Prognostic lncRNAs Research Articles

Constructing a Ferroptosis-related Long Non-coding RNA Signature to Predict the Prognostic of Head and Neck Squamous Cell Carcinoma Patients by Bioinformatic Analysis

Ferroptosis is a novel discovered iron-dependent mode of regulated cell death (RCD) which characterized non-apoptosis. Researches have shown the effect of ferroptosis in the biological activities of tumors. But there is no relevant study showing the relationship between ferroptosis-related genes and head and neck squamous cell carcinoma (HNSCC). In this study, we paid attention to several ferroptosis-related lncRNAs in head and neck squamous cell carcinoma and its clinical features. HNSCC data were obtained from The Cancer Genome Atlas (TCGA) database. Ferroptosis-related lncRNAs were selected by the coexpression network. Then, we identified prognostic ferroptosis-related lncRNAs in HNSCC patients and constructed a ferroptosis-related lncRNAs signature by Lasso cox regression. Besides, GSEA analysis was performed to explore the functional enrichment of ferroptosis-related lncRNAs. Nine ferroptosis-related lncRNAs (AC004687.1, AL450992.2, AC010894.2, AL451085.2, AC104083.1, LIPE-AS1, AC108010.1, CTBP1-DT, and PTCSC2) were identified to have the independent prognostic value in HNSCC patients and the ferroptosis-related lncRNAs signature was constructed based on these nine genes. According to the risk score of the signature, the high-risk group had shorter overall survival (OS) compared with the low-risk group. Risk score showed to be an independent factor for the patients with HNSCC. Additionally, the nomogram was generated and the nine lncRNAs were mainly enriched in phagocytosis, metabolism, and chemokine signaling pathways. The ferroptosis-related lncRNA signature has effects on the prognostic prediction of patients with HNSCC which may serve as potential therapeutic targets for patients with HNSCC.

Identification of an Eight-lncRNA Signature as the Prognostic lncRNA Markers in Hepatocellular Carcinoma Patients

Long non-coding RNA (lncRNA) signature has been reputable for the predetermination of cancer prognosis. In the present study, we constructed a lncRNA model to predict the survival outcomes for hepatocellular carcinoma (HCC) patients. Using the transcriptome data from TCGA HCC samples, we identified that NEAT1 and MALAT1 were highly expressed in HCC and other tumor subtypes compared to the adjacent normal tissues. Based on the LASSO Cox regression model, we identified an eight-lncRNA signature that significantly correlated with the overall survival and disease-free survival of the HCC training group. The prognostic value of this signature was validated using the test group. Further analysis suggested that this signature was associated with the clinicopathological parameters such as vascular tumor invasion, pathological stage, and tumor grade. Integrated functional analysis showed that these eight-lncRNAs were involved in the cell cycle, metabolic process, and immune response. In conclusion, we constructed an applicable eight-lncRNA signature that is robust and reliable for the prognosis of HCC. This signature may provide an efficient clinical prediction for HCC patients, and further study is required to uncover the function of the identified lncRNAs better.

Comprehensive Analysis of N6-Methylandenosine-Related Long Non-Coding RNAs Signature in Prognosis and Tumor Microenvironment of Bladder Cancer.

To investigate the role of N6-methyladenosine (m6A)- related long non-coding RNAs (lncRNAs) in bladder cancer (BC). 50 m6A-related lncRNAs were screened out and were correlated with prognosis from BC samples in The Cancer Genome Atlas (TCGA). The lncRNAs were subdivided into cluster 1 and cluster 2 with consensus cluster analysis, and it was found that lncRNAs in cluster 2 were associated with poor prognosis and increased PD-L1 expression. Gene set enrichment analysis (GSEA) revealed tumor-related pathways in cluster 2. Through least absolute shrinkage and selection operator (LASSO) Cox regression analysis, univariate and multivariate Cox regression, and ROC analyses, 14 prognostic lncRNAs were selected and used to construct the m6A-related lncRNA prognostic signature (m6A-LPS), furthermore, that m6A-LPS was as a valuable independent prognostic factor. Interestingly, the m6A-LPS risk score was positively correlated with the immune score, PD-L1 expression, and the infiltration of immune cell subtypes in BC. SNHG16, a member of the high-risk group based on m6A-LPS, was highly expressed in BC tissues and cell lines and interfered with siRNA resulted in suppressed proliferation, migration, and invasion in vitro. Our study illustrates the role of m6A-related lncRNAs in BC. The m6A-LPS may be an important regulatory target of the tumor microenvironment (TME) in BC.

