A Prognostic Model Based on Immune-Related Long Non-Coding RNAs for Patients With Cervical Cancer

Exosomal lncRNA USP30-AS1 activates the Wnt/β-catenin signaling pathway to promote cervical cancer progression via stabilization of β-catenin by USP30

Cervical cancer (CC) remains a major cause of cancer-related mortality among women globally. Long noncoding RNAs (lncRNAs) play crucial regulatory roles in various cancers, including CC. This study investigates the function of a novel lncRNA, USP30 antisense RNA 1 (USP30-AS1), in CC tumorigenesis. We analyzed USP30-AS1 expression using RT-qPCR and conducted in vitro loss-of-function assays, as well as in vivo assays, to evaluate the effects of USP30-AS1 silencing on CC cell growth and migration. Additional mechanistic experiments, including RNA pull-down, RNA immunoprecipitation (RIP), and co-immunoprecipitation (Co-IP) assays, were performed to elucidate the regulatory mechanisms influenced by USP30-AS1. We discovered that USP30-AS1 is overexpressed in CC tissues and cells. Silencing USP30-AS1 significantly reduced cell proliferation, migration, invasion, and tumor growth. Moreover, USP30-AS1 was found to modulate the expression of ubiquitin-specific peptidase 30 (USP30) by sponging microRNA-2467-3p (miR-2467-3p) and recruiting the FUS RNA binding protein (FUS), thereby stabilizing β-catenin and activating the Wnt/β-catenin signaling pathway. These findings suggest that USP30-AS1 enhances CC cell growth and migration through the miR-2467-3p/FUS/USP30 axis, highlighting its potential as a biomarker for CC.

The construction of a prognostic model of cervical cancer based on four immune-related LncRNAs and an exploration of the correlations between the model and oxidative stress

Introduction: The immune-related lncRNAs (IRLs) are critical for the development of cervical cancer (CC), but it is still unclear how exactly ILRs contribute to CC. In this study, we aimed to examine the relationship between IRL and CC in detail. Methods: First, the RNAseq data and clinical data of CC patients were collected from The Cancer Genome Atlas (TCGA) database, along with the immune genes from the Import database. We used univariate cox and least absolute shrinkage and selection operator (lasso) to obtain IRLs for prediction after screening the variables. According to the expression levels and risk coefficients of IRLs, the riskscore were calculated. We analyzed the relationship between the model and oxidative stress. We stratified the risk model into two as the high and low-risk groups. We also evaluated the survival differences, immune cell differences, immunotherapeutic response differences, and drug sensitivity differences between the risk groups. Finally, the genes in the model were experimentally validated. Results: Based on the above analyses, we further selected four IRLs (TFAP2A.AS1, AP000911.1, AL133215.2, and LINC02078) to construct the risk model. The model was associated with oxidative-stress-related genes, especially SOD2 and OGG1. Patients in the high-risk group had a lower overall survival than those in the low-risk group. Riskscore was positively correlated with resting mast cells, neutrophils, and CD8+ T-cells. Patients in the low-risk group showed a greater sensitivity to immunosuppression therapy. In addition, we found that patients with the PIK3CA mutation were more sensitive to chemotherapeutic agents such as dasatinib, afatinib, dinaciclib and pelitinib. The function of AL133215.2 was verified, which was consistent with previous findings, and AL133215.2 exerted a pro-tumorigenic effect. We also found that AL133215.2 was closely associated with oxidative-stress-related pathways. Discussion: The results suggested that risk modeling might be useful for prognosticating patients with CC and opening up new routes for immunotherapy.

