Long Non-Coding RNA Profile Study Identifies an Immune-Related lncRNA Prognostic Signature for Kidney Renal Clear Cell Carcinoma

Prognosis prediction of head and neck squamous cell carcinoma through the basement membrane-related lncRNA risk model

Background

Head and neck squamous cell carcinoma (HNSCC) ranks among the most widespread and significantly heterogeneous malignant tumors globally. Increasing evidence suggests that the basement membrane (BM) and associated long non-coding RNAs (lncRNA) are correlated with the onset of HNSCC and its prognosis. Our study aims to construct a basement membrane-associated lncRNAs (BMlncRNAs) marker to accurately predict the prognosis of HNSCC patients and find novel immunotherapy targets.

Methods

The Cancer Genome Atlas (TCGA) database was accessed to acquire the transcriptome expression matrices, somatic mutation data, and clinical follow-up data of HNSCC patients. Utilizing co-expression analysis, the BMlncRNAs were identified and the differentially expressed lncRNAs (DEBMlncRNA) were then filtered, The filtering thresholds are FDR<0.05 and |log2FC|≥1. Furthermore, univariate analysis, least absolute shrinkage and selection operator (LASSO), and multivariable Cox regression were utilized to develop the risk model. The model then underwent thorough evaluation across diverse perspectives, encompassing tumor immune infiltration, tumor mutation burden (TMB), functional enrichment, and chemotherapy sensitivity.

Results

The risk assessment model consists of 14 BMlncRNA pairs. The acquired data is indicative of the reliability of the risk score in its capacity as a prognostic factor. Individuals at high risk exhibited a poorer prognosis, and a statistically significant variance was noted in TMB and tumor immune infiltration compared to the low-risk group. Additionally, heightened sensitivity to paclitaxel and docetaxel was evident in the patients at high risk.

Conclusion

We have established a BMLncRNA-based prognostic model that can provide clinical guidance for future laboratory and clinical studies of HNSCC.

LASSO regression and WGCNA-based telomerase-associated lncRNA signaling predicts clear cell renal cell carcinoma prognosis and immunotherapy response

OBJECTIVE

To investigate whether telomerase-associated lncRNA expression affects the prognosis and anti-tumor immunity of patients with renal clear cell carcinoma (ccRCC).

METHODS

A series of analyses were performed to establish a prognostic risk model and validate its accuracy. Immune-related analyses were performed to assess further the association between immune status, tumor microenvironment, and prognostic risk models.

RESULTS

Eight telomerase-associated lncRNAs associated with prognosis were identified and applied to establish a prognostic risk model. Overall survival was higher in the low-risk group.

CONCLUSION

The established prognostic risk model has a good predictive ability for the prognosis of ccRCC patients and provides a new possible therapeutic target for ccRCC.

A novel immune-related lncRNA signature predicts the prognosis and immune landscape in ccRCC

BACKGROUND

As one of the most common tumors, the pathogenesis and progression of clear cell renal cell carcinoma (ccRCC) in the immune microenvironment are still unknown.

METHODS

The differentially expressed immune-related lncRNA (DEirlncRNA) was screened through co-expression analysis and the limma package of R, which based on the ccRCC project of the TCGA database. Then, we designed the risk model by irlncRNA pairs. In RCC patients, we have compared the area under the curve, calculated the Akaike Information Criterion (AIC) value of the 5-year receiver operating characteristic curve, determined the cut-off point, and established the optimal model for distinguishing the high-risk group from the low-risk group. We used the model for immune system assessment, immune point detection and drug sensitivity analysis after verifying the feasibility of the above model through clinical features.

RESULTS

In our study, 1541 irlncRNAs were included. 739 irlncRNAs were identified as DEirlncRNAs to construct irlncRNA pairs. Then, 38 candidate DEirlncRNA pairs were included in the best risk assessment model through improved LASSO regression analysis. As a result, we found that in addition to age and gender, T stage, M stage, N stage, grade and clinical stage are significantly related to risk. Moreover, univariate and multivariate Cox regression analysis results reveals that in addition to gender, age, grade, clinical stage and risk score are independent prognostic factors. The results show that patients in the high-risk group are positively correlated with tumor infiltrating immune cells when the above model is applied to the immune system. But they are negatively correlated with endothelial cells, macrophages M2, mast cell activation, and neutrophils. In addition, the risk model was positively correlated with overexpressed genes (CTLA, LAG3 and SETD2, P<0.05). Finally, risk models can also play as an important role in predicting the sensitivity of targeted drugs.

CONCLUSIONS

The new risk model may be a new method to predict the prognosis and immune status of ccRCC.