N6-Methyladenosine-Related lncRNAs as potential biomarkers for predicting prognoses and immune responses in patients with cervical cancer

BackgroundSeveral recent studies have confirmed epigenetic regulation of the immune response. However, the potential role of RNA N6-methyladenosine (m6A) modifications in cervical cancer and tumour microenvironment (TME) cell infiltration remain unclear.ResultsWe evaluated and analysed m6A modification patterns in 307 cervical cancer samples from The Cancer Genome Atlas (TCGA) dataset based on 13 m6A regulators. Pearson correlation analysis was used to identify lncRNAs associated with m6A, followed by univariate Cox regression analysis to screen their prognostic role in cervical cancer patients. We also correlated TME cell infiltration characteristics with modification patterns. We screened six m6A-associated lncRNAs as prognostic lncRNAs and established the prognostic profile of m6A-associated lncRNAs by least absolute shrinkage and choice of operator (LASSO) Cox regression. The corresponding risk scores of the patients were derived based on their prognostic features, and the correlation between this feature model and disease prognosis was analysed. The prognostic model constructed based on the TCGA-CESC (The Cancer Genome Cervical squamous cell carcinoma and endocervical adenocarcinoma) dataset showed strong prognostic power in the stratified analysis and was confirmed as an independent prognostic indicator for predicting the overall survival of patients with CESC. Enrichment analysis showed that biological processes, pathways, and markers associated with malignancy were more common in the high-risk subgroup. Risk scores were strongly correlated with the tumour grade. ECM receptor interactions and pathways in cancer were enriched in Cluster 2, while oxidative phosphorylation and other biological processes were enriched in Cluster 1. The expression of immune checkpoint molecules, including programmed death 1 (PD-1) and programmed death ligand 1 (PD-L1), was significantly increased in the high-risk subgroup, suggesting that this prognostic model could be a predictor of immunotherapy.ConclusionsThis study reveals that m6A modifications play an integral role in the diversity and complexity of TME formation. Assessing the m6A modification patterns of individual tumours will help improve our understanding of TME infiltration characteristics and thus guide immunotherapy more effectively. We also developed an independent prognostic model based on m6A-associated lncRNAs as a predictor of overall survival, which can also be used as a predictor of immunotherapy.

Identification of Immune-Related Prognostic mRNA and lncRNA in Patients with Hepatocellular Carcinoma.

Background As the most common hepatic malignancy, hepatocellular carcinoma (HCC) has a high incidence; therefore, in this paper, the immune-related genes were sought as biomarkers in liver cancer. Methods In this study, a differential expression analysis of lncRNA and mRNA in The Cancer Genome Atlas (TCGA) dataset between the HCC group and the normal control group was performed. Enrichment analysis was used to screen immune-related differentially expressed genes. Cox regression analysis and survival analysis were used to determine prognostic genes of HCC, whose expression was detected by molecular experiments. Finally, important immune cells were identified by immune cell infiltration and detected by flow cytometry. Results Compared with the normal group, 1613 differentially expressed mRNAs (DEmRs) and 1237 differentially expressed lncRNAs (DElncRs) were found in HCC. Among them, 143 immune-related DEmRs and 39 immune-related DElncRs were screened out. These genes were mainly related to MAPK cascade, PI3K-AKT signaling pathway, and TGF-beta. Through Cox regression analysis and survival analysis, MMP9, SPP1, HAGLR, LINC02202, and RP11-598F7.3 were finally determined as the potential diagnostic biomarkers for HCC. The gene expression was verified by RT-qPCR and western blot. In addition, CD4 + memory resting T cells and CD8 + T cells were identified as protective factors for overall survival of HCC, and they were found highly expressed in HCC through flow cytometry. Conclusion The study explored the dysregulation mechanism and potential biomarkers of immune-related genes and further identified the influence of immune cells on the prognosis of HCC, providing a theoretical basis for the prognosis prediction and immunotherapy in HCC patients.

Prognostic Ferroptosis-Related lncRNA Signatures Associated With Immunotherapy and Chemotherapy Responses in Patients With Stomach Cancer.

