Identification of Potential Prognostic Competing Triplets in High-Grade Serous Ovarian Cancer

Ferroptosis-related lncRNAs: Distinguishing heterogeneity of the tumour microenvironment and predicting immunotherapy response in bladder cancer

Ferroptosis, a cell death pathway dependent on iron, has been shown in research to play a role in the development, advancement, and outlook of tumours through ferroptosis-related lncRNAs (FRLRs). However, the value of the FRLRs in bladder cancer (BLCA) has not been thoroughly investigated. This research project involved developing a predictive model using ten specific FRLRs (AC099850.4, AL731567.1, AL133415.1, AC021321.1, SPAG5-AS1, HMGA2-AS1, RBMS3-AS3, AC006160.1, AL583785.1, and AL662844.4) through univariate COX and LASSO regression techniques. The validation of this signature as a standalone predictor was confirmed in a group of 65 patients from the urology bladder tumour database at the First Affiliated Hospital of Wenzhou Medical University in Wenzhou, China. Patients were categorized based on their median risk score into either a low-risk group or a high-risk group. Enrichment analysis identified possible molecular mechanisms that could explain the variations in clinical outcomes observed in high-risk and low-risk groups. Moreover, we explored the correlation between FLPS and immunotherapy-related indicators. The ability of FLPS to forecast the effectiveness of immunotherapy was validated by the elevated levels of immune checkpoint genes (PD-L1, CTLA4, and PD-1) in the group at high risk. We also screened the crucial FRLR (HMGA2-AS1) through congruent expression and prognostic conditions and established a ceRNA network, indicating that HMGA2-AS1 may affect epithelial-mesenchymal transition by modulating the Wnt signalling pathway through the ceRNA mechanism. We identified the top five mRNAs (NFIB, NEGR1, JAZF1, JCAD, and ESM1) based on random forest algorithm and analysed the relationship between HMGA2-AS1, the top five mRNAs, and immunotherapy, and their interactions with drug sensitivities. Our results suggest that patients with BLCA have a greater sensitivity to four drugs (dasatinib, pazopanib, erismodegib and olaparib). Our study provides new insights into the TME, key signalling pathways, genome, and potential therapeutic targets of BLCA, with future guidance for immunotherapy and targeted precision drugs.

miR-3653-3p Expression in PBMCs: Unveiling the Diagnostic Potential for Ovarian Cancer

Ovarian cancer is typically diagnosed at an advanced stage, recurs early and often, and currently lacks effective treatment. Therefore, overall survival and progression-free survival are relatively short for this disease. Sensitive and specific biomarkers for early diagnosis and follow-up for effective treatment of the disease are currently lacking. MicroRNA (miRNA/miR) expression studies are widely used in cancer research. Disruption or malfunction of miRNAs, a class of noncoding small RNAs, has been implicated in cancer progression in several publications. Of note, the expression of a series of miRNAs is known to differ in ovarian cancer. In cancer research, it is crucial to analyze expression patterns in both cancer patients and healthy individuals to identify cancer-specific biological markers and to understand their role in cancer. In the present study, the expression levels of miR-3653-3p in the peripheral blood mononuclear cells (PBMCs) of 150 patients with high-risk ovarian cancer were determined, including those with a family history of cancer or an early-age diagnosis of ovarian cancer, as well as 100 healthy individuals. The results were then compared between the two groups. The expression level of miR-3653-3p in the PBMCs of patients with ovarian cancer was determined to be 9.49-fold higher than that in the healthy control group, and this result was statistically significant (P < 0.001). In addition, receiver-operating characteristic curve analysis of PBMC showed statistical significance of miR-3653-3p in discriminating ovarian cancer patients from healthy subjects (P < 0.001). These results suggest that miR-3653-3p detected in peripheral blood may be used as a non-invasive biomarker for ovarian cancer.