A Notch signaling-related lncRNA signature for predicting prognosis and therapeutic response in clear cell renal cell carcinoma

Increasing evidence has confirmed the vital role of Notch signaling in the tumorigenesis of clear cell renal cell carcinoma (ccRCC). The underlying function of long non-coding RNA (lncRNA) related to Notch signaling in ccRCC remains unclear. In present study, the prognostic value and therapeutic strategy of Notch signaling-related lncRNA are comprehensively explored in ccRCC. In total, we acquired 1422 NSRlncRNAs, of which 41 lncRNAs were identified the key NSRlncRNAs associated with the occurrence of ccRCC. The prognostic signature containing five NSRlncRNAs (AC092611.2, NNT-AS1, AGAP2-AS1, AC147651.3, and AC007406.3) was established and validated, and the ccRCC patients were clustered into the high- and low-risk groups. The overall survival of patients in the low-risk group were much more favorable than those in the high-risk group. Multivariate Cox regression analysis indicated that the risk score was an independent prognostic biomarker. Based on the risk score and clinical variables, a nomogram for predicting prognosis of ccRCC patients was constructed, and the calibration curves and DCA curves showed the superior predictive ability of nomogram. The risk score was correlated with immune cell infiltration, targeted therapy or chemotherapy sensitivity, and multiple oncogenic pathways. Additionally, consensus clustering analysis stratified the ccRCC patients into four clusters with obvious different outcomes, immune microenvironments, and expression of immune checkpoints. The constructed NSRlncRNA-based signature might serve as a potential biomarker for predicting prognosis and response to immunotherapy or targeted therapy in patients with ccRCC.

Predicting the prognosis, immune response, and immunotherapy in head and neck squamous cell carcinoma using a novel risk model based on anoikis-related lncRNAs

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) is an extremely heterogeneous and metastatic disease. Anoikis, which is a specific type of programmed apoptosis, is involved in tumor metastasis, tissue homeostasis, and development. Herein, we constructed an anoikis-related long non-coding RNA (lncRNA) signature to predict the prognosis, immune responses, and therapeutic effects in HNSCC patients.

METHODS

A total of 501 HNSCC samples were acquired from the TCGA database and randomly classified into the training and validation groups (1:1 ratio). Thereafter, the results derived from the training set were analyzed with the LASSO regression analysis, and a novel anoikis-related lncRNA risk model was constructed. Time-dependent ROC curves and Kaplan-Meier analysis were carried out to assess the diagnostic value and survival outcomes. A nomogram was utilized to predict the prognostic accuracy. Furthermore, we studied the tumor microenvironment, tumor mutation burden, enrichment pathways, and the response to chemotherapy and immunotherapy.

RESULTS

Seven anoikis-related lncRNAs (AC015878.1, CYTOR, EMSLR, LINC01503, LINC02084, RAB11B-AS1, Z97200.1) were screened to design a novel risk model, which was recognized as the independent prognostic factor for HNSCC patients. The findings implied that low-risk patients showed significantly longer OS, PFS, and DSS compared to those high-risk patients. The two groups that were classified using the risk model showed significant differences in their immune landscape. The risk model also predicted that low-risk HNSCC patients could attain a better response to immunotherapy, while high-risk patients would be more sensitive to gemcitabine, docetaxel, and cisplatin.

CONCLUSIONS

We constructed a novel risk model that could be employed for effectively predicting patient prognosis with a good independent prognostic value for HNSCC patients. Furthermore, this model could be used for designing new immunotherapeutic and chemotherapeutic strategies, and it helps clinicians establish personalized and detailed strategies for HNSCC patients.

A novel prognostic N7-methylguanosine-related long non-coding RNA signature in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is regulated by methylation modifications and long noncoding RNAs (lncRNAs). However, knowledge of N7-methylguanosine (m7G)-related lncRNAs that predict ccRCC prognosis remains insufficient. A prognostic multi-lncRNA signature was created using LASSO regression to examine the differential expression of m7G-related lncRNAs in ccRCC. Furthermore, we performed Kaplan-Meier analysis and area under the curve (AUC) analysis for diagnosis. In all, a model based on five lncRNAs was developed. Principal component analysis (PCA) indicated that the risk model precisely separated the patients into different groups. The IC50 value for drug sensitivity divided patients into two risk groups. High-risk group of patients was more susceptible to A.443654, A.770041, ABT.888, AMG.706, and AZ628. Moreover, a lower tumor mutation burden combined with low-risk scores was associated with a better prognosis of ccRCC. Quantitative real-time polymerase chain reaction (qRT-PCR) exhibited that the expression levels of LINC01507, AC093278.2 were very high in all five ccRCC cell lines, AC084876.1 was upregulated in all ccRCC cell lines except 786-O, and the levels of AL118508.1 and DUXAP8 were upregulated in the Caki-1 cell line. This risk model may be promising for the clinical prediction of prognosis and immunotherapeutic responses in patients with ccRCC.

Construction and validation of stemness-related lncRNA pair signature for predicting prognosis in colorectal cancer

PURPOSE

The purpose of this study was to identify a prognostic signature based on stemness-related differentially expressed lncRNAs in colorectal cancer (CRC) and to investigate their potential as biomarkers for diagnosis, prognosis, and therapeutic targets.

METHODS

Stemness-related genes were collected from the TCGA cohort, and 13 differently expressed stemness-related lncRNAs were identified as prognostic factors for CRC using Kaplan-Meier analysis. A risk model was constructed based on the calculated risk score as a novel independent prognostic factor for CRC patients. The study also investigated the association between the risk model and immune checkpoints and m6A differentiation gene expression. qRT-PCR analysis was performed to validate the expression of differentially expressed stemness-related lncRNAs in CRC cell lines compared to normal colon mucosal cell line.