New algorithms based on autophagy-related lncRNAs pairs to predict the prognosis of skin cutaneous melanoma patients

Skin cutaneous melanoma (SKCM) is the most malignant skin tumor for it is enormously easy to develop invasion and metastasis. Autophagy is a process by which cellular material is degraded by lysosomes or vacuoles and recycled. Autophagy-related long non-coding RNAs (lncRNAs) have been thought to correlate with SKCM. This study aims to explore the prognostic significance of autophagy-related lncRNAs and establish a prognostic model of autophagy-related lncRNA pairs in SKCM. Firstly, the RNA-seq data and related clinical information were downloaded from the TCGA database. 446 qualified samples were enrolled. 222 autophagy-related genes were obtained from the HADb database. Pearson correlation analysis was conducted to identify autophagy-related lncRNAs (ARLs). After that, we obtained prognosis-related ARLs and autophagy-related lncRNA pairs (ARLPs). Using Lasso-Cox regression analysis, an autophagy-related lncRNA-pair prognostic signature was established. The accuracy of the signature were confirmed through a series of validations in terms of mutation profiles, immunity infiltration, and cellular pathways. And we used the random forest method to find USP30-AS1 as a key mediating factor in SKCM.

Identification of cuproptosis-related lncRNA for predicting prognosis and immunotherapeutic response in cervical cancer

Patients diagnosed with advanced cervical cancer (CC) have poor prognosis after primary treatment, and there is a lack of biomarkers for predicting patients with an increased risk of recurrence of CC. Cuproptosis is reported to play a role in tumorigenesis and progression. However, the clinical impacts of cuproptosis-related lncRNAs (CRLs) in CC remain largely unclear. Our study attempted to identify new potential biomarkers to predict prognosis and response to immunotherapy with the aim of improving this situation. The transcriptome data, MAF files, and clinical information for CC cases were obtained from the cancer genome atlas, and Pearson correlation analysis was utilized to identify CRLs. In total, 304 eligible patients with CC were randomly assigned to training and test groups. LASSO regression and multivariate Cox regression were performed to construct a cervical cancer prognostic signature based on cuproptosis-related lncRNAs. Afterwards, we generated Kaplan-Meier curves, receiver operating characteristic curves and nomograms to verify the ability to predict prognosis of patients with CC. Genes for assessing differential expression among risk subgroups were also evaluated by functional enrichment analysis. Immune cell infiltration and the tumour mutation burden were analysed to explore the underlying mechanisms of the signature. Furthermore, the potential value of the prognostic signature to predict response to immunotherapy and sensitivity to chemotherapy drugs was examined. In our study, a risk signature containing eight cuproptosis-related lncRNAs (AL441992.1, SOX21-AS1, AC011468.3, AC012306.2, FZD4-DT, AP001922.5, RUSC1-AS1, AP001453.2) to predict the survival outcome of CC patients was developed, and the reliability of the risk signature was appraised. Cox regression analyses indicated that the comprehensive risk score is an independent prognostic factor. Moreover, significant differences were found in progression-free survival, immune cell infiltration, therapeutic response to immune checkpoint inhibitors, and IC50 for chemotherapeutic agents between risk subgroups, suggesting that our model can be well employed to assess the clinical efficacy of immunotherapy and chemotherapy. Based on our 8-CRLs risk signature, we were able to independently assess the outcome and response to immunotherapy of CC patients, and this signature might benefit clinical decision-making for individualized treatment.

T2DB: A Web Database for Long Non-Coding RNA Genes in Type II Diabetes

Type II diabetes (T2D) is a growing health problem worldwide due to increased levels of obesity and can lead to other life-threatening diseases, such as cardiovascular and kidney diseases. As the number of individuals diagnosed with T2D rises, there is an urgent need to understand the pathogenesis of the disease in order to prevent further harm to the body caused by elevated blood glucose levels. Recent advances in long non-coding RNA (lncRNA) research may provide insights into the pathogenesis of T2D. Although lncRNAs can be readily detected in RNA sequencing (RNA-seq) data, most published datasets of T2D patients compared to healthy donors focus only on protein-coding genes, leaving lncRNAs to be undiscovered and understudied. To address this knowledge gap, we performed a secondary analysis of published RNA-seq data of T2D patients and of patients with related health complications to systematically analyze the expression changes of lncRNA genes in relation to the protein-coding genes. Since immune cells play important roles in T2D, we conducted loss-of-function experiments to provide functional data on the T2D-related lncRNA USP30-AS1, using an in vitro model of pro-inflammatory macrophage activation. To facilitate lncRNA research in T2D, we developed a web application, T2DB, to provide a one-stop-shop for expression profiling of protein-coding and lncRNA genes in T2D patients compared to healthy donors or subjects without T2D.