Comprehensive analysis identifies long non-coding RNA RNASEH1-AS1 as a potential prognostic biomarker and oncogenic target in hepatocellular carcinoma

RNASEH1-AS1, a long non-coding RNA (lncRNA) divergently transcribed from the antisense strand of its neighboring protein-coding gene ribonuclease H1 (RNASEH1), has recently been demonstrated to be involved in tumor progression. However, the association between RNASEH1-AS1 and hepatocellular carcinoma (HCC) remains unclear. In the present study, first, the expression of RNASEH1-AS1 in HCC and its correlation with clinicopathological features, prognosis, diagnosis, immune cell infiltration of HCC patients was inspected using relevant R packages based on The Cancer Genome Atlas (TCGA) data. RNASEH1-AS1 was found to be up-regulated in most cancer types, including HCC, and its overexpression was significantly associated with histologic grade and AFP level as well as poor prognosis, and was an independent risk factor affecting overall survival with good diagnostic and prognostic values for HCC. RNASEH1-AS1 was inversely associated with the infiltration of most immune cell types, including plasmacytoid dendritic cells (pDC), B cells and neutrophils. Second, a total of 1109 positively co-expressed genes (PCEGs) of RNASEH1-AS1 were screened out in HCC by correlation analysis in batches (|Spearman's r| >0.4 and adjusted P value <0.01). GO and KEGG enrichment analysis indicated that PCEGs of RNASEH1-AS1 were mainly related to RNA processing, ribosome biogenesis, transcription and histone acetylation. The top 10 hub genes (EIF4A3, WDR43, WDR12, DKC1, NAT10, UTP18, DDX18, BYSL, DDX10, PDCD11) were identified by constructing the protein-protein interaction (PPI) network, and they were all highly expressed in HCC and positively correlated with histological grade. Third, a risk model was constructed based on four RNASEH1-AS1-related hub genes (EIF4A3, WDR12, DKC1, and NAT10) with good prognostic predictive potential via univariate Cox and the least absolute selection operator (LASSO) regression analysis. Fourth, experimental validation revealed that RNASEH1-AS1 was significantly elevated in HCC tissues and several cell lines, and its knockdown could suppress the proliferation, migration, and invasion of HCC cells. Finally, mechanistic studies demonstrated that the stability of RNASEH1-AS1 could be regulated by DKC1 via their direct interaction. Taken together, RNASEH1-AS1 may serve as a potential prognostic and diagnostic biomarker and oncogenic lncRNA for HCC.

Construction and validation of cuproptosis-related lncRNA prediction signature for bladder cancer and immune infiltration analysis

Bladder cancer (BC) is a common urologic tumor with a high recurrence rate. Cuproptosis and long noncoding RNAs (lncRNAs) have demonstrated essential roles in the tumorigenesis of many malignancies. Nevertheless, the prognostic value of cuproptosis-related lncRNA (CRLs) in BC is still unclear. The public data used for this study were acquired from the Cancer Genome Atlas database. A comprehensive exploration of the expression profile, mutation, co-expression, and enrichment analyses of cuproptosis-related genes was performed. A total of 466 CRLs were identified using Pearson's correlation analysis. 16 prognostic CRLs were then retained by univariate Cox regression. Unsupervised clustering divided the patients into two clusters with diverse survival outcomes. The signature consists of 7 CRLs was constructed using the least absolute shrinkage and selection operator (LASSO) Cox regression analyses. Survival curves and receiver operating characteristics showed the prognostic signature possessed good predictive value, which was validated in the testing and entire sets. The reliability and stability of our signature were further confirmed by stratified analysis. Additionally, the signature-based risk score was confirmed as an independent prognostic factor. Gene set enrichment analysis showed molecular alteration in the high-risk group was closely associated with cancer. We then developed the clinical nomogram using independent prognostic indicators. Notably, the infiltration of immune cells and expression of immune checkpoints were higher in the high-risk group, suggesting that they may benefit more from immunotherapy. In summary, the prognostic signature might effectively predict the prognosis and provide new insight into the clinical treatment of BC 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.

