Immune-Related Long Non-coding RNA Signature and Clinical Nomogram to Evaluate Survival of Patients Suffering Esophageal Squamous Cell Carcinoma

Immune-Related Long Non-Coding RNA Signature Determines Prognosis and Immunotherapeutic Coherence in Esophageal Cancer

Objectives

Aim of this study was to explore the immune-related lncRNAs having prognostic role and establishing risk score model for better prognosis and immunotherapeutic coherence for esophageal cancer (EC) patients.

Methods

To determine the role of immune-related lncRNAs in EC, we analyzed the RNA-seq expression data of 162 EC patients and 11 non-cancerous individuals and their clinically relevant information from the cancer genome atlas (TCGA) database. Bioinformatic and statistical analysis such as Differential expression analysis, co-expression analysis, Kaplan Meier survival analysis, Cox proportional hazards model, ROC analysis of risk model was employed.

Results

Utilizing a cutoff criterion (log2FC > 1 + log2FC < -1 and FDR < 0.01), we identified 3737 RNAs were significantly differentially expressed in EC patients. Among these, 2222 genes were classified as significantly differentially expressed mRNAs (demRNAs), and 966 were significantly differentially expressed lncRNAs (delncRNA). Through Pearson correlation analysis between differentially expressed lncRNAs and immune related-mRNAs, we identified 12 immune-related lncRNAs as prognostic signatures for EC. Notably, through Kaplan-Meier analysis on these lncRNAs, we found the low-risk group patients showed significantly improved survival compared to the high-risk group. Moreover, this prognostic signature has consistent performance across training, testing and entire validation cohort sets. Using ESTIMATE and CIBERSORT algorithm we further observed significant enriched infiltration of naive B cells, regulatory T cells resting CD4+ memory T cells, and, plasma cells in the low-risk group compared to high-risk EC patients group. On the contrary, tumor-associated M2 macrophages were highly enriched in high-risk patients. Additionally, we confirmed immune-related biological functions and pathways such as inflammatory, cytokines, chemokines response and natural killer cell-mediated cytotoxicity, toll-like receptor signaling pathways, JAK-STAT signaling pathways, chemokine signaling pathways significantly associated with identified IRlncRNA signature and their co-expressed immune genes. Furthermore, we assessed the predictive potential of the lncRNA signature in immune checkpoint inhibitors; we found that programed cell death ligand 1 (PD-L1; P-value = .048), programed cell death ligand 2 (PD-L2; P-value = .002), and T cell immunoglobulin and mucin-domain containing-3 (TIM-3; P-value = .045) expression levels were significantly higher in low-risk patients compared to high-risk patients.

Conclusion

We believe this study will contribute to better prognosis prediction and targeted treatment of EC in the future.

Identification of tumor antigens for mRNA vaccines and ferroptosis-related landscape in esophageal squamous cell carcinoma

Background

Ferroptosis, an iron-dependent form of cell death that is characterized by lipid peroxidation, has been implicated in conferring resistance to cancer therapies and may contribute to the pathogenesis of esophageal squamous cell carcinoma (ESCC). Furthermore, messenger RNA (mRNA) vaccines have emerged as a promising modality in the treatment arsenal against diverse malignancies. The aim of the study was to investigate the role of ferroptosis subtypes in ESCC and the immune microenvironment, as well as to identify key genes that could serve as targets for mRNA vaccine development.

Methods

Gene expression profiles and clinical data from 79 and 358 ESCC patients were collected from The Cancer Genome Atlas and Gene Expression Omnibus databases. Subsequently, we identified tumor mutational burden (TMB), immune microenvironment scores, and immune checkpoint and immune cell dysfunction genes for each ferroptosis subtype. Furthermore, we utilized weighted gene co-expression network analysis (WGCNA) to describe the immune landscape of ESCC and identify key genes for mRNA vaccine development.

Results

Our analysis revealed that MMD, MTDH, and TRFC were overexpressed ferroptosis genes in ESCC. In addition, ESCC was categorized into two ferroptosis subtypes, namely FS1 and FS2. Notably, FS2 exhibited a poorer prognosis, higher TMB, and increased immune cell infiltration when compared to FS1. The ferroptosis landscape analysis further revealed the presence of three distinct states. WGCNA analysis identified different modules of interest emerging as an independent prognostic factor and enriched with hub genes that could serve as targets for mRNA vaccine development.