Ferroptosis is associated with the prognosis and therapeutic responses of patients with various cancers. LncRNAs are reported to exhibit antitumor or oncogenic functions. Currently, few studies have assessed the combined effects of ferroptosis and lncRNAs on the prognosis and therapy of stomach cancer. In this study, transcriptomic and clinical data were downloaded from TCGA database, and ferroptosis-related genes were obtained from the FerrDb database. Through correlation analysis, Cox analysis, and the Lasso algorithm, 10 prognostic ferroptosis-related lncRNAs (AC009299.2, AC012020.1, AC092723.2, AC093642.1, AC243829.4, AL121748.1, FLNB-AS1, LINC01614, LINC02485, LINC02728) were screened to construct a prognostic model, which was verified in two test cohorts. Risk scores for patients with stomach cancer were calculated, and patients were divided into two risk groups. The low-risk group, based on the median value, had a longer overall survival time in the KM curve, and a lower proportion of dead patients in the survival distribution curve. Potential mechanisms and possible functions were revealed using GSEA and the ceRNA network. By integrating clinical information, the association between lncRNAs and clinical features was analyzed and several features affecting prognosis were identified. Then, a nomogram was developed to predict survival rates, and its good predictive performance was indicated by a relatively high C-index (0.67118161) and a good match in calibration curves. Next, the association between these lncRNAs and therapy was explored. Patients in the low-risk group had an immune-activating environment, higher immune scores, higher TMB, lower TIDE scores, and higher expression of immune checkpoints, suggesting they might receive a greater benefit from immune checkpoint inhibitor therapy. In addition, a significant difference in the sensitivity to mitomycin. C, cisplatin, and docetaxel, but not etoposide and paclitaxel, was observed. In summary, this model had guiding significance for prognosis and personalized therapy. It helped screen patients with stomach cancer who might benefit from immunotherapy and guided the selection of personalized chemotherapeutic drugs.

Aerial View of the Association Between m6A-Related LncRNAs and Clinicopathological Characteristics of Pancreatic Cancer.

Pancreatic cancer is a highly malignant tumor with a poor survival prognosis. We attempted to establish a robust prognostic model to elucidate the clinicopathological association between lncRNA, which may lead to poor prognosis by influencing m6A modification, and pancreatic cancer. We investigated the lncRNAs expression level and the prognostic value in 440 PDAC patients and 171 normal tissues from GTEx, TCGA, and ICGC databases. The bioinformatic analysis and statistical analysis were used to illustrate the relationship. We implemented Pearson correlation analysis to explore the m6A-related lncRNAs, univariate Cox regression and Kaplan-Meier methods were performed to identify the seven prognostic lncRNAs signatures. We inputted them in the LASSO Cox regression to establish a prognostic model in the TCGA database, verified in the ICGC database. The AUC of the ROC curve of the training set is 0.887, while the validation set is 0.711. Each patient has calculated a risk score and divided it into low-risk and high-risk subgroups by the median value. Moreover, the model showed a robust prognostic ability in the stratification analysis of different risk subgroups, pathological grades, and recurrence events. We established a ceRNA network between lncRNAs and m6A regulators. Enrichment analysis indicated that malignancy-associated biological function and signaling pathways were enriched in the high-risk subgroup and m6A-related lncRNAs target mRNA. We have even identified small molecule drugs, such as Thapsigargin, Mepacrine, and Ellipticine, that may affect pancreatic cancer progression. We found that seven lncRNAs were highly expressed in tumor patients in the GTEx-TCGA database, and LncRNA CASC19/UCA1/LINC01094/LINC02323 were confirmed in both pancreatic cell lines and FISH relative quantity. We provided a comprehensive aerial view between m6A-related lncRNAs and pancreatic cancer’s clinicopathological characteristics, and performed experiments to verify the robustness of the prognostic model.

Comprehensive Analysis of N6-Methyladenosine-Related IncRNAs Prognostic Signature and the Associated Immune Infiltrates in Kidney Renal Clear Cell Carcinoma.

Kidney renal clear cell carcinoma (KIRC) is the most common and aggressive subtype of renal cell carcinoma. N6-methyladenosine (m6A) RNA methylation is the most prevalent epigenetic RNA modification. Long non-coding RNAs (lncRNAs) have emerged as a key role in regulating cancer progression. However, little has been learned about the molecular functions of m6A-related lncRNAs in KIRC. The prognostic value of m6A-related ln-cRNAs was investigated in KIRC samples downloaded from The Cancer Genome Atlas (TCGA) dataset. The m6A-related lncRNAs were further screen out by Pearson correlation test. Then, 27 m6A-related lncRNAs were confirmed as potential prognostic factors through univariate Cox regression analysis. They were entered into Lasso and multivariate Cox regression to build a m6A-related lncRNA prognostic signature, including 14 m6A-related lncRNAs determined as independent prognostic factors. Additionally, a risk score calculated according to the prognostic model could divide KIRC patients into low- and high-risk groups depending on median risk score as cut-off. A prognostic nomogram, derived from the prognostic model and integrating clinical characteristics of patients, was constructed. Three distinct clusters were identified with different immune signatures through consensus clustering analysis according to the expression pattern of m6A-related lncRNAs. Twenty-seven prognostic m6A-related lncRNAs were determined as prognostic lncRNAs from TCGA-KIRC cohort. The m6A-related lncRNA prognostic signature containing 14 independent prognostic lncRNAs exhibited good accuracy in predicting overall survival of KIRC patients. We correlated the three distinct clusters with immune infiltration signature of KIRC for the first time. We found that the worse prognosis of cluster2 was probably mediated by immune evasion. In summary, our study identified a m6A-related lncRNAs prognostic signature which had great clinical value in prognosis assessment. We classified TCGA-KIRC samples into three clusters with distinct immune signatures, which could be considered as potential targets of immunotherapy for KIRC treatment in the future.