Prediction of prognosis, immune infiltration and immunotherapy response with N6-methyladenosine-related lncRNA clustering patterns in cervical cancer

LncRNAs and tumor microenvironment (TME) exert an important effect in antitumor immunity. Nonetheless, the role of m⁶A-related lncRNA clustering patterns in prognosis, TME and immunotherapy of cervical cancer (CC) remains unknown. Here, based on 7 m⁶A-related prognostic lncRNAs obtained from TCGA-CC dataset, two m⁶AlncRNA clustering patterns were determined. m⁶AlncRNA clusterA was characterized by immune cell infiltrates and immune activation. m⁶AlncRNA clusterB was characterized by enrichment of immune evasion and tumorigenic activation pathways as well as survival and clinical stage disadvantage. Then, principal component analysis algorithms were used to construct m⁶AlncRNAscore based on prognostic differentially expressed genes between two m⁶AlncRNA clusters to quantify m⁶AlncRNA clustering patterns. m⁶AlncRNAscore was an independent prognostic protective factor. Higher Th2 and Treg cells and enrichment of immunosuppressive pathways were observed in the low-m⁶AlncRNAscore group, with poorer survival. High-m⁶AlncRNAscore was characterized by increased infiltration of activated CD8 T cell, enrichment of immune activation pathways, lower IL-10 and TGF-beta1 levels, and higher immunophenscore values, indicating inflamed TME and better anti-tumor immunotherapy efficacy. Quantitative Real-Time Polymerase Chain Reaction was used for detection of m⁶A-related prognostic lncRNAs. Collectively, we identified two m⁶AlncRNA clustering patterns which play a nonnegligible role in the prognosis, TME heterogeneity and immunotherapy of CC patients.

Development and Validation of a Novel Mitochondrion and Ferroptosis-Related Long Non-Coding RNA Prognostic Signature in Hepatocellular Carcinoma

Mitochondrion and ferroptosis are related to tumorigenesis and tumor progression of hepatocellular carcinoma (HCC). Therefore, this study focused on exploring the participation of lncRNAs in mitochondrial dysfunction and ferroptosis using public datasets from The Cancer Genome Atlas (TCGA) database. We identified the mitochondrion- and ferroptosis-related lncRNAs by Pearson’s analysis and lasso-Cox regression. Moreover, real-time quantitative reverse transcription PCR (RT-qPCR) was utilized to further confirm the abnormal expression of these lncRNAs. Based on eight lncRNAs, the MF-related lncRNA prognostic signature (LPS) with outstanding stratification ability and prognostic prediction capability was constructed. In addition, functional enrichment analysis and immune cell infiltration analysis were performed to explore the possible functions of lncRNAs and their impact on the tumor microenvironment. The pathways related to G2M checkpoint and MYC were activated, and the infiltration ratio of regulatory T cells and M0 and M2 macrophages was higher in the high-risk group. In conclusion, these lncRNAs may affect mitochondria functions, ferroptosis, and immune cell infiltration in HCC through specific pathways, which may provide valuable insight into the progression and therapies of HCC.

N7-methylguanosine-related lncRNAs: Predicting the prognosis and diagnosis of colorectal cancer in the cold and hot tumors

Background: 7-Methylguanosine(m7G) contributes greatly to its pathogenesis and progression in colorectal cancer. We proposed building a prognostic model of m7G-related LncRNAs. Our prognostic model was used to identify differences between hot and cold tumors.

Methods: The study included 647 colorectal cancer patients (51 cancer-free patients and 647 cancer patients) from The Cancer Genome Atlas (TCGA). We identified m7G-related prognostic lncRNAs by employing the univariate Cox regression method. Assessments were conducted using univariate Cox regression, multivariate Cox regression, receiver operating characteristics (ROC), nomogram, calibration curves, and Kaplan-Meier analysis. All of these procedures were used with the aim of confirming the validity and stability of the model. Besides these two analyses, we also conducted half-maximal inhibitory concentration (IC50), immune analysis, principal component analysis (PCA), and gene set enrichment analysis (GSEA). The entire set of m7G-related (lncRNAs) with respect to cold and hot tumors has been divided into two clusters for further discussion of immunotherapy.

Results: The risk model was constructed with 17 m7G-related lncRNAs. A good correlation was found between the calibration plots and the prognosis prediction in the model. By assessing IC50 in a significant way across risk groups, systemic treatment can be guided. By using clusters, it may be possible to distinguish hot and cold tumors effectively and to aid in specific therapeutic interventions. Cluster 1 was identified as having the highest response to immunotherapy drugs and thus was identified as the hot tumor.

Conclusion: This study shows that 17 m7G-related lncRNA can be used in clinical settings to predict prognosis and use them to determine whether a tumor is cold or hot in colorectal cancer and improve the individualization of treatment.