Conclusions

The ferroptosis subtypes demonstrated significant associations with both prognosis and the immune microenvironment in ESCC. Additionally, the module of interest identified through immune landscape analysis represented an independent prognostic factor, with its contained genome offering promising targets for mRNA vaccine development.

Non‑coding RNA: A promising diagnostic biomarker and therapeutic target for esophageal squamous cell carcinoma (Review)

Esophageal cancer (EC) is a common form of malignant tumor in the digestive system that is classified into two types: Esophageal squamous cell carcinomas (ESCC) and esophageal adenocarcinoma. ESCC is known for its early onset of symptoms, which can be difficult to identify, as well as its rapid progression and tendency to develop drug resistance to chemotherapy and radiotherapy. These factors contribute to the high incidence of disease and low cure rate. Therefore, a diagnostic biomarker and therapeutic target need to be identified for ESCC. Non-coding RNAs (ncRNAs) are a class of molecules that are transcribed from DNA but do not encode proteins. Initially, ncRNAs were considered to be non-functional segments generated during transcription. However, with advancements in high-throughput sequencing technologies in recent years, ncRNAs have been associated with poor prognosis, drug resistance and progression of ESCC. The present study provides a comprehensive overview of the biogenesis, characteristics and functions of ncRNAs, particularly focusing on microRNA, long ncRNAs and circular RNAs. Furthermore, the ncRNAs that could potentially be used as diagnostic biomarkers and therapeutic targets for ESCC are summarized to highlight their application value and prospects in ESCC.

Construction of a novel signature based on immune-related lncRNA to identify high and low risk pancreatic adenocarcinoma patients

BACKGROUND

Pancreatic adenocarcinoma is one of the most lethal tumors in the world with a poor prognosis. Thus, an accurate prediction model, which identify patients within high risk of pancreatic adenocarcinoma is needed to adjust the treatment and elevate the prognosis of these patients.

METHODS

We obtained RNAseq data of The Cancer Genome Atlas (TCGA) pancreatic adenocarcinoma (PAAD) from UCSC Xena database, identified immune-related lncRNAs (irlncRNAs) by correlation analysis, and identified differential expressed irlncRNAs (DEirlncRNAs) between pancreatic adenocarcinoma tissues from TCGA and normal pancreatic tissues from TCGA and Genotype-Tissue Expression (GTEx). Further univariate and lasso regression analysis were performed to construct prognostic signature model. Then, we calculated the areas under curve and identified the best cut-off value to identify high- and low-risk patients with pancreatic adenocarcinoma. The clinical characteristics, immune cell infiltration, immunosuppressive microenvironment, and chemoresistance were compared between high- and low-risk patients with pancreatic adenocarcinoma.

RESULTS

We identified 20 DEirlncRNA pairs and grouped the patients by the best cut-off value. We proved that our prognostic signature model possesses a remarkable efficiency to predict prognosis of PAAD patients. The AUC for ROC curve was 0.905 for 1-year prediction, 0.942 for 2-year prediction, and 0.966 for 3-year prediction. Patients in high-risk group have poor survival rate and worse clinical characteristics. We also proved that patients in high-risk groups were in immunosuppressive status and may be resistant to immunotherapy. Anti-cancer drug evaluation was performed based on in-silico predated tool, such as paclitaxel, sorafenib, and erlotinib, may be suitable for PAAD patients in high-risk group.

CONCLUSIONS

Overall, our study constructed a novel prognostic risk model based on pairing irlncRNAs, exhibited a promising prediction value in patients with pancreatic adenocarcinoma. Our prognostic risk model may help distinguish PAAD patients suitable for medical treatments.

Identification of hub necroptosis-related lncRNAs for prognosis prediction of esophageal carcinoma