Identification and Validation of a Novel 2-LncRNAs Signature Associated with m6A Regulation in Colorectal Cancer.

Colorectal cancer (CRC) is one of the most common tumors in the digestive system, and it is urgent to identify a new biomarker for the diagnosis and treatment of CRC. N6-methyladenosine (m6A) is an abundant mRNA modification and is almost involved in every aspect of physiological processes. In this study, we constructed a novel m6A-related 2-lncRNAs signature that can predict the prognosis of CRC. We obtained m6A-related lncRNAs and identified prognostic lncRNAs through univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) analysis, then constructed a prognostic model based on the risk score, and we also verified the stability of the model. In addition, differential expression analysis between the high- and low-risk subgroups was performed. A total of 1,894 m6A-related lncRNAs were screened from various sources. Using univariate Cox regression analysis and survival analysis, two lncRNAs (AL135999.1 and AL049840.4) were identified (P < 0.05), and the coefficients of lncRNAs were calculated by LASSO. The high-risk group had worse clinical outcomes and overall survival (OS) than the low-risk group, and the risk score can serve as an independent prognostic factor in CRC. In addition, different stages of CRC also showed a different level of risk score. Finally, we found that two lncRNAs were differentially expressed (P < 0.01) in CRC patients, and AL135999.1 may be relevant to m6A modification mediated by methyltransferase-like 3 (METTL3) in CRC. In summary, we constructed a reliable 2-lncRNAs signature based on the risk score, and we identified two m6A-related prognostic lncRNAs, AL135999.1 and AL049840.4. The novel 2-lncRNAs signature plays an essential role in predicting the prognosis of CRC.

Prognostic Value of Macrophage-Associated Long Non-Coding RNA Expression for Hepatocellular Carcinoma.

BackgroundThere is ample evidence that macrophages play a crucial role in the biological processes of hepatocellular carcinoma (HCC). This study was designed to establish a novel macrophage-associated prognostic model for HCC.MethodsLong non-coding RNA (lncRNA) microarrays and clinical data in The Cancer Genome Atlas (TCGA) database were analysed using a univariate Cox proportional regression model to select macrophage-associated prognostic lncRNAs. Multivariate Cox proportional regression models and survival analysis were used to establish a prognosis index (PI) model. Furthermore, to better understand the biological functions of differentially expressed macrophage-associated lncRNAs (MALs) in HCC, enrichment analysis was performed. Finally, the correlation between MALs and clinical features was further analysed in HCC.ResultsWe identified eight MALs with significant prognostic values for HCC. Next, a PI model for HCC was developed, and patients were classified into the high-risk or low-risk group based on risk scores. The overall survival (OS) of high-risk patients was significantly shorter than that of low-risk patients (P < 0.001). Univariate and multivariate factors indicate that risk scores can be used as independent prognostic factors for patients with HCC. Multiple receiver operating characteristic (ROC) plots show that the area under the ROC curve (AUC) of the risk score is higher than that of other clinical features. The C-index of our nomogram was 0.768.ConclusionThe PI model has a prognostic efficacy superior to that of other clinical features.

The ETS1-LINC00278 negative feedback loop plays a role in COL4A1/COL4A2 regulation in laryngeal squamous cell carcinoma.