Construction of a Metabolism-Related Long Non-Coding RNAs-Based Risk Score Model of Hepatocellular Carcinoma for Prognosis and Personalized Treatment Prediction

Background: Long non-coding RNAs (lncRNAs) play a key regulatory role in tumor metabolism. Although hepatocellular carcinoma (HCC) is a metabolic disease, there have been few systematic reports on the association between lncRNA expression and metabolism in HCC. Results: In this study, we screened 557 metabolism-related lncRNAs in HCC. A risk score model based on 13 metabolism-related lncRNA pairs was constructed to predict the outcome and drug response in HCC. The risk score model presented a better prediction of the outcomes than that with common clinicopathological characteristics, such as tumor stage, grade, and status and aneuploidy score in both training and testing cohorts. In addition, patients in the high-risk group exhibited higher responses to gemcitabine and epothilone, whereas those in the low-risk group were more sensitive to metformin and nilotinib. Conclusion: The metabolism-related lncRNAs-based risk score model and the other findings of this study may be helpful for HCC prognosis and personalized treatment prediction.

The role of long non-coding RNA FGD5-AS1 in cancer

Long noncoding RNAs (lncRNAs) refers to a class of RNAs that have at least 200 nucleotides and do not encode proteins, and the relationship between lncRNA and cancer has recently attracted considerable research attention. The lncRNA FGD5-AS1 is a newly discovered lncRNA with a length of 3772 nucleotides. Studies have found that FGD5-AS1 is abnormally highly expressed in many cancer tissues and was closely related to the lymph node metastasis, tumor invasion, survival time, and recurrence rate of various cancers. Mechanistic analyses show that FGD5-AS1 can stabilize mRNA expression by sponging miRNA, which not only induces cancer cell proliferation, metastasis, invasion, and chemoresistance in vitro, but also promotes tumor growth and metastasis in vivo. In addition, FGD5-AS1 can serve as a diagnostic or prognostic marker for a variety of cancers. This review demonstrates the clinical significance of FGD5-AS1 in human cancer and its role in tumorigenesis and tumor progression.

m6A-Related lncRNAs Are Potential Prognostic Biomarkers of Cervical Cancer and Affect Immune Infiltration

The correlation of m⁶A-related lncRNAs with the prognosis and immune microenvironment of cervical cancer is not yet clear. In this study, we identified 7 m⁶A-related prognostic lncRNAs by Pearson correlation and univariate Cox regression analyses based on TCGA-cervical cancer dataset. Then, patients were divided into two clusters by consensus clustering based on the 7 m⁶A-related prognostic lncRNA expression. Cluster 1 was characterized by survival and stage disadvantage, enrichment of immunosuppressive and carcinogenic activation pathways. Besides, cluster 1 had higher immunosuppressive factor TGFbeta and lower immune cell infiltration compared with cluster 2. According to the expression of 7 m⁶A-related lncRNA, a 6-m⁶A-related lncRNA risk score model was established in the training set by LASSO regression analysis. The high-risk group had worse overall survival than the low-risk group. No matter in the training or validation sets, the m⁶A-related lncRNA risk score was an independent prognostic factor for overall survival. Meanwhile, we validated the independent prognostic value of risk score in the disease-specific survival and progression-free survival by multivariate Cox analysis. The high-risk group was characterized by higher TGFbeta and regulatory T cell and was rich in malignant pathways. Additionally, we also detected and compared the expression levels of four m⁶A-related prognostic lncRNA in 9 tumor samples and 9 normal tissues using quantitative real-time polymerase chain reaction assay. In conclusion, the novel m⁶A-related lncRNA risk score is a potential prognostic predictor of cervical cancer patients. These 6 m⁶A-related lncRNAs might serve as key mediators of the immune microenvironment and represent promising therapeutic targets for improving cervical cancer prognosis.

A Novel Ferroptosis-Related lncRNA Signature for Prognosis Prediction in Patients with Papillary Renal Cell Carcinoma

Purpose

Papillary renal cell carcinoma (PRCC) is a common renal cell carcinoma. Recent studies have reported that ferroptosis is involved in the occurrence and development of tumors. Long non-coding RNAs can be used as independent biomarkers for the diagnosis and prognosis of a variety of tumors.