Long non-coding RNA signature in colorectal cancer: research progression and clinical application

Colorectal cancer is one of the top-ranked human malignancies. The development and progression of colorectal cancer are associated with aberrant expression of multiple coding and non-coding genes. Long non-coding RNAs (lncRNAs) have an important role in regulating gene stability as well as gene expression. Numerous current studies have shown that lncRNAs are promising biomarkers and therapeutic targets for colorectal cancer. In this review, we have searched the available literature to list lncRNAs involved in the pathogenesis and regulation of colorectal cancer. We focus on the role of lncRNAs in cancer promotion or suppression, their value in tumor diagnosis, and their role in treatment response and prognosis prediction. In addition, we will discuss the signaling pathways that these lncRNAs are mainly associated with in colorectal cancer. We also summarize the role of lncRNAs in colorectal precancerous lesions and colorectal cancer consensus molecular subgroups. We hope this review article will bring you the latest research progress and outlook on lncRNAs in colorectal cancer.

Significance of platelet adhesion-related genes in colon cancer based on non-negative matrix factorization-based clustering algorithm

Background

Although surgical methods are the most effective treatments for colon adenocarcinoma (COAD), the cure rates remain low, and recurrence rates remain high. Furthermore, platelet adhesion-related genes are gaining attention as potential regulators of tumorigenesis. Therefore, identifying the mechanisms responsible for the regulation of these genes in patients with COAD has become important. The present study aims to investigate the underlying mechanisms of platelet adhesion-related genes in COAD patients.

Methods

The present study was an experimental study. Initially, the effects of platelet number and related genomic alteration on survival were explored using real-world data and the cBioPortal database, respectively. Then, the differentially expressed platelet adhesion-related genes of COAD were analyzed using the TCGA database, and patients were further classified by employing the non-negative matrix factorization (NMF) analysis method. Afterward, some of the clinical and expression characteristics were analyzed between clusters. Finally, least absolute shrinkage and selection operator regression analysis was used to establish the prognostic nomogram. All data analyses were performed using the R package.

Results

High platelet counts are associated with worse survival in real-world patients, and alternations to platelet adhesion-related genes have resulted in poorer prognoses, based on online data. Based on platelet adhesion-related genes, patients with COAD were classified into two clusters by NMF-based clustering analysis. Cluster2 had a better overall survival, when compared to Cluster1. The gene copy number and enrichment analysis results revealed that two pathways were differentially enriched. In addition, the differentially expressed genes between these two clusters were enriched for POU6F1 in the transcription factor signaling pathway, and for MATN3 in the ceRNA network. Finally, a prognostic nomogram, which included the ALOX12 and ACTG1 genes, was established based on the platelet adhesion-related genes, with a concordance (C) index of 0.879 (0.848-0.910).

Conclusion

The mRNA expression-based NMF was used to reveal the potential role of platelet adhesion-related genes in COAD. The series of experiments revealed the feasibility of targeting platelet adhesion-associated gene therapy.

A prognostic risk prediction model based on ferroptosis-related long non-coding RNAs in bladder cancer: A bulk RNA-seq research and scRNA-seq validation

BACKGROUND

To construct a prognostic risk model of bladder cancer (BC) from the perspective of long non-coding RNAs (lncRNAs) and ferroptosis, in order to guide clinical prognosis and identify potential therapeutic targets.

METHODS

In-hours BC samples were collected from 4 patients diagnosed with BC, who underwent radical cystectomy. Single cell transcriptome sequencing was performed and Seurat package were used for quality control and secondary analysis. LncRNAs expression profiles of BC samples were extracted from The Cancer Genome Atlas database. And sex, age, tumor, node, metastasis stage and other clinical data was downloaded at the same time. Ferroptosis-related lncRNAs were identified by co-expression analysis. We constructed a risk model by Cox regression and least absolute shrinkage and selection operator regression analyses. The predictive strength of the risk model for overall survival (OS) of patients with BC was evaluated by the log-rank test and Kaplan-Meier method. Finally, the enrichment analysis was performed and visualized.