RESULTS

The low-risk lncRNAs were associated with higher survival in CRC patients (Kaplan-Meier analysis, P < 0.001). The risk model was a significant independent prognostic factor for CRC patients. Type I INF response was statistically significant between low- and high-risk groups. CD44, CD70, PVR, TNFSF4, BTNL2, CD40, these immune checkpoints were expressed differently between two risk groups. There was a significant difference between m6A differentiation gene expression such as METTL3, METTL14, WTAP, RBM15, ZC3H13, YTHDC2, YTHDF2, ALKBH5. qRT-PCR analysis validated that there were five up-regulated and eight down-regulated differently expressed stemness-related lncRNAs in CRC cell lines compared to the normal colon mucosal cell line.

CONCLUSION

This study suggests that the 13 CRC stemness-related lncRNA signature could become a promising and reliable prognostic factor for colorectal cancer. The risk model based on the calculated risk score may have implications for personalized medicine and targeted therapies for CRC patients. The study also suggests that immune checkpoints and m6A differentiation genes may play important roles in the development and progression of CRC.

Single-cell and spatial transcriptomics reveal 5-methylcytosine RNA methylation regulators immunologically reprograms tumor microenvironment characterizations, immunotherapy response and precision treatment of clear cell renal cell carcinoma

Clear cell Renal Cell Carcinoma (ccRCC) is a highly heterogeneous disease, making it challenging to predict prognosis and therapy efficacy. In this study, we aimed to explore the role of 5-methylcytosine (m5C) RNA modification in ccRCC and its potential as a predictor for therapy response and overall survival (OS). We established a novel 5-methylcytosine RNA modification-related gene index (M5CRMRGI) and studied its effect on the tumor microenvironment (TME) using single-cell sequencing data for in-depth analysis, and verified it using spatial sequencing data. Our results showed that M5CRMRGI is an independent predictor of OS in multiple datasets and exhibited outstanding performance in predicting the OS of ccRCC. Distinct mutation profiles, hallmark pathways, and infiltration of immune cells in TME were observed between high- and low-M5CRMRGI groups. Single-cell/spatial transcriptomics revealed that M5CRMRGI could reprogram the distribution of tumor-infiltrating immune cells. Moreover, significant differences in tumor immunogenicity and tumor immune dysfunction and exclusion (TIDE) were observed between the two risk groups, suggesting a better response to immune checkpoint blockade therapy of the high-risk group. We also predicted six potential drugs binding to the core target of the M5CRMRGI signature via molecular docking. Real-world treatment cohort data proved once again that high-risk patients were appropriate for immune checkpoint blockade therapy, while low-risk patients were appropriate for Everolimus. Our study shows that the m5C modification landscape plays a role in TME distribution. The proposed M5CRMRGI-guided strategy for predicting survival and immunotherapy efficacy, we reported here, might also be applied to more cancers other than ccRCC.

Identification and validation of fatty acid metabolism-related lncRNA signatures as a novel prognostic model for clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is a main subtype of renal cancer, and advanced ccRCC frequently has poor prognosis. Many studies have found that lipid metabolism influences tumor development and treatment. This study was to examine the prognostic and functional significance of genes associated with lipid metabolism in individuals with ccRCC. Using the database TCGA, differentially expressed genes (DEGs) associated with fatty acid metabolism (FAM) were identified. Prognostic risk score models for genes related to FAM were created using univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses. Our findings demonstrate that the prognosis of patients with ccRCC correlate highly with the profiles of FAM-related lncRNAs (AC009166.1, LINC00605, LINC01615, HOXA-AS2, AC103706.1, AC009686.2, AL590094.1, AC093278.2). The prognostic signature can serve as an independent predictive predictor for patients with ccRCC. The predictive signature's diagnostic effectiveness was superior to individual clinicopathological factors. Between the low- and high-risk groups, immunity research revealed a startling difference in terms of cells, function, and checkpoint scores. Chemotherapeutic medications such lapatinib, AZD8055, and WIKI4 had better outcomes for patients in the high-risk group. Overall, the predictive signature can help with clinical selection of immunotherapeutic regimens and chemotherapeutic drugs, improving prognosis prediction for ccRCC patients.

Ferroptosis-associated lncRNA prognostic signature predicts prognosis and immune response in clear cell renal cell carcinoma

Clear cell Renal Cell Carcinoma (ccRCC), the most deadly and life-threatening tumor in the urinary system, has a dismal prognosis and a high risk of metastasizing. Regulation of ferroptosis is a prospective therapeutic target to eradicate malignant cells. Our objective was to seek ferroptosis-associated long non-coding RNAs (FALs) and developed a prediction signature for ccRCC. We extracted transcriptome data and clinical information from The Cancer Genome Atlas (TCGA) databases. Ferroptosis-associated genes (FAGs) were obtained from FerrDb database. A ferroptosis-associated lncRNA prognostic signature (FLPS) of ccRCC was generated utilizing univariate Cox regression, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression, sequentially, based on 8 lncRNAs (LINC00460, AC124854.1, AC084876.1, IGFL2-AS1, LINC00551, AC083967.1, AC073487.1, and LINC02446). The signature's independent predictive value for ccRCC was demonstrated using univariate and multivariate regression analysis (P < 0.05). Subsequently, by combining independent predictive factors, a prognostic nomogram was established. Immunity analysis proclaimed a striking difference in terms of cells, function, checkpoints, and ESTIMATE scores between low- and high-risk groups. Overall, the innovative signature of ferroptosis-associated signatures may have a considerable effect on the immune response and prognosis for ccRCC.

