (lincRNAs) can be a new class of molecular targets for cancer management, because lincRNAs likely exert tissue-specific activities compared with protein-coding genes or other noncoding RNAs. We here show that an unannotated lincRNA originated from chromosome 10q21 and designated as ovarian cancer long intergenic noncoding RNA 1 (OIN1), is often overexpressed in ovarian cancer tissues compared with normal ovaries as analyzed by RNA sequencing. OIN1 silencing by specific siRNAs significantly exerted proliferation inhibition and enhanced apoptosis in ovarian cancer cells. Notably, RNA sequencing showed that OIN1 expression was negatively correlated with the expression of apoptosis-related genes ras association domain family member 5 (RASSF5) and adenosine A1 receptor (ADORA1), which were upregulated by OIN1 knockdown in ovarian cancer cells. OIN1-specifc siRNA injection was effective to suppress in vivo tumor growth of ovarian cancer cells inoculated in immunodeficient mice. Taken together, OIN1 could function as a tumor-promoting lincRNA in ovarian cancer through modulating apoptosis and will be a potential molecular target for ovarian cancer management.