Necroptosis is a newly identified programmed cell death associated with the biological process of various cancers, including esophageal carcinoma (ESCA). Meanwhile, the dysregulation of long non-coding RNAs (lncRNAs) is greatly implicated in ESCA progression and necroptosis regulation. However, the lncRNAs involved in regulating necroptosis in ESCA are still unclear. In this study, we aim to explore the expression profile of necroptosis-related lncRNAs (NRLs), and evaluate their roles in ESCA prognosis and treatment. In the present study, 198 differentially expressed NRLs were identified between the ESCA and adjacent normal tissues through screening the data extracted from the Cancer Genome Atlas (TCGA) database. And, a prognostic panel consisting of 6 NRLs was constructed using the LASSO algorithm and multivariate Cox regression analysis. The ESCA patients with high risks had a markedly reduced survival time and higher mortality prevalence. Moreover, C-index of 6 NRLs-panel was superior to 48 published prognostic models based on lncRNAs or mRNAs for ESCA. There were significant differences between the high-risk and low-risk groups in tumor-related pathways, genetic mutations, and drug sensitivity responses. In vitro analysis revealed that inhibition of PVT1 impeded the proliferation, migration, and colony formation of ESCA cells, increased the expressions of p-RIP1 and p-MLKL and promoted necroptosis. By contrast, PVT1 overexpression resulted in a decrease in necroptotic cell death events, thus promoting tumor progression. Collectively, the established 6-NRLs panel was a promising biomarker for the prognostic prediction of ESCA. Moreover, our current findings provided potential targets for individualized therapy for ESCA patients.

Construction of prognostic risk model of bladder cancer based on cuproptosis-related long non-coding RNAs

OBJECTIVES

To construct a prognosis risk model based on long noncoding RNAs (lncRNAs) related to cuproptosis and to evaluate its application in assessing prognosis risk of bladder cancer patients.

METHODS

RNA sequence data and clinical data of bladder cancer patients were downloaded from the Cancer Genome Atlas database. The correlation between lncRNAs related to cuproptosis and bladder cancer prognosis was analyzed with Pearson correlation analysis, univariate Cox regression, Lasso regression, and multivariate Cox regression. Then a cuproptosis-related lncRNA prognostic risk scoring equation was constructed. Patients were divided into high-risk and low-risk groups based on the median risk score, and the immune cell abundance between the two groups were compared. The accuracy of the risk scoring equation was evaluated using Kaplan-Meier survival curves, and the application of the risk scoring equation in predicting 1, 3 and 5-year survival rates was evaluated using receiver operating characteristic (ROC) curves. Univariate and multivariate Cox regression were used to screen for prognostic factors related to bladder cancer patients, and a prognostic risk assessment nomogram was constructed, the accuracy of which was evaluated with calibration curves.

RESULTS

A prognostic risk scoring equation for bladder cancer patients was constructed based on nine cuproptosis-related lncRNAs. Immune infiltration analysis showed that the abundances of M0 macrophages, M1 macrophages, M2 macrophages, resting mast cells and neutrophils in the high-risk group were significantly higher than those in the low-risk group, while the abundances of CD8+ T cells, helper T cells, regulatory T cells and plasma cells in the low-risk group were significantly higher than those in the high-risk group (all P<0.05). Kaplan-Meier survival curve analysis showed that the total survival and progression-free survival of the low-risk group were longer than those of the high-risk group (both P<0.01). Univariate and multivariate Cox analysis showed that the risk score, age and tumor stage were independent factors for patient prognosis. The ROC curve analysis showed that the area under the curve (AUC) of the risk score in predicting 1, 3 and 5-year survival was 0.716, 0.697 and 0.717, respectively. When combined with age and tumor stage, the AUC for predicting 1-year prognosis increased to 0.725. The prognostic risk assessment nomogram for bladder cancer patients constructed based on patient age, tumor stage, and risk score had a prediction value that was consistent with the actual value.

CONCLUSIONS

A bladder cancer patient prognosis risk assessment model based on cuproptosis-related lncRNA has been successfully constructed in this study. The model can predict the prognosis of bladder cancer patients and their immune infiltration status, which may also provide a reference for tumor immunotherapy.

Novel chemokine related LncRNA signature correlates with the prognosis, immune landscape, and therapeutic sensitivity of esophageal squamous cell cancer

BACKGROUND

Esophageal squamous cell carcinoma (ESCC) is closely correlated with malignant biological characteristics and poor survival. Recently, chemokines have been reported to be involved in the progression of tumors, and they can also regulate the tumor microenvironment. However, it is unclear whether chemokine-related long noncoding RNAs (lncRNAs) affect the prognosis of ESCC.

METHODS

We downloaded RNA-seq and clinical data from the Gene Expression Omnibus (GEO database. Chemokine-related lncRNAs were screened by differential analysis and Pearson correlation analysis. Then, prognosis-related lncRNAs were screened by using univariate COX regression, and risk models were constructed after the least absolute shrinkage and selection operator (LASSO) regression and multivariate COX regression. The predictive value of the signature was assessed using Kaplan-Meier test, time-dependent receiver operating characteristic (ROC) curves, decision curve analysis (DCA) and calibration curve. Moreover, a nomogram to predict patients' 1-year 3-year and 5-year prognosis was constructed. Gene set enrichment analyses (GSEA), Gene Ontology/Kyoto Encyclopedia of Genes and Genomes (GO/KEGG), evaluation of immune cell infiltration, and estimation of drug sensitivity were also conducted.