Assessment of Diagnosis, Prognosis and Immune Infiltration Response to the Expression of the Ferroptosis-Related Molecule HAMP in Clear Cell Renal Cell Carcinoma

BACKGROUND

Hepcidin antimicrobial peptide (HAMP) is a key factor in maintaining iron metabolism, which may induce ferroptosis when upregulated. However, its prognostic value and relation to immune infiltrating cells remains unclear.

METHODS

This study analyzed the expression levels of HAMP in the Oncomine, Timer and Ualcan databases, and examined its prognostic potential in KIRC with R programming. The Timer and GEPIA databases were used to estimate the correlations between HAMP and immune infiltration and the markers of immune cells. The intersection genes and the co-expression PPI network were constructed via STRING, R programming and GeneMANIA, and the hub genes were selected with Cytoscape. In addition, we analyzed the gene set enrichment and GO/KEGG pathways by GSEA.

RESULTS

Our study revealed higher HAMP expression levels in tumor tissues including KIRC, which were related to poor prognosis in terms of OS, DSS and PFI. The expression of HAMP was positively related to the immune infiltration level of macrophages, Tregs, etc., corresponding with the immune biomarkers. Based on the intersection genes, we constructed the PPI network and used the 10 top hub genes. Further, we performed a pathway enrichment analysis of the gene sets, including Huntington's disease, the JAK-STAT signaling pathway, ammonium ion metabolic process, and so on.

CONCLUSION

In summary, our study gave an insight into the potential prognosis of HAMP, which may act as a diagnostic biomarker and therapeutic target related to immune infiltration in KIRC.

Long-stranded non-coding RNAs temporal-specific expression profiles reveal longissimus dorsi muscle development and intramuscular fat deposition in Tianzhu white yak

The process of muscle development and intramuscular fat (IMF) deposition is quite complex and controlled by both mRNAs and ncRNAs. Long-stranded non-coding RNAs (LncRNAs) are involved in various biological processes in mammals while also playing a critical role in muscle development and fat deposition. In the present study, RNA-Seq was used to comprehensively study the expression of lncRNAs and mRNAs during muscle development and intramuscular fat deposition in postnatal Tianzhu white yaks at three stages, including 6 mo of age (calve, n = 6), 30 mo of age (young cattle, n = 6) and 54 mo of age (adult cattle, n = 6). The results indicated that a total of 2,101 lncRNAs and 20,855 mRNAs were screened across the three stages, of which the numbers of differential expression (DE) lncRNAs and DE mRNAs were 289 and 1,339, respectively, and DE lncRNAs were divided into eight different expression patterns based on expression trends. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed that some DE mRNAs overlapped with target genes of lncRNAs, such as NEDD4L, SCN3B, AGT, HDAC4, DES, MYH14, KLF15 (muscle development), ACACB, PCK2, LIPE, PIK3R1, PNPLA2, and MGLL (intramuscular fat deposition). These DE mRNAs were significantly enriched in critical muscle development and IMF deposition-related pathways and GO terms, such as AMPK signaling pathway, PI3K-Akt signaling pathway, PPAR signaling pathway, etc. In addition, lncRNA-mRNA co-expression network analysis revealed that six lncRNAs (MSTRG.20152.2, MSTRG.20152.3, XR_001351700.1, MSTRG.8190.1, MSTRG.4827.1, and MSTRG.11486.1) may play a major role in Tianzhu white yak muscle development and lipidosis deposition. Therefore, this study enriches the database of yak lncRNAs and could help to further explore the functions and roles of lncRNAs in different stages of muscle development and intramuscular fat deposition in the Tianzhu white yak.

Prognostic value and immunological characteristics of a novel cuproptosis-related long noncoding RNAs risk signature in kidney renal clear cell carcinoma

Background: Cuproptosis has been found as a novel cell death mode significantly associated with mitochondrial metabolism, which may be significantly associated with the occurrence and growth of tumors. LncRNAs take on critical significance in regulating the development of kidney renal clear cell carcinoma (KIRC), whereas the correlation between cuproptosis-related LncRNAs (CRLs) and KIRC is not clear at present. Therefore, this study built a prognosis signature based on CRLs, which can achieve accurate prediction of the outcome of KIRC patients. Methods: The TCGA database provided the expression profile information and relevant clinical information of KIRC patients. Univariate Cox, Lasso, and multivariate Cox were employed for building a risk signature based on CRLs. Kaplan-Meier (K-M) survival analysis and time-dependent receiver operating characteristic (ROC) curve were employed for the verification and evaluation of the reliability and accuracy of risk signature. Then, qRT-PCR analysis of risk LncRNAs was conducted. Finally, the possible effect of the developed risk signature on the microenvironment for tumor immunization was speculated in accordance with ssGSEA and ESTIMATE algorithms. Results: A prognosis signature composed of APCDD1L-DT, MINCR, AL161782.1, and AC026401.3 was built based on CRLs. As revealed by the results of the K-M survival study, the OS rate and progression-free survival rate of highrisk KIRC patients were lower than those of lowrisk KIRC patients, and the areas under ROC curves of 1, 3, and 5 years were 0.828, 0.780, and 0.794, separately. The results of the immune analysis showed that there were significant differences in the status of immunization and the microenvironment of tumor between groups at low-risk and at high-risk. The qRT-PCR results showed that the relative expression level of MINCR and APCDD1L-DT were higher in 786-O and 769-P tumor cells than in HK-2 cells, which were normal renal tubular epithelial cells. Conclusion: The developed risk signature takes on critical significance in the prediction of the prognosis of patients with KIRC, and it can bring a novel direction for immunotherapy and clinical drug treatment of KIRC. In addition, 4 identified risk LncRNAs (especially APCDD1L-DT and MINCR) can be novel targets for immunotherapy of KIRC patients.