RESULTS

We identified and included 15 prognostic ferroptosis-related lncRNAs (AL356740.1, FOXC2AS1, ZNF528AS1, LINC02535, PSMB8AS1, AL590428.1, AP000347.2, OCIAD1-AS1, AP001347.1, AC104986.2, AC018926.2, LINC00867, AC099518.4, USP30-AS1, and ARHGAP5-AS1), to build our ferroptosis-related lncRNAs risk model. Using this risk model, BC patients were divided into high and low-risk groups, and their respective survival lengths were calculated. The results showed that the OS of the low-risk group was significantly longer than that of the high-risk group. A nomogram was utilized to predict the survival rate of BC patients. As indicated in the nomogram, risk score was the most important indicator of OS in patients with BC. The ferroptosis-related lncRNAs risk model is an independent tool for prognostic risk assessment in patients with BC. Single cell transcriptome sequencing suggests that ferroptosis-related lncRNAs express specifically in BC tumor microenvironment. AL356740.1, LINC02535 and LINC00867 were mainly expressed in tumor cells.

CONCLUSION

The risk model based on the ferroptosis-related lncRNAs and the genomic clinico-pathological nomogram could be used to accurately predict the prognosis of patients with BC. The lncRNAs used to build this model might become potential therapeutic targets in the future.

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.

lncRNA USP30-AS1 sponges miR-765 and modulates the progression of colon cancer

Background

The incidence and mortality of colon cancer is increasing recently. It is necessary to identify effective biomarkers for the progression and prognosis of colon cancer. To assess the potential of lncRNA USP30-AS1 (USP30-AS1) in serving as the biomarker of colon cancer and unearth the underlying mechanism.

Methods

There were 123 colon cancer patients enrolled. The expression of USP30-AS1 was evaluated with PCR in tissue and cell samples. The clinical significance of USP30-AS1 was assessed with a series of statistical methods, while the CCK8 and Transwell assay were conducted to estimate its biological effect on the colon cancer cellular processes. In mechanism, the interaction of USP30-AS1 with miR-765 was evaluated with the dual-luciferase reporter assay.

Results

In colon cancer tissues, the USP30-AS1 downregulation and the miR-765 upregulation were observed, and there was a negative correlation between the USP30-AS1 expression level and the miR-765 expression level. The downregulation of USP30-AS1 related to the malignant progression and served as an adverse prognostic indicator of colon cancer. The overexpression of USP30-AS1 dramatically suppressed colon cancer cellular processes, which was alleviated by miR-765.

Conclusions

USP30-AS1 predicts the malignancy and prognosis of colon cancer patients. USP30-AS1 suppressed the progression of colon cancer through modulating miR-765.

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.

Bioinformatic Analyses of the Ferroptosis-Related lncRNAs Signature for Ovarian Cancer

Both ferroptosis and lncRNAs are significant for ovarian cancer (OC). Whereas, the study of ferroptosis-related lncRNAs (FRLs) still few in ovarian cancer. We first constructed an FRL-signature for patients with OC in the study. A total of 548 FRLs were identified for univariate Cox regression analysis, and 21 FRLs with significant prognosis were identified. The prognostic characteristics of nine FRLs was constructed and validated, showing opposite prognosis in two subgroups based on risk scores. The multivariate Cox regression analysis and nomogram further verified the prognostic value of the risk model. By calculating ferroptosis score through ssGSEA, we found that patients with higher risk scores exhibited higher ferroptosis scores, and high ferroptosis score was a risk factor. There were 40 microenvironment cells with significant differences in the two groups, and the difference of Stromal score between the two groups was statistically significant. Six immune checkpoint genes were expressed at different levels in the two groups. In addition, five m6A regulators (FMR1, HNRNPC, METTL16, METTL3, and METTL5) were higher expressed in the low-risk group. GSEA revealed that the risk model was associated with tumor-related pathways and immune-associated pathway. We compared the sensitivity of chemotherapy drugs between the two risk groups. We also explored the co-expression, ceRNA relation, cis and trans interaction of ferroptosis-related genes and lncRNAs, providing a new idea for the regulatory mechanisms of FRLs. Moreover, the nine FRLs were selected for detecting their expression levels in OC cells and tissues.