The present study aimed to investigate LINC00278 expression in laryngeal squamous cell carcinoma (LSCC) and its involvement in the process of proliferation, migration, and invasion, providing a rationale for mining potential diagnostic and therapeutic targets of LSCC. Univariate and multivariate Cox regression analyses were performed to identify optimal prognostic lncRNAs. MTS, colony formation, wound healing, and Transwell invasion assays were used to determine the effects of LINC00278 overexpression on the proliferation, migration, and invasion of cancer cells. The expressions of signaling pathway-related proteins and epithelial-mesenchymal transition (EMT) marker proteins were detected using western blot. The chromatin immunoprecipitation (ChIP) and dual-luciferase reporter assays were performed to demonstrate the binding of ETS proto-oncogene 1, transcription factor (ETS1), and LINC00278 promoter region. The molecular targets of LINC00278 were identified by RNA sequencing analysis and co-expression analysis. Kaplan-Meier analysis and CIBERSORT algorithm were used to analyze survival and immune cell infiltration based on LINC00278, COL4A1, and COL4A2. Multivariate Cox regression was used to establish a six-gene prognostic model. LINC00278 expression was low in LSCC tissues, and it was significantly associated with the TNM (tumors/nodes/metastases) stage (p<0.001), lymphatic metastasis (p<0.01), and pathological differentiation (p<0.01). LINC00278 overexpression significantly reduced LSCC cell proliferation, migration, and invasion in TU686, TU177, and AMC-HN-8 cell lines. E-cadherin protein expression was increased, while N-cadherin, Vimentin, Zeb1, and Snail protein expression was decreased in the LINC00278 group, compared to the pcDNA3.1 group. Additionally, in AMC-HN-8 and FaDu cell lines, the LINC00278-treated group had significantly lower p-AKT and p-mTOR protein levels than the control group. ETS1 is a direct transcriptional regulator of the LINC00278 gene based on luciferase reporter assays and ChIP experiments. Western blot analysis demonstrated that high LINC00278 expression inhibited both ETS1 expression and phosphorylation. COL4A1/COL4A2 were identified as potential downstream targets of LINC00278. Meanwhile, the LINC00278/COL4A1/COL4A2-dominated low-risk group showed higher antigen-presenting activity and a higher immune score than the high-risk group. The findings indicated that ETS1 upregulated LINC00278 expression on the Y chromosome, which in turn inhibited LSCC growth in vivo and in vitro by inhibiting the AKT/mTOR signaling pathway via downregulation of COL4A1/COL4A2.

N6-methyladenosine Modification-Related Long Non-Coding RNAs are Potential Biomarkers for Predicting the Prognosis of Patients With Osteosarcoma.

Background: The role of N6-methyladenosine (m6A)-related long non-coding RNAs (lncRNAs) in osteosarcoma (OS) has not been fully studied yet. We aimed to identify m6A-related lncRNAs that could act as prognostic biomarkers for OS. Methods: Pearson correlation was performed to identify m6A-related lncRNAs. Univariate and multivariate Cox regression analyses were performed to construct the risk model and assess whether the risk score was an independent prognostic factor for patients with OS. Gene Set Enrichment Analysis (GSEA) was performed to analyze the functions of genes in high-risk and low-risk groups. StarBase and Cytoscape were used to construct a competing endogenous RNA (ceRNA) network based on m6A-related prognostic lncRNA signature. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to analyze the function of genes involved in the ceRNA network. Results: We extracted 122 common lncRNAs from TCGA and Gene Expression Omnibus (GEO) databases. Pearson correlation results revealed 59 significant m6A-related lncRNAs in The Cancer Genome Atlas (TCGA) database, from which 2 were screened to construct a risk signature in TCGA dataset, which was then validated in the GEO dataset. A corresponding risk score was calculated and shown to be an independent prognostic factor for patients with OS. Enrichment analysis indicated that cell proliferation-related biological processes were more common in the high-risk group, while immune-related biological processes were more common in the low-risk group. Moreover, we established a nomogram that had a good ability to predict the overall survival of patients with OS. Additionally, a ceRNA network based on small nucleolar RNA host gene 7 (SNHG7) and small nucleolar RNA host gene 12 (SNHG12) was constructed, with genes that were enriched in hepatocellular carcinoma, gastric cancer, and non-small-cell lung cancer pathways. Conclusion: Our study revealed the prognostic role of m6A-related lncRNAs in OS and identified SNHG7 and SNHG12 as potential biomarkers for predicting the prognosis of patients with OS. These findings have enriched our understanding of the role of m6A modification in the dysregulation of lncRNAs in OS.

Identification of Differentially Expressed and Prognostic lncRNAs for the Construction of ceRNA Networks in Lung Adenocarcinoma.