Development of genomic instability-associated long non-coding RNA signature: A prognostic risk model of clear cell renal cell carcinoma

Purpose

Renal clear cell carcinoma (ccRCC) is the most lethal of all pathological subtypes of renal cell carcinoma (RCC). Genomic instability was recently reported to be related to the occurrence and development of kidney cancer. The biological roles of long non-coding RNAs (lncRNAs) in tumorigenesis have been increasingly valued, and various lncRNAs were found to be oncogenes or cancer suppressors. Herein, we identified a novel genomic instability-associated lncRNA (GILncs) model for ccRCC patients to predict the overall survival (OS).

Methods

The Cancer Genome Atlas (TCGA) database was utilized to obtain full transcriptome data, somatic mutation profiles, and clinical characteristics. The differentially expressed lncRNAs between the genome-unstable-like group (GU) and the genome-stable-like group (GS) were defined as GILncs, with |logFC| > 1 and an adjusted p-value< 0.05 for a false discovery rate. All samples were allocated into GU-like or GS-like types based on the expression of GILncs observed using hierarchical cluster analyses. A genomic instability-associated lncRNA signature (GILncSig) was constructed using parameters of the included lncRNAs. Quantitative real-time PCR analysis was used to detect the in vitro expression of the included lncRNAs. Validation of the risk model was performed by the log-rank test, time-dependent receiver operating characteristic (ROC) curves analysis, and multivariate Cox regression analysis.

Results

Forty-six lncRNAs were identified as GILncs. LINC00460, AL139351.1, and AC156455.1 were employed for GILncSig calculation based on the results of Cox analysis. GILncSig was confirmed as an independent predictor for OS of ccRCC patients. Additionally, it presented a higher efficiency and accuracy than other RCC prognostic models reported before.

Conclusion

GILncSig score was qualified as a critical indicator, independent of other clinical factors, for prognostic prediction of ccRCC patients.

A novel cuproptosis-related LncRNA signature: Prognostic and therapeutic value for acute myeloid leukemia

Background

Cuproptosis is a type of programmed cell death that is involved in multiple physiological and pathological processes, including cancer. We constructed a prognostic cuproptosis-related long non-coding RNA (lncRNA) signature for acute myeloid leukemia (AML).

Methods

RNA-seq and clinical data for AML patients were acquired from The Cancer Genome Atlas (TCGA) database. The cuproptosis-related prognostic lncRNAs were identified by co-expression and univariate Cox regression analysis. The least absolute shrinkage and selection operator (LASSO) was performed to construct a cuproptosis-related lncRNA signature, after which the AML patients were classified into two risk groups based on the risk model. Kaplan-Meier, ROC, univariate and multivariate Cox regression, nomogram, and calibration curves analyses were used to evaluate the prognostic value of the model. Then, expression levels of the lncRNAs in the signature were investigated in AML samples by quantitative polymerase chain reaction (qPCR). KEGG functional analysis, single-sample GSEA (ssGSEA), and the ESTIMATE algorithm were used to analyze the mechanisms and immune status between the different risk groups. The sensitivities for potential therapeutic drugs for AML were also investigated.

Results

Five hundred and three lncRNAs related to 19 CRGs in AML samples from the TCGA database were obtained, and 21 differentially expressed lncRNAs were identified based on the 2-year overall survival (OS) outcomes of AML patients. A 4-cuproptosis-related lncRNA signature for survival was constructed by LASSO Cox regression. High-risk AML patients exhibited worse outcomes. Univariate and multivariate Cox regression analyses demonstrated the independent prognostic value of the model. ROC, nomogram, and calibration curves analyses revealed the predictive power of the signature. KEGG pathway and ssGSEA analyses showed that the high-risk group had higher immune activities. Lastly, AML patients from different risk groups showed differential responses to various agents.

Conclusion

A cuproptosis-related lncRNA signature was established to predict the prognosis and inform on potential therapeutic strategies for AML patients.

Qualification of Necroptosis-Related lncRNA to Forecast the Treatment Outcome, Immune Response, and Therapeutic Effect of Kidney Renal Clear Cell Carcinoma

Background

Kidney renal clear cell carcinoma (KIRC) is considered as a highly immune infiltrative tumor. Necroptosis is an inflammatory programmed cell death associated with a wide range of diseases. Long noncoding RNAs (lncRNAs) play important roles in gene regulation and immune function. lncRNA associated with necroptosis could systematically explore the prognostic value, regulate tumor microenvironment (TME), etc.