A prognostic model based on immune-related long noncoding RNAs for patients with epithelial ovarian cancer

BACKGROUND

Long noncoding RNAs (lncRNAs) are important regulators of gene expression and can affect a variety of physiological processes. Recent studies have shown that immune-related lncRNAs play an important role in the tumour immune microenvironment and may have potential application value in the treatment and prognosis prediction of tumour patients. Epithelial ovarian cancer (EOC) is characterized by a high incidence and poor prognosis. However, there are few studies on immune-related lncRNAs in EOC. In this study, we focused on immune-related lncRNAs associated with survival in EOC.

METHODS

We downloaded mRNA data for EOC patients from The Cancer Genome Atlas (TCGA) database and mRNA data for normal ovarian tissue from the Genotype-Tissue Expression (GTEx) database and identified differentially expressed genes through differential expression analysis. Immune-related lncRNAs were obtained through intersection and coexpression analysis of differential genes and immune-related genes from the Immunology Database and Analysis Portal (ImmPort). Samples in the TCGA EOC cohort were randomly divided into a training set, validation set and combination set. In the training set, Cox regression analysis and LASSO regression were performed to construct an immune-related lncRNA signature. Kaplan-Meier survival analysis, time-dependent ROC curve analysis, Cox regression analysis and principal component analysis were performed for verification in the training set, validation set and combination set. Further studies of pathways and immune cell infiltration were conducted through Gene Set Enrichment Analysis (GSEA) and the Timer data portal.

RESULTS

An immune-related lncRNA signature was identified in EOC, which was composed of six immune-related lncRNAs (KRT7-AS, USP30-AS1, AC011445.1, AP005205.2, DNM3OS and AC027348.1). The signature was used to divide patients into high-risk and low-risk groups. The overall survival of the high-risk group was lower than that of the low-risk group and was verified to be robust in both the validation set and the combination set. The signature was confirmed to be an independent prognostic biomarker. Principal component analysis showed the different distribution patterns of high-risk and low-risk groups. This signature may be related to immune cell infiltration (mainly macrophages) and differential expression of immune checkpoint-related molecules (PD-1, PDL1, etc.).

CONCLUSIONS

We identified and established a prognostic signature of immune-related lncRNAs in EOC, which will be of great value in predicting the prognosis of clinical patients and may provide a new perspective for immunological research and individualized treatment in EOC.

Immune-Related Long Non-coding RNA Constructs a Prognostic Signature of Ovarian Cancer

Background

Since ovarian cancer leads to the poor prognosis in women all over the world, we aim to construct an immune-related lncRNAs signature to improve the survival of ovarian cancer patients.

Methods

Normal and cancer patient samples and corresponding clinical data of ovarian were obtained from The Genotype-Tissue Expression (GTEx) portal and The Cancer Genome Atlas (TCGA) database. The predictive signature was constructed by the lasso penalty Cox proportional hazard regression model. The division of different risk groups was accounting for the optimal critical value of the time-dependent Receiver Operating Characteristic (ROC) curve. Finally, we validated and evaluated the application of this prognostic signature based on the clinical factors, chemo-sensitivity and immune status of different risk groups.

Results

The signature was established from 145 DEirlncRNAs and can be shown as an independent prognostic risk factor with accurate prediction on overall survival in ovarian cancer patients. Further analysis on the application of the prognostic signature showed that patients with low-risk had a better sensitivity to chemotherapy and a higher immunogenicity.

Conclusion

We constructed and verified an effective signature based on DEirlncRNA pairs, which could predict the prognosis, drug sensitivity and immune status of ovarian cancer patients and promote the prognostic estimation and individualized treatment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12575-021-00161-9.