RESULTS

In this study, 677 chemokine-related lncRNAs were first obtained by differential analysis and Pearson correlation. Then, six chemokine-related lncRNAs were obtained by using univariate COX, LASSO and multivariate COX to construct a novel chemokine-related lncRNAs risk model. The signature manifested favorable predictive validity and accuracy both in the testing and training cohorts. The chemokine-related signature could classify ESCC patients into two risk groups well, which indicated that high-risk group exhibited poor prognostic outcome. In addition, this risk model played an important role in predicting signaling pathways, immune cell infiltration, stromal score, and drug sensitivity in ESCC patients.

CONCLUSIONS

These findings elucidated the critical role of novel prognostic chemokine-related lncRNAs in prognosis, immune landscape, and drug therapy, thus throwing light on prognostic evaluation and therapeutic targets for ESCC patients.

Treatments for resectable esophageal cancer: from traditional systemic therapy to immunotherapy

ABSTRACT

Esophageal cancer (EC) has a high incidence and poor prognosis. The two major histological types, squamous cell carcinoma and adenocarcinoma, differ in their epidemiology and treatment options. Patients with locally advanced EC benefit from multimodal therapy concepts including neoadjuvant chemotherapy, neoadjuvant chemoradiotherapy, and perioperative chemotherapy. Currently, immunotherapy for the solid tumor is a hot spot. Treatment with adjuvant immune checkpoint inhibitors (ICIs) is the first immunotherapy for resectable EC listed in the latest National Comprehensive Cancer Network Guidelines for the Esophageal and Esophagogastric Junction Cancers. Recent clinical trials have established ICIs for three treatment models of resectable EC. Their short-term results demonstrated ideal efficacy and tolerable toxicity, though some concerns remain. This review summarizes the novel data on the ICIs for resectable EC and lists the registered related clinical trials. Hopefully, this review can provide a reference for ongoing research on the treatment options for resectable EC.

A novel immune-related radioresistant lncRNAs signature based model for risk stratification and prognosis prediction in esophageal squamous cell carcinoma

Background and purpose: Radioresistance remains a major reason of radiotherapeutic failure in esophageal squamous cell carcinoma (ESCC). Our study is to screen the immune-related long non-coding RNA (ir-lncRNAs) of radiation-resistant ESCC (rr-ESCC) via Gene Expression Omnibus (GEO) database and to construct a prognostic risk model.

Methods: Microarray data (GSE45670) related to radioresistance of ESCC was downloaded from GEO. Based on pathologic responses after chemoradiotherapy, patients were divided into a non-responder (17 samples) and responder group (11 samples), and the difference in expression profiles of ir-lncRNAs were compared therein. Ir-lncRNA pairs were constructed for the differentially expressed lncRNAs as prognostic variables, and the microarray dataset (GSE53625) was downloaded from GEO to verify the effect of ir-lncRNA pairs on the long-term survival of ESCC. After modelling, patients are divided into high- and low-risk groups according to prognostic risk scores, and the outcomes were compared within groups based on the COX proportional hazards model. The different expression of ir-lncRNAs were validated using ECA 109 and ECA 109R cell lines via RT-qPCR.

Results: 26 ir-lncRNA genes were screened in the GSE45670 dataset with differential expression, and 180 ir-lncRNA pairs were constructed. After matching with ir-lncRNA pairs constructed by GSE53625, six ir-lncRNA pairs had a significant impact on the prognosis of ESCC from univariate analysis model, of which three ir-lncRNA pairs were significantly associated with prognosis in multivariate COX analysis. These three lncRNA pairs were used as prognostic indicators to construct a prognostic risk model, and the predicted risk scores were calculated. With a median value of 2.371, the patients were divided into two groups. The overall survival (OS) in the high-risk group was significantly worse than that in the low-risk group (p < 0.001). The 1-, 2-, and 3-year prediction performance of this risk-model was 0.666, 0.702, and 0.686, respectively. In the validation setting, three ir-lncRNAs were significantly up-regulated, while two ir-lncRNAs were obviouly down-regulated in the responder group.