Method

The patients' data was collected from TCGA datasets. We used the univariate Cox regression (UCR) to select prediction lncRNAs that are related to necroptosis. Meanwhile, risk models were constructed using LASSO Cox regression (LCR). Kaplan–Meier (KM) analysis, accompanied with receiver operating characteristic (ROC) curves, was performed to assess the independent risk factors of different clinical characteristics. The evaluated factors are age, gender, disease staging, grade, and their related risk score. Databases such as Gene Ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG), and Gene set enrichment analysis (GSEA) were used to search the probable biological characteristics that could influence the risk groups, containing signaling pathway and immue-related pathways. The single-sample gene set enrichment analysis (ssGSEA) was chosen to perform gene set variation analysis (GSVA), and the GSEABase package was selected to detect the immune and inflammatory infiltration profiles. The TIDE and IC₅₀ evaluation were used to estimate the effectiveness of clinical treatment on KIRC.

Results

Based on the above analysis, we have got a conclusion that patients who show high risk had higher immune infiltration, immune checkpoint expression, and poorer prognosis. We identified 19 novel prognostic necroptosis-related lncRNAs, which could offer opinions for a deeper study of KIRC.

Conclusion

The risk model we constructed makes it possible to predict the prognosis of KIRC patients and offers directions for further research on the prognostication and treatment strategies for KIRC.

Establishment of a prognostic ferroptosis- and immune-related long noncoding RNAs profile in kidney renal clear cell carcinoma

Background: Ferroptosis and immunity are novel treatments that target several cancers, including kidney renal clear cell carcinoma (KIRC). Long noncoding RNAs (lncRNAs) are an important class of gene expression regulators that play fundamental roles in the regulation of ferroptosis and immunity. We aimed to identify ferroptosis- and immune-related lncRNAs as biomarkers in patients with KIRC.

Methods: Corresponding data for each patient with KIRC were obtained from The Cancer Genome Atlas (TCGA) database. Univariate and multivariate Cox regression analyses were used to identify candidate biomarkers followed by least absolute shrinkage and selection operator (LASSO) regression analyses, weighted gene coexpression network analysis (WGCANA), and gene set enrichment analysis (GSEA).

Results: Three ferroptosis- and immune-related differentially expressed lncRNAs (FI-DELs) (AC124854.1, LINC02609, and ZNF503-AS2) were markedly and independently correlated with the overall survival (OS) of patients with KIRC. The area under the curve (AUC) value of the prognostic model in the entire group using the three FI-DELs was > 0.70. The sensitivity and specificity of the diagnostic model using the three FI-DELs were 0.8586 and 0.9583, respectively.

Conclusion: The present study found that AC124854.1, LINC02609, and ZNF503-AS2 were considerably and independently correlated with the OS of patients with KIRC, suggesting that the three FI-DELs could be used as prognostic and diagnostic biomarkers for patients with KIRC.

Epidemiology and Prevention of Renal Cell Carcinoma

With 400,000 diagnosed and 180,000 deaths in 2020, renal cell carcinoma (RCC) accounts for 2.4% of all cancer diagnoses worldwide. The highest disease burden developed countries, primarily in Europe and North America. Incidence is projected to increase in the future as more countries shift to Western lifestyles. Risk factors for RCC include fixed factors such as gender, age, and hereditary diseases, as well as intervening factors such as smoking, obesity, hypertension, diabetes, diet and alcohol, and occupational exposure. Intervening factors in primary prevention, understanding of congenital risk factors and the establishment of early diagnostic tools are important for RCC. This review will discuss RCC epidemiology, risk factors, and biomarkers involved in reducing incidence and improving survival.

Novel Prognosis and Therapeutic Response Model of Immune-Related lncRNA Pairs in Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common type of renal carcinoma. It is particularly important to accurately judge the prognosis of patients. Since most tumor prediction models depend on the specific expression level of related genes, a better model therefore needs to be constructed. To provide an immune-related lncRNA (irlncRNAs) tumor prognosis model that is independent of the specific gene expression levels, we first downloaded and sorted out the data on ccRCC in the TCGA database and screened irlncRNAs using co-expression analysis and then obtained the differently expressed irlncRNA (DEirlncRNA) pairs by means of univariate analysis. In addition, we modified LASSO penalized regression. Subsequently, the ROC curve was drawn, and we compared the area under the curve, calculated the Akaike information standard value of the 5-year receiver operating characteristic curve, and determined the cut-off point to establish the best model to distinguish the high- or low-disease-risk group of ccRCC. Subsequently, we reassessed the model from the perspectives of survival, clinic-pathological characteristics, tumor-infiltrating immune cells, chemotherapeutics efficacy, and immunosuppressed biomarkers. A total of 17 DEirlncRNAs pairs (AL031710.1|AC104984.5, AC020907.4|AC127-24.4,AC091185.1|AC005104.1, AL513218.1|AC079015.1, AC104564.3|HOXB-AS3, AC003070.1|LINC01355, SEMA6A-AS1|CR936218.1, AL513327.1|AS005785.1, AC084876.1|AC009704.2, IGFL2-AS1|PRDM16-DT, AC011462.4|MMP25-AS1, AL662844.3I|TGB2-AS1, ARHGAP27P1|AC116914.2, AC093788.1|AC007098.1, MCF2L-AS1|AC093001.1, SMIM25|AC008870.2, and AC027796.4|LINC00893) were identified, all of which were included in the Cox regression model. Using the cut-off point, we can better distinguish patients according to different factors, such as survival status, invasive clinic-pathological features, tumor immune infiltration, whether they are sensitive to chemotherapy or not, and expression of immunosuppressive biomarkers. We constructed the irlncRNA model by means of pairing, which can better eliminate the dependence on the expression level of the target genes. In other words, the signature established by pairing irlncRNA regardless of expression levels showed promising clinical prediction value.