Background Long noncoding RNAs (lncRNAs) could function as competitive endogenous RNAs (ceRNAs) to competitively adsorb microRNAs (miRNAs), thereby regulating the expression of their target protein-coding mRNAs. In this study, we aim to identify more effective diagnostic and prognostic markers for lung adenocarcinoma (LUAD). Methods We obtained differentially expressed lncRNAs (DElncRNAs), miRNAs (DEmiRNAs), and mRNAs (DEmRNAs) for LUAD by using The Cancer Genomes Atlas (TCGA) portal. Weighted gene coexpression network analysis (WGCNA) was performed to unveil core gene modules associated with LUAD. The Cox proportional hazards model was performed to determine the prognostic significance of DElncRNAs. The diagnostic and prognostic significance of DElncRNAs was further verified based on the receiver operating characteristic curve (ROC). Cytoscape was used to construct the ceRNA networks comprising the lncRNAs-miRNAs-mRNAs axis based on the correlation obtained from the miRcode, miRDB, and TargetScan. Results Compared with normal lung tissues, 2355 DElncRNAs, 820 DEmiRNAs, and 17289 DEmRNAs were identified in LUAD tissues. We generated 8 WGCNA core modules in the lncRNAs coexpression network, 5 modules in the miRNAs, and 12 modules in the mRNAs coexpression network, respectively. One lncRNA module (blue) consisting of 441 lncRNAs, two miRNA modules (blue and turquoise) containing 563 miRNAs, and one mRNA module (turquoise), which consisted of 15162 mRNAs, were mostly significantly related to LUAD status. Furthermore, 67 DEmRNAs were found to be tumor-associated as well as the target genes of the DElncRNAs-DEmiRNAs axis. Survival analyses showed that 6 lncRNAs (LINC01447, WWC2-AS2, OGFRP1, LINC00942, LINC01168, and AC005863.1) were significantly correlated with the prognosis of LUAD patients. Ultimately, the potential ceRNA networks including 6 DElncRNAs, 4 DEmiRNAs, and 22 DEmRNAs were constructed. Conclusion Our study indicated that 6 DElncRNAs had the possibilities as diagnostic and prognostic biomarkers for LUAD. The lncRNA-mediated ceRNA networks might provide novel insights into the molecular mechanisms of LUAD progression.

Potential Prognostic Value of a Seven m6A-Related LncRNAs Signature and the Correlative Immune Infiltration in Colon Adenocarcinoma.

Long non-coding RNAs (lncRNAs) and their N6-methyladenosine (m6A) modifications play an essential role in tumorigenesis and cancer progression. This study was designed to explore the value of m6A-related lncRNAs in prognosis and therapeutic applications of immune infiltration of colon adenocarcinoma (COAD). We downloaded the COAD gene expression and clinical data from The Cancer Genome Atlas project. By co-expression analysis, Lasso Cox regression analysis, and univariate and multivariate Cox regression, we constructed an independent prognostic signature of seven m6A-related lncRNAs. The prognostic lncRNAs were divided into two clusters by consistent clustering analysis, as well as into two groups of low–high risk based on the signature. Then we identified the relationship between the different groups with clinical features and immune cell infiltration. Cluster 2 had a higher risk score with a lower survival rate. The risk score was higher in groups with advanced clinical features, such as stage III–IV, N1-3, and M1. The expression of AC156455.1 was increased in tumor tissues and cluster 2, and the lncRNA ZEB1−AS1 was notably higher in the high-risk group. Five types of immune cells showed differences in two clusters, and most were upregulated in type 2. The expression of memory B cells was positively correlated with the risk score. The prognostic model was verified by the Gene Expression Omnibus (GEO) dataset. Besides, we found that the expression of these seven lncRNAs in tumor tissues was significantly higher than that in normal tissues, which verified the feasibility of the model. Thus, the signature of seven m6A-related lncRNAs can independently predict the prognosis of COAD. This signature is also closely associated with immune cell infiltration, and new therapeutic targets can be explored from this field.

Development and Validation of a Novel Hypoxia-Related Long Noncoding RNA Model With Regard to Prognosis and Immune Features in Breast Cancer.