Methods

Gene expression profile and clinical information of patients with PRCC were obtained from The Cancer Genome Atlas (TCGA) database. Lasso penalized Cox regression and univariate Cox regression analysis were utilized for model construction. The Kaplan–Meier (K-M) and receiver operating characteristic (ROC) curves were plotted to validate the predictive effect of the prognostic signature. Immune cell infiltration and immune function were compared between the high-risk and low-risk groups. Chemotherapy sensitivity analysis was also performed.

Results

We constructed a prognostic signature consisting of 15 ferroptosis-related lncRNAs. The K-M curves validated the fine predictive accuracy of the prognostic signature (p < 0.001). The area under the curve (AUC) of the lncRNA signature was 0.930, exhibiting robust prognostic capacity. The high-risk group had a greater degree of immune cell infiltration than the low-risk group. Significant differences in inflammation promotion, parainflammation, and type I IFN response were noted between the low-risk and high-risk groups (p < 0.01). The expression levels of immune checkpoints including CD80, IDO1, and LAG3 were significantly higher in the high-risk group than in the low-risk group (p < 0.05). Chemotherapy sensitivity analysis showed that MNX1-AS1, ZFAS1, MIR4435-2HG, and ADAMTS9-AS1 were significantly correlated with the sensitivity of some chemotherapy drugs (p < 0.05).

Conclusion

We demonstrated that a ferroptosis-related lncRNA prognostic signature could be a novel biomarker for PRCC.

The Immunological Role of CDK4/6 and Potential Mechanism Exploration in Ovarian Cancer

Background

Ovarian cancer (OC) is one of the most lethal gynecologic cancers. Growing evidence has proven that CDK4/6 plays a key role in tumor immunity and the prognosis of many cancers. However, the expression and function of CDK4/6 in OC remain unclear. Therefore, we aimed to explore the influence of CDK4/6 in OC, especially on immunity.

Methods

We analyzed CDK4/6 expression and prognosis using The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and Genotype Tissue Expression (GTEx) data. Subsequently, we used the cytoHubba plug-in of Cytoscape software and starBase to identify the noncoding RNAs (ncRNAs) regulating CDK4/6. Finally, we verified the effect of CDK4/6 on immunity in OC cell lines and animal models.

Results

CDK4/6 expression was higher in OC tissues than in normal ovarian tissues, and the high expression levels of CDK4/6 contributed to the immunosuppressive state of OC and were thus related to the poor prognosis of OC patients. This was also in general agreement with the results of OC cell line and animal experiments. Mechanistically, the CDK4/6 inhibitor palbociclib increased the secretion of interferon (IFN)-γ and the interferon-stimulated gene (ISG) response, thereby upregulating the expression of antigen-presenting molecules; this effect was partly dependent on the STING pathway and thus activated immunity in OC. Additionally, according to public data, the LRRC75A-AS1-hsa-miR-330-5p axis could inhibit the immune response of OC patients by upregulating CDK4/6, leading to a poor prognosis.

Conclusion

CDK4/6 affects the immune microenvironment of OC and correlates with the prognosis of OC patients.

Use of 6 m6A-relevant lncRNA genes as prognostic markers of primary liver hepatocellular carcinoma based on TCGA database

BACKGROUND

Hepatocellular carcinoma (HCC) is diagnosed at the middle and advanced stages, negating radical treatment. Identifying specific and effective prognostic HCC biomarkers is important and can facilitate the discovery of potential therapeutic targets. N6-methyladenosine (m6A) and long non-coding RNAs (lncRNAs) are associated with the development of multiple tumors. The role of m6A-relevant lncRNAs in the initiation and progression of HCC is unclear. The aim of the present study was to investigate the expression of m6A-relevant lncRNAs in HCC and to identify new prognostic markers of the disease.

METHODS

Gene expression and clinical data were retrieved from The Cancer Genome Atlas database. m6A-relevant lncRNAs were identified by co-expression analysis and were screened by univariate Cox regression analysis. Different HCC patient clusters were established via consensus clustering. Gene set enrichment analysis (GSEA) was used to determine the cluster enrichment pathways. A risk score model was constructed, and Kaplan-Meier analysis of the overall survival (OS) between cluster 1 (high risk) and cluster 2 (low risk) was performed. Relationships between the clusters, risk scores, and clinicopathological characteristics were clarified.