Identification of Ferroptosis-Associated Long Noncoding RNA Prognostic Model and Tumor Immune Microenvironment in Thyroid Cancer

Background

Thyroid cancer (TC) is a rapidly increasing incidence of endocrine malignancies, occupying 3% of new cancer incidence, of which 10% has a heterogeneous prognosis. Ferroptosis is a form of cell death distinct from apoptosis, which involves antitumor drug-related research. Long noncoding RNAs (lncRNAs) could affect cancer prognosis by regulating the ferroptosis; thus, ferroptosis-associated lncRNAs are emerging as prospective biomarkers for cancer therapy and prognosis. However, the prognostic factors of ferroptosis-associated lncRNAs in this solid tumor and their mechanisms remain unknown.

Methods

The TC lncRNA data were extracted from RNA sequencing files of The Cancer Genome Atlas (TCGA). Then, we performed a two-cluster analysis and grouped 502 patients with TC in a 7 : 3 ratio. Both the least absolute shrinkage and selection operator (LASSO) regression and Cox regression analysis were conducted to create and validate the ferroptosis-associated lncRNA prognostic model (Ferr-LPM). Based on the median Ferr-LPM-based risk score (LPM_score) of the training cohort, we categorized patients into high and low LPM_score groups, which were then subjected to prognostic correlation and difference analysis. We also created a nomogram and assessed its predictive ability. Furthermore, immune-related mechanisms were investigated by analyzing the tumor immune microenvironment (TIME) and applying algorithms such as CIBERSROT.

Results

We built a highly accurate nomogram to promote the clinical applicability of Ferr-LPM. The area under the receiver operating characteristic curve (AUC-ROC) reached above 0.9. Survival analysis suggested that when the Ferr-LPM score was higher, the overall survival (OS) of patients within this group was shorter. Meanwhile, we found a strong association between Ferr-LPM and TIME. Interestingly, the LPM_score was inversely proportional to the tumor purity but positively related to immune checkpoint blockade (ICB) response.

Conclusion

We constructed a novel ferroptosis-associated lncRNA nomogram that could highly predict the prognosis of TC patients. Ferroptosis-associated lncRNAs might possess potential functions in regulating TIME, and lncRNAs provide TC patients with new prognostic biomarkers and therapeutic targets.

Long non-coding RNAs (lncRNAs); roles in tumorigenesis and potentials as biomarkers in cancer diagnosis

New research has indicated that long non-coding RNAs (lncRNAs) play critical roles in a broad range of biological processes, including the pathogenesis of many complex human diseases, including cancer. The detailed regulation mechanisms of many lncRNAs in cancer initiation and progression have yet to be discovered, even though a few of lncRNAs' functions in cancer have been characterized. In the present study, we summarize recent advances in the mechanisms and functions of lncRNAs in cancer. We focused on the roles of newly-identified lncRNAs as oncogenes and tumor suppressors, as well as the potential pathways these molecules could play. The paper also discusses their potential uses as biomarkers for the diagnosis and prognosis of cancer.

Pyroptosis-Related lncRNA Prognostic Model for Renal Cancer Contributes to Immunodiagnosis and Immunotherapy

Background

Renal clear cell cancer (ccRCC) is one of the most common cancers in humans. Thus, we aimed to construct a risk model to predict the prognosis of ccRCC effectively.

Methods

We downloaded RNA sequencing (RNA-seq) data and clinical information of 539 kidney renal clear cell carcinoma (KIRC) patients and 72 normal humans from The Cancer Genome Atlas (TCGA) database and divided the data into training and testing groups randomly. Pyroptosis-related lncRNAs (PRLs) were obtained through Pearson correlation between pyroptosis genes and all lncRNAs (p &lt; 0.05, coeff &gt; 0.3). Univariate and multivariate Cox regression analyses were then performed to select suitable lncRNAs. Next, a novel signature was constructed and evaluated by survival analysis and ROC analysis. The same observation applies to the testing group to validate the value of the signature. By gene set enrichment analysis (GSEA), we predicted the underlying signaling pathway. Furthermore, we calculated immune cell infiltration, immune checkpoint, the T-cell receptor/B-cell receptor (TCR/BCR), SNV, and Tumor Immune Dysfunction and Exclusion (TIDE) scores in TCGA database. We also validated our model with an immunotherapy cohort. Finally, the expression of PRLs was validated by quantitative PCR (qPCR).

Results

We constructed a prognostic signature composed of six key lncRNAs (U62317.1, MIR193BHG, LINC02027, AC121338.2, AC005785.1, AC156455.1), which significantly predict different overall survival (OS) rates. The efficiency was demonstrated using the receiver operating characteristic (ROC) curve. The signature was observed to be an independent prognostic factor in cohorts. In addition, we found the PRLs promote the tumor progression via immune-related pathways revealed in GSEA. Furthermore, the TCR, BCR, and SNV data were retrieved to screen immune features, and immune cell scores were calculated to measure the effect of the immune microenvironment on the risk model, indicating that high- and low-risk scores have different immune statuses. The TIDE algorithm was then used to predict the immune checkpoint blockade (ICB) response of our model, and subclass mapping was used to verify our model in another immunotherapy cohort data. Finally, qPCR validates the PRLs in cell lines.