Background: Female breast cancer is currently the most frequently diagnosed cancer in the world. This study aimed to develop and validate a novel hypoxia-related long noncoding RNA (HRL) prognostic model for predicting the overall survival (OS) of patients with breast cancer. Methods: The gene expression profiles were downloaded from The Cancer Genome Atlas (TCGA) database. A total of 200 hypoxia-related mRNAs were obtained from the Molecular Signatures Database. The co-expression analysis between differentially expressed hypoxia-related mRNAs and lncRNAs based on Spearman’s rank correlation was performed to screen out 166 HRLs. Based on univariate Cox regression and least absolute shrinkage and selection operator Cox regression analysis in the training set, we filtered out 12 optimal prognostic hypoxia-related lncRNAs (PHRLs) to develop a prognostic model. Kaplan–Meier survival analysis, receiver operating characteristic curves, area under the curve, and univariate and multivariate Cox regression analyses were used to test the predictive ability of the risk model in the training, testing, and total sets. Results: A 12-HRL prognostic model was developed to predict the survival outcome of patients with breast cancer. Patients in the high-risk group had significantly shorter median OS, DFS (disease-free survival), and predicted lower chemosensitivity (paclitaxel, docetaxel) compared with those in the low-risk group. Also, the risk score based on the expression of the 12 HRLs acted as an independent prognostic factor. The immune cell infiltration analysis revealed that the immune scores of patients in the high-risk group were lower than those of the patients in the low-risk group. RT-qPCR assays were conducted to verify the expression of the 12 PHRLs in breast cancer tissues and cell lines. Conclusion: Our study uncovered dozens of potential prognostic biomarkers and therapeutic targets related to the hypoxia signaling pathway in breast cancer.

A Prognostic Pyroptosis-Related lncRNAs Risk Model Correlates With the Immune Microenvironment in Colon Adenocarcinoma.

Recent studies have indicated that long non-coding RNAs (lncRNAs) may participate in the regulation of tumor cell proptosis. However, the connection between lncRNA expression and pyroptosis remains unclear in colon adenocarcinoma (COAD). This study aims to explore and establish a prognostic signature of COAD based on the pyroptosis-related lncRNAs. We identify 15 prognostic pyroptosis-related lncRNAs (ZNF667-AS1, OIP5-AS1, AL118506.1, AF117829.1, POC1B-AS1, CCDC18-AS1, THUMPD3-AS1, FLNB-AS1, SNHG11, HCG18, AL021707.2, UGDH-AS1, LINC00641, FGD5-AS1 and AC245452.1) from the TCGA-COAD dataset and use them to construct the risk model. After then, this pyroptosis-related lncRNA signature is validated in patients from the GSE17536 dataset. The COAD patients are divided into low-risk and high-risk groups by setting the median risk score as the cut-off point and represented differences in the immune microenvironment. Hence, we construct the immune risk model based on the infiltration levels of ssGSEA immune cells. Interestingly, the risk model and immune risk model are both independent prognostic risk factors. Therefore, a nomogram combined risk score, immune risk score with clinical information which is meaningful in univariate and multivariate Cox regression analysis is established to predict the overall survival (OS) of COAD patients. In general, the signature consisted of 15 pyroptosis-related lncRNAs and was proved to be associated with the immune landscape of COAD patients.

Identification and in vitro validation of prognostic lncRNA signature in head and neck squamous cell carcinoma

ABSTRACT Long non-coding RNAs (lncRNAs) are promising cancer prognostic markers. However, the clinical significance of lncRNA signatures in evaluating overall survival (OS) outcomes of head and neck squamous cell carcinoma (HNSCC) has not been explored. This study aimed to assess the significance of lncRNA in HNSCC and to develop a lncRNA signature related to OS in HNSCC. LncRNA expression matrices were retrieved from the Cancer Genome Atlas (TCGA) data. Least Absolute Shrinkage and Selection of the Operator (LASSO), univariate and multivariate Cox regression were used for establishing a prognostic model. In vitro experiments were carried out to demonstrate the biological role of lncRNA. A prognosis model based on 7 DElncRNAs was finally established.The patients were then divided into high-risk and low-risk groups. Relative to the low-risk group, overall survival times for patients in the high-risk group were significantly low (P=2.466e−07). Risk score remained an independent prognostic factor in univariate (HR=1.329, 95%CI=1.239−1.425, p < 0.001) and multivariate (HR=1.279, 95%CI=1.184−1.382, p < 0.001) Cox regression analyses. The area under the curve (AUC) of the signature was as high as 0.78. Expressions of FOXD2-AS1 in tumor tissues were elevated, and significantly correlated with OS (P=0.008). FOXD2-AS1 silencing then significantly reduced HNSCC cell proliferation, invasion, and migration. In conclusion, a lncRNA signature was established for HNSCC prognostic prediction and FOXD2-AS1 was identified as an HNSCC oncogene.