Conclusion: Ir-lncRNAs may be involved in the biological regulation of radioresistance in patients with ESCC; and the prognostic risk-model, established by three ir-lncRNAs pairs has important clinical value in predicting the prognosis of patients with rr-ESCC.

Characterization and validation of a ferroptosis-related LncRNA signature as a novel prognostic model for lung adenocarcinoma in tumor microenvironment

Ferroptosis is a more relatively recently identified type of programmed cell death, which is associated with tumor progression. However, the mechanism underlying the effect of ferroptosis-related long non-coding RNAs (lncRNAs) in lung adenocarcinoma (LUAD) remains elusive. Therefore, the current study aimed to investigate the role of ferroptosis-related lncRNAs in LUAD and to develop a prognostic model. The clinicopathological characteristics of patients and the gene sequencing data were obtained from The Cancer Genome Atlas, while the ferroptosis-associated mRNAs were downloaded from the FerrDb database. A ferroptosis-related lncRNA signature was established with Least Absolute Shrinkage and Selection Operator Cox regression analysis. Furthermore, the risk scores of ferroptosis-related lncRNAs were calculated and LUAD patients were then assigned to high- and low-risk groups based on the median risk score. The prognostic model was established by K-M plotters and nomograms. Gene set enrichment analysis (GSEA) was performed to evaluate the association between immune responses and ferroptosis-related lncRNAs. A total of 10 ferroptosis-related lncRNAs were identified as independent predictors of LUAD outcome, namely RP11-386M24.3, LINC00592, FENDRR, AC104699.1, AC091132.1, LANCL1-AS1, LINC-PINT, IFNG-AS1, LINC00968 and AC006129.2. The area under the curve verified that the established signatures could determine LUAD prognosis. The nomogram model was used to assess the predictive accuracy of the established signatures. Additionally, GSEA revealed that the 10 ferroptosis-related lncRNAs could be involved in immune responses in LUAD. Overall, the results of the current study may provide novel insights into the development of novel therapies or diagnostic strategies for LUAD.

Lactate Metabolism-Associated lncRNA Pairs: A Prognostic Signature to Reveal the Immunological Landscape and Mediate Therapeutic Response in Patients With Colon Adenocarcinoma

Background

Lactate metabolism is critically involved in the tumor microenvironment (TME), as well as cancer progression. It is important to note, however, that lactate metabolism-related long non-coding RNAs (laRlncRNAs) remain incredibly understudied in colon adenocarcinoma (COAD).

Methods

A gene expression profile was obtained from the Cancer Genome Atlas (TCGA) database to identify laRlncRNA expression in COAD patients. A risk signature with prognostic value was identified from TCGA and Gene Expression Omnibus (GEO) cohort based on laRlncRNA pairs by the least absolute shrinkage and selection operator (LASSO) and Cox regression analyses. Quantitative real-time polymerase chain reaction (qRT-PCR) and functional experiments were carried out to verify the expression of laRlncRNAs in COAD. The relationship of laRlncRNA pairs with immune landscape as well as the sensitivity of different therapies was explored.

Results

In total, 2378 laRlncRNAs were identified, 1,120 pairs of which were studied to determine their prognostic validity, followed by a risk signature established based on the screened 5 laRlncRNA pairs. The laRlncRNA pairs-based signature provided a better overall survival (OS) prediction than other published signatures and functioned as a prognostic marker for COAD patients. According to the calculated optimal cut-off point, patients were divided into high- and low-risk groups. The OS of COAD patients in the high-risk group were significantly shorter than that of those in the low-risk group (P=4.252e-14 in the TCGA cohort and P=2.865-02 in the GEO cohort). Furthermore, it remained an effective predictor of survival in strata of gender, age, TNM stage, and its significance persisted after univariate and multivariate Cox regressions. Additionally, the risk signature was significantly correlated with immune cells infiltration, tumor mutation burden (TMB), microsatellite instability (MSI) as well as immunotherapeutic efficacy and chemotherapy sensitivity. Finally, one of the laRlncRNA, LINC01315, promotes proliferation and migration capacities of colon cancer cells.

Conclusion

The newly identified laRlncRNAs pairs-based signature exhibits potential effects in predicting prognosis, deciphering patients’ immune landscape, and mediating sensitivity to immunotherapy and chemotherapy. Findings in our study may provide evidence for the role of laRlncRNAs pairs as novel prognostic biomarkers and potentially individualized therapy targets for COAD patients.