Conclusion

This study provided a new risk model to evaluate ccRCC and may be pyroptosis-related therapeutic targets in the clinic.

Correlation Between Immune Cell Infiltration and PD-L1 Expression and Immune-Related lncRNA Determination in Triple-Negative Breast Cancer

As a key element of the tumor microenvironment (TME), immune cell infiltration (ICI) is a frequently observed histologic finding in people with triple-negative breast cancer (TNBC), and it is linked to immunotherapy sensitivity. Nonetheless, the ICI in TNBC, to the best of our knowledge, has not been comprehensively characterized. In our current work, computational algorithms based on biological data from next-generation sequencing were employed to characterize ICI in a large cohort of TNBC patients. We defined various ICI patterns by unsupervised clustering and constructed the ICI scores using the principal component analysis (PCA). We observed patients with different clustering patterns had distinct ICI profiles and different signatures of differentially expressed genes. Patients with a high ICI score tended to have an increased PD-L1 expression and improved outcomes, and these patients were associated with decreased tumor mutational burden (TMB). Interestingly, it was showed that patients with high TMB exhibited an ameliorated overall survival (OS) than patients with low TMB. Furthermore, TMB scores only affected the prognosis of TNBC patients in the low-ICI score group but not in the high group. Finally, we identified a new immune-related lncRNA (irlncRNA) signature and established a risk model for the TNBC prognosis prediction. In addition, the high-risk group was related to poor prognosis, a high infiltration level of plasma B cells, monocytes, M2 macrophages, and neutrophils and a low PD-L1 expression. Therefore, the characterization and systematic evaluation of ICI patterns might potentially predict the prognosis and immunotherapy response in TNBC patients.

Immune checkpoints related-LncRNAs can identify different subtypes of lung cancer and predict immunotherapy and prognosis

BACKGROUND

Non-small cell lung cancer is the most common subtype of lung cancer in the world. However, the survival rate of non-small cell lung cancer patients remains low currently. Immune checkpoint and long non-coding RNAs are emerging as critical roles in prognostic significance and the immunotherapeutic response of non-small cell lung cancer. It is critical to discern LncRNAs related with immune checkpoints in patients with Non-small cell lung cancer.

METHODS

In this study, immune checkpoint-linked LncRNAs were determined and achieved by the co-expression analysis. Immune checkpoint-linked LncRNAs with noteworthy prognostic value (P < 0.05) gained were next utilized to separate into two cluster by non-negative matrix factorization (NMF). Univariate and a least absolute shrinkage and selection operator were applied to construct an immune checkpoint-linked LncRNAs model. Kaplan-Meier analysis, Gene Set Enrichment Analysis, and the nomogram were utilized to investigate the LncRNAs model. Lastly, the capability immunotherapy and chemotherapy prediction value of this risk model were also estimated.

RESULTS

The model consisting of ten immune checkpoint-related LncRNAs was acknowledged to be a self-determining predictor of prognosis. Through regrouping the NSCLC patients by this model, difference between them more efficiently on immunotherapeutic response, tumor microenvironment and chemotherapy response could be discovered. This risk model related to the immune checkpoint-based LncRNAs may have an excellent clinical prediction for prognosis and the immunotherapeutic response in patients with NSCLC.

CONCLUSIONS

We performed an integrative analysis of LncRNAs linked with immune checkpoints and emphasized the significance of NSCLC subtypes classification, immune checkpoints related LncRNAs in estimating the tumor microenvironment score, immune cell infiltration of the tumor, immunotherapy, and chemotherapy response.

Immune-related lncRNA pairs as novel signature to predict prognosis and immune landscape in melanoma patients

To investigate immune-related long non-coding RNA (irlncRNA) signatures for predicting survival and the immune landscape in melanoma patients.We retrieved gene expression files from The Cancer Genome Atlas and the Genotype-Tissue Expression database and extracted all the long non-coding RNAs from the original data. Then, we selected immune-related long non-coding RNAs (irlncRNAs) using co-expression networks and screened differentially expressed irlncRNAs (DEirlncRNAs) to form pairs. We also performed univariate analysis and Least absolute shrinkage and selection operator (LASSO) penalized regression analysis to identify prognostic DEirlncRNA pairs, constructed receiver operating characteristic curves, compared the areas under the curves, and calculated the optimal cut-off point to divide patients into high-risk and low-risk groups. Finally, we performed multivariate Cox regression analysis, Kaplan-Meier (K-M) survival analysis, clinical correlation analysis, and investigated correlations with tumor-infiltrating immune cells, chemotherapeutic effectiveness, and immunogene biomarkers.A total of 297 DEirlncRNAs were identified, of which 16 DEirlncRNA pairs were associated with prognosis in melanoma. After grouping patients by the optimal cut-off value, we could better distinguish melanoma patients with different survival outcomes, clinical characteristics, tumor immune status changes, chemotherapeutic drug sensitivity, and specific immunogene biomarkers.The DEirlncRNA pairs showed potential as novel biomarkers to predict the prognosis of melanoma patients. Furthermore, these DEirlncRNA pairs could be used to evaluate treatment efficacy in the future.

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

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

An immune-related lncRNA risk coefficient model to predict the outcomes in clear cell renal