The Role of Critical N6-Methyladenosine-Related Long Non-Coding RNAs and Their Correlations with Immune Checkpoints in Renal Clear Cell Carcinoma

PurposeThis study aimed to evaluate the functions of critical N6-methyladenosine (m6A)-related long non-coding RNAs (lncRNAs) and their correlations with immunotherapeutic targets in clear cell renal cell carcinoma (ccRCC).Methodsm6A-related lncRNAs were analyzed using the dataset from The Cancer Genome Atlas database via Pearson correlation analysis. Then, their prognostic functions in patients with ccRCC were determined via univariate Cox analysis. A prognostic m6A-related lncRNA signature (MRLS) in ccRCC was established using the least absolute shrinkage and selection operator (LASSO) Cox regression model. In addition, the correlations between these prognostic m6A-related lncRNAs with immune checkpoints were further evaluated in clinical samples.ResultsMRLS was established by the LASSO Cox regression model on the basis of seven prognostic m6A-related lncRNAs. The risk score for each patient was calculated using the MRLS model, and the patients were further stratified into high- and low-risk subgroups. The MRLS model was validated with a robust prognostic ability by the stratification analysis. On the basis of age, grade, stage, and risk score, a nomogram was developed with a strong reliability in forecasting the overall survival percentages of the patients with ccRCC. Moreover, seven prognostic m6A-related lncRNAs enrolled in the MRLS model were found to be correlated with various immunotherapeutic targets, namely, PD-1, PD-L1, CTLA4, and LAG3, and the expression levels of which in the high-risk subgroup were significantly higher than those in the low-risk subgroup. The significant correlations between LINC00342 and the aforementioned immunotherapeutic targets were also confirmed in clinical samples.ConclusionIn this study, seven m6A-related lncRNAs were identified as potential biomarkers for forecasting the prognosis of patients with ccRCC and evaluating the efficacy of immunotherapy for these patients. Furthermore, a prognostic and predictive MRLS model with a high reliability was constructed to predict the overall survival probability of patients with ccRCC.

LncRNA Expression-Based Risk Scoring System Can Predict Survival of Tumor-Positive Patients with Hepatocellular Carcinoma.

Background:Long non-coding RNAs (lncRNAs) play critical roles in the progression of hepatocellular carcinoma (HCC). The aim of this study was to explore whether lncRNA expression profiles can predict prognosis of HCC patients with tumors. Methods:Expression of lncRNAs in HCC patients based on data in The Cancer Genome Atlas (TCGA) was examined by uni- and multivariate cox analysis to identify associations between clinical features and overall survival (OS) or recurrence-free survival (RFS). Based on our finding that both were independently associated with tumor status, we examined lncRNAs differentially expressed between patients with or without tumors. An lncRNA-based risk scoring system was developed to predict OS and RFS in tumor-positive patients, and it was assessed using uni- and multivariate cox analyses. Potential functions of the prognostic lncRNAs were explored. Results:A risk scoring system to predict OS for HCC patients with tumors was developed based on the expression of six lncRNAs (AC090921.1, AC012640.1, AL158839.1, AL356056.1, AL359853.1 and C10orf91), and a corresponding scoring system to predict RFS was developed from nine lncRNAs (AL356056.1, AL158839.1, MIR7-3HG, AL445493.2, AP000808.1, AP003354.2, PLCE1-AS1, TH2LCRR and LINC01447). Both risk scoring systems gave areas under receiver operating characteristic curves >0.7. Uni- and multivariate cox analyses showed that both risk scoring systems independently predicted survival even after adjusting for clinical factors. The lncRNAs related to OS may be involved in complement and coagulation cascades, while those related to RFS may be involved in the cell cycle. Conclusion:Risk scoring system based on these lncRNAs may be useful for predicting prognosis of tumor-positive HCC patients.

A four immune-related long noncoding RNAs signature as predictors for cervical cancer.

The progression, metastasis, and prognosis of cervical cancer (CC) is influenced by the tumor immune microenvironment. Studies proved that long non-coding RNAs (lncRNAs) to engage in cervical cancer development, especially immune-related lncRNAs, have emerged crucial in the tumor immune process. This study was set out to identify an immune-related lncRNA signature. In total, 13,838 lncRNA expression profiles and 328 immune genes were acquired from the clnical data of 306 CC tissues and 3 non-CC tissues. From the 433 identified immune-related lncRNAs, 4 candidate immune-related lncRNAs (SOX21-AS1, AC005332.4, NCK1-DT, LINC01871) were considered independent indicators of cervical cancer prognosis through the univariate and multivariate Cox regression analysis, and they were used to construct a prognostic and survival lncRNA signature model followed by the bootstrap method for further verification. Kaplan-Meier curves illustrated that cervical cancer patients could be divided into high-risk and low-risk groups with significant differences (P = 2.052e-05), and the discrepancy of immune profiles between these two risk groups was illustrated by principal components analysis. Taken together, the novel survival predictive model created by the four immune-related lncRNAs showed promising clinical prediction value in cervical cancer.