A Novel Defined Endoplasmic Reticulum Stress-Related lncRNA Signature for Prognosis Prediction and Immune Therapy in Glioma

Gliomas are a group of the most aggressive primary central nervous system tumors with limited treatment options. The abnormal expression of long non-coding RNA (lncRNA) is related to the prognosis of glioma. However, the role of endoplasmic reticulum (ER) stress-associated lncRNAs in glioma prognosis has not been reported. In this paper, we obtained ER stress-related lncRNAs by co-expression analysis, and then a risk signature composed of 6 ER stress-related lncRNAs was constructed using Cox regression analysis. Glioma samples in The Cancer Genome Atlas (TCGA) were separated into high- and low-risk groups based on the median risk score. Compared with the low-risk group, patients in the high-risk group had shorter survival times. Additionally, we verified the predictive ability of these candidate lncRNAs in the testing set. Three glioma patient subgroups (cluster 1/2/3) were identified by consensus clustering. We further analysed the abundance of immune-infiltrating cells and the expression levels of immune checkpoint molecules in both three subgroups and two risk groups, respectively. Immunotherapy and anticancer drug response prediction showed that ER stress-related lncRNA risk signature positively correlates with responding to immune checkpoints and chemosensitivity. Functional analysis showed that these gene sets are enriched in the malignant process of tumors. Finally, LINC00519 was chosen for functional experiments. The silence of LINC00519 restrained the migration and invasion of glioma cells. Hence, those results indicated that ER stress-related lncRNA risk signature could be a potential treatment target and a prognosis biomarker for glioma patients.

An Immune-Related lncRNA Signature to Predict the Biochemical Recurrence and Immune Landscape in Prostate Cancer

Purpose

This study aims to construct an immune-related signature to provide comprehensive insights into the immune landscape of prostate cancer, which can predict biochemical recurrence (BCR) and clinical treatment.

Methods

Based on The Cancer Genome Atlas (TCGA) dataset, a signature constructed by DEirlncRNAs pairs was determined. The receiver operating characteristic curve analysis, Kaplan-Meier analysis, nomogram, and decision curve analysis were used to analyze it. Then, immunophenoscore (IPS), immune cell infiltration, tumor mutation burden (TMB), and immune function were investigated. Finally, we evaluated the role of the signature in medical treatment.

Results

A signature constructed by 10 valid DEirlncRNAs pairs was identified in the training set and validated well in the testing and entire set. The signature was a reliable and independent prognostic indicator to predict the BCR of prostate cancer, which was better than the clinicopathological characteristics. After dividing the patients into low- and high-risk groups by median value, we found that the high-risk group had shorter BCR-free time and higher TMB levels. Furthermore, the high-risk group was negatively associated with plasma B cells and CD+8 T cells. IPS and immune functions, such as immune checkpoints and human leukocyte antigen, were significantly different between the two groups. Low-risk group was more sensitive to endocrine therapy and immunotherapy, while high-risk group was more inclined to targeted drugs. Both groups had their own sensitive chemotherapy.

Conclusion

We established a novel signature to predict BCR and validated its role in the immune landscape of prostate cancer, which could help patients receive personalized medical treatment.

Hypoxia-Related lncRNA Correlates With Prognosis and Immune Microenvironment in Lower-Grade Glioma

Background

Hypoxia-related genes are demonstrated to correlate with the prognosis of various cancers. However, the role of hypoxia-related long non-coding RNAs (HRLs) in lower-grade glioma (LGG) remains unclear.

Methods

A total of 700 LGG samples were extracted from TCGA and CGGA databases. Pearson correlation analysis was used to identify HRLs. Lasso analysis was adopted to construct the HRL signature. TIDE algorithm was used to predict responses to immune checkpoint inhibitors. Cell proliferation was estimated by cell counting kit-8 assay, colony formation assay, and EdU assay.

Results

We identified 340 HRLs and constructed a novel risk signature composed of 19 HRLs. The risk score exhibited potent value in predicting the prognosis of LGG patients and was significantly associated with the prognosis of LGG patients. Moreover, HRL signature could distinguish patients with similar expression levels of immune checkpoints and might predict the efficacy of immune checkpoint inhibitors. Additionally, hypoxia-related pathways and immune pathways were enriched in high-risk group, and high risk score indicated low tumor purity and high immune infiltration. Two major HRLs, LINC00941 and BASP1-AS1, could significantly affect the proliferation of glioma cells.