Identification of m6A-Related lncRNAs Associated With Prognoses and Immune Responses in Acute Myeloid Leukemia

Background: Acute myeloid leukemia (AML) remains the most common type of hematopoietic malignancy in adults and has an unfavorable outcome. Herein, we aimed to construct an N6-methylandenosine (m6A)-related long noncoding RNAs (lncRNAs) signature to accurately predict the prognosis of patients with AML using the data downloaded from The Cancer Genome Atlas (TCGA) database.

Methods: The RNA-seq and clinical data were obtained from the TCGA AML cohort. First, Pearson correlation analysis was performed to identify the m6A-related lncRNAs. Next, univariate Cox regression analysis was used to determine the candidate lncRNAs with prognostic value. Then, feature selection was carried out by Least absolute shrinkage and selection operator (LASSO) analysis, and seven eligible m6A-related lncRNAs were included to construct the prognostic risk signature. Kaplan–Meier and receiver operating characteristic (ROC) curve analyses were performed to evaluate the predictive capacity of the risk signature both in the training and testing datasets. A nomogram was used to predict 1-year, 2-year, and 3-year overall survival (OS) of AML patients. Next, the expression levels of lncRNAs in the signature were validated in AML samples by qRT-PCR. Functional enrichment analyses were carried out to identify probable biological processes and cellular pathways. The ceRNA network was developed to explore the downstream targets and mechanisms of m6A-related lncRNAs in AML.

Results: Seven m6A-related lncRNAs were identified as a prognostic signature. The low-risk group hold significantly prolonged OS. The nomogram showed excellent accuracy of the signature for predicting 1-year, 2-year and 3-year OS (AUC = 0.769, 0.820, and 0.800, respectively). Moreover, the risk scores were significantly correlated with enrichment in cancer hallmark- and malignancy-related pathways and immunotherapy response in AML patients.

Conclusion: We developed and validated a novel risk signature with m6A-related lncRNAs which could predict prognosis accurately and reflect the immunotherapy response in AML patients.

Using Immune-Related lncRNA Signature for Prognosis and Response to Immunotherapy in Cutaneous Melanoma

Background

Cutaneous melanoma is a highly malignant skin tumor, and most patients have a poor prognosis. In recent years, immunotherapy has assumed an important role in the treatment of advanced cutaneous melanoma, but only a small percentage of patients benefit from immunotherapy. A growing number of studies have demonstrated that the prognosis of patients with cutaneous melanoma is closely related to long non-coding RNA and the tumor immune microenvironment.

Methods

We downloaded RNA expression data and immune-related gene lists of cutaneous melanoma patients separately from The Cancer Genome Atlas database and ImmPort website and identified immune-related lncRNAs by co-expression analysis. The prognostic model was constructed by applying least absolute shrinkage and selection operator regression, and all patients were classified into high- and low-risk groups according to the risk score of the model. We evaluated the differences between the two groups in terms of survival outcomes, immune infiltration, pathway enrichment, chemotherapeutic drug sensitivity and immune checkpoint gene expression to verify the impact of lncRNA signature on clinical prognosis and immunotherapy efficacy.

Results

By correlation analysis and LASSO regression analysis, we constructed an immune-related lncRNA prognostic model based on five lncRNA: HLA-DQB1-AS1, MIR205HG, RP11-643G5.6, USP30-AS1 and RP11-415F23.4. Based on this model, we plotted Kaplan-Meier survival curves and time-dependent ROC curves and analyzed its ability as an independent prognostic factor for cutaneous melanoma in combination with clinicopathological features. The results showed that these lncRNA signature was an independent prognostic factor of cutaneous melanoma with favorable prognostic ability. Our results also show a higher degree of immune infiltration, higher expression of immune checkpoint-associated genes, and better outcome of immunotherapy in the low-risk group of the lncRNA signature.

Conclusion

The 5 immune-related lncRNA signatures constructed in our study can predict the prognosis of cutaneous melanoma and contribute to the selection of immunotherapy.