RESULTS

Of the 1,852 m6A-relevant lncRNAs identified, 68 had prognostic relevance. The pathological grade, American Joint Committee on Cancer stage, and T stage of cluster 1 were significantly more advanced than those of cluster 2. Based on GSEA, mitotic spindle, G2M_CHECKPOINT, glycolysis, the phosphoinositide 3-kinase (PI3K) protein kinase B (AKT) mammalian target of rapamycin (mTOR) pathway, and DNA repair were more enriched in cluster 1. Six key m6A-relevant lncRNAs were selected to build a risk score model predicting the prognosis of HCC. The OS of patients in the high-risk group was shorter than that of patients in the low-risk group. Risk score was an independent prognostic factor of HCC patients.

CONCLUSIONS

The findings indicated that m6A-relevant lncRNAs may be important in the progression of HCC. The risk score model based on the 6 key m6A-relevant lncRNAs can accurately predict the prognosis of patients with HCC.

Identification of novel biomarkers for intracerebral hemorrhage via long noncoding RNA-associated competing endogenous RNA network

Intracerebral hemorrhage (ICH) is a leading cause of death and disability worldwide. This study aimed to examine the involvement of long non-coding RNAs (lncRNAs), a group of non-coding transcripts, in ICH as potential biomarkers. An expression profile of patients with ICH using four contralateral grey matter controls (GM) and four contralateral white matter controls (WM) was downloaded from the Gene Expression Omnibus (GEO) database. Co-expressed lncRNAs and mRNAs were selected to create competing endogenous RNA (ceRNA) networks. Key lncRNAs were identified in ceRNA networks, which were validated through Real-time qPCR (RT-qPCR) with peripheral blood samples from patients with ICH. A total of 49 differentially expressed lncRNAs were discovered in different brain regions. The ceRNA network in GM included 9 lncRNAs, 40 mRNAs, and 20 microRNAs (miRNAs), while the one in WM covered 6 lncRNAs, 25 mRNAs, and 14 miRNAs. Six hub lncRNAs were observed and RT-qPCR results showed that LY86-AS1, DLX6-AS1, RRN3P2, and CRNDE were down-regulated, while HCP5 and MIAT were up-regulated in patients with ICH. Receiver Operating Characteristic (ROC) assessments demonstrated the diagnostic value of these lncRNAs. Our findings highlight the potential roles of lncRNA in ICH pathogenesis. Moreover, the hub lncRNAs discovered here might become novel biomarkers and promising targets for ICH drug development.

Identification and Validation of Hypoxia-Related lncRNA Signature as a Prognostic Model for Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most general malignant tumors. Hypoxia is a critical clinical characteristic and acts as a significant part in the development and cancers’ prognosis. The prognostic value and biological functions of hypoxia-related lncRNAs in hepatocellular carcinoma is little known. Thus, we aim to establish a hypoxia-related lncRNA signature to predict the HCC patients’ survival. First, we extracted the hypoxia-related genes and expression of lncRNAs from the MSigDB and TCGA database, respectively. The co-expression analysis among hypoxia-related mRNAs and lncRNAs was employed to identify hypoxia-related lncRNAs. Then, comprehensive analyses of lncRNAs expression level and survival data were applied to establish the signature. We built a prognostic signature on the foundation of the three differently expressed hypoxia-related lncRNAs. Kaplan-Meier curves indicated the low-risk group is associated with better survival. The 1−, 3−, and 5 years AUC values of the signature were 0.805, 0.672 and 0.63 respectively. The test set performed consistent outcomes. A nomogram was built grounded on the risk score and clinicopathological features. GSEA showed the immune-related pathways in high-risk group, while metabolism-related pathways in low-risk group. Besides, we found this model was correlated with the clinical features, tumor immune cell infiltration, immune checkpoints, and m6A-related genes. Finally, a novel signature based on hypoxia-related lncRNAs was established and validated for predicting HCC patients’ survival and may offer some useful information for immunotherapies.