Conclusions

Our study constructed a novel HRL signature that could predict the prognosis and immunotherapy response of LGG patients. HRLs could be novel biomarkers to predict the prognosis of LGG patients and potential targets for LGG treatment.

Construction of a Novel Immune-Related lncRNA Pair Signature with Prognostic Significance for Kidney Clear Cell Renal Cell Carcinoma

Background

Renal cell carcinoma (RCC) is one of the most common aggressive malignant tumors in the urinary system, among which the clear cell renal cell carcinoma (ccRCC) is the most common subtype. The immune-related long noncoding ribonucleic acids (irlncRNAs) which are abundant in immune cells and immune microenvironment (IME) have potential significance in evaluating the prognosis and effects of immunotherapy. The signature based on irlncRNA pairs and independent of the exact expression level seems to have a latent predictive significance for the prognosis of patients with malignant tumors but has not been applied in ccRCC yet.

Method

In this article, we retrieved The Cancer Genome Atlas (TCGA) database for the transcriptome profiling data of the ccRCC and performed coexpression analysis between known immune-related genes (ir-genes) and lncRNAs to find differently expressed irlncRNA (DEirlncRNA). Then, we adopted a single-factor test and a modified LASSO regression analysis to screen out ideal DEirlncRNAs and constructed a Cox proportional hazard model. We have sifted 28 DEirlncRNA pairs, 12 of which were included in this model. Next, we compared the area under the curve (AUC), found the cutoff point by using the Akaike information criterion (AIC) value, and distinguished the patients with ccRCC into a high-risk group and a low-risk group using this value. Finally, we tested this model by investigating the relationship between risk score and survival, clinical pathological characteristics, cells in tumor immune microenvironment, chemotherapy, and targeted checkpoint biomarkers.

Results

A novel immune-related lncRNA pair signature consisting of 12 DEirlncRNA pairs was successfully constructed and tightly associated with overall survival, clinical pathological characteristics, cells in tumor immune microenvironment, and reactiveness to immunotherapy and chemotherapy in patients with ccRCC. Besides, the efficacy of this signature was verified in some commonly used clinicopathological subgroups and could serve as an independent prognostic factor in patients with ccRCC.

Conclusions

This signature was proven to have a potential predictive significance for the prognosis of patients with ccRCC and the efficacy of immunotherapy.

Identification and Validation of a Ferroptosis-Related Long Non-coding RNA Signature for Predicting the Outcome of Lung Adenocarcinoma

BACKGROUND

Ferroptosis is a recently recognized type of programmed cell death that is involved in the biological processes of various cancers. However, the mechanism of ferroptosis in lung adenocarcinoma (LUAD) remains unclear. This study aimed to determine the role of ferroptosis-associated long non-coding RNAs (lncRNAs) in LUAD and to establish a prognostic model.

METHODS

We downloaded ferroptosis-related genes from the FerrDb database and RNA sequencing data and clinicopathological characteristics from The Cancer Genome Atlas. We randomly divided the data into training and validation sets. Ferroptosis-associated lncRNA signatures with the lowest Akaike information criteria were determined using COX regression analysis and the least absolute shrinkage and selection operator. The risk scores of ferroptosis-related lncRNAs were calculated, and patients with LUAD were assigned to high- and low-risk groups based on the median risk score. The prognostic value of the risk scores was evaluated using Kaplan-Meier curves, Cox regression analyses, and nomograms. We then explored relationships between ferroptosis-related lncRNAs and the immune response using gene set enrichment analysis (GSEA).

RESULTS

Ten ferroptosis-related lncRNA signatures were identified in the training group, and Kaplan-Meier and Cox regression analyses confirmed that the risk scores were independent predictors of LUAD outcome in the training and validation sets (all P < 0.05). The area under the curve confirmed that the signatures could determine the prognosis of LUAD. The predictive accuracy of the established nomogram model was verified using the concordance index and calibration curve. The GSEA showed that the 10 ferroptosis-related lncRNAs might be associated with tumor immune response.

CONCLUSION

We established a novel signature involving 10 ferroptosis-related lncRNAs (LINC01843, MIR193BHG, AC091185.1, AC027031.2, AL021707.2, AL031667.3, AL606834.1, AC026355.1, AC124045.1, and AC025048.4) that can accurately predict the outcome of LUAD and are associated with the immune response. This will provide new insights into the development of new therapies for LUAD.