Bioinformatic identification of genomic instability-associated lncRNAs signatures for improving the clinical outcome of cervical cancer by a prognostic model

The research is executed to analyze the connection between genomic instability-associated long non-coding RNAs (lncRNAs) and the prognosis of cervical cancer patients. We set a prognostic model up and explored different risk groups' features. The clinical datasets and gene expression profiles of 307 patients have been downloaded from The Cancer Genome Atlas database. We established a prognostic model that combined somatic mutation profiles and lncRNA expression profiles in a tumor genome and identified 35 genomic instability-associated lncRNAs in cervical cancer as a case study. We then stratified patients into low-risk and high-risk groups and were further checked in multiple independent patient cohorts. Patients were separated into two sets: the testing set and the training set. The prognostic model was built using three genomic instability-associated lncRNAs (AC107464.2, MIR100HG, and AP001527.2). Patients in the training set were divided into the high-risk group with shorter overall survival and the low-risk group with longer overall survival (p < 0.001); in the meantime, similar comparable results were found in the testing set (p = 0.046), whole set (p < 0.001). There are also significant differences in patients with histological grades, FIGO stages, and different ages (p < 0.05). The prognostic model focused on genomic instability-associated lncRNAs could predict the prognosis of cervical cancer patients, paving the way for further research into the function and resource of lncRNAs, as well as a key approach to customizing individual care decision-making.

Integrative analysis of ceRNA network reveals functional lncRNAs associated with independent recurrent prognosis in colon adenocarcinoma

Background

Long non-coding RNAs (lncRNAs), acting as competing endogenous RNA (ceRNA) have been reported to regulate the expression of targeted genes by sponging miRNA in colon adenocarcinoma (COAD).

Methods

However, their potential implications for recurrence free survival prognosis and functional roles remains largely unclear in COAD. In this study, we downloaded the TCGA dataset (training dataset) and GSE39582 (validation dataset) of COAD patients with prognostic information.

Results

A total of 411 differentially expressed genes (DElncRNAs: 12 downregulated and 43 upregulated), 18 DE miRNAs (9 downregulated and 9 upregulated) and 338 DEmRNAs (113 downregulated and 225 upregulated) were identified in recurrence samples compared with non-recurrence samples with the thresholds of FDR < 0.05 and |log₂FC|> 0.263. Based on six signature lncRNAs (LINC00899, LINC01503, PRKAG2-AS1, RAD21-AS1, SRRM2-AS1 and USP30-AS1), the risk score (RS) system was constructed. Two prognostic clinical features, including pathologic stage and RS model status were screened for building the nomogram survival model. Moreover, a recurrent-specific ceRNA network was successfully constructed with 2 signature lncRNAs, 4 miRNAs and 113 mRNAs. Furthermore, we further manifested that SRRM2-AS1 predicted a poor prognosis in COAD patients. Furthermore, knockdown of SRRM2-AS1 significantly suppressed cell proliferation, migration, invasion and EMT markers in HT-29 and SW1116 cells.

Conclusion

These identified novel lncRNA signature and ceRNA network associated with recurrence prognosis might provide promising therapeutic targets for COAD patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02069-6.

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.

A prognostic model for melanoma patients on the basis of immune-related lncRNAs

The prognosis of melanoma patients is highly variable due to multiple factors conditioning immune response and driving metastatic progression. In this study, we have correlated the expression of immune-related lncRNAs with patient survival, developed a prognostic model, and investigated the characteristics of immune response in the diverse groups. The gene expression profiles and prognostic information of 470 melanoma patients were downloaded from TCGA database. Significantly predictive lncRNAs were identified by multivariate Cox regression analyses, and a prognostic model based on these variables was constructed to predict survival. Kaplan-Meier curves were plotted to estimate overall survival. The predictive accuracy of the model was evaluated by the area under the ROC curve (AUC). Principal component analysis was used to observe the distribution of immune-related genes. CIBERSORT and ESTIMATE were used to evaluate the composition of immune cells and the immune microenvironment. Eight immune-related lncRNAs were determined to be prognostic by multivariate COX regression analysis. The patient scores were calculated and divided into high- and low-risk groups. The model could effectively predict the prognosis in patients of different stages. The AUC of the model is 0.784, which was significantly higher than that of the other variables. There were significant differences in the distribution of immune-related genes between two groups; the immune score and immune function enrichment score were higher in the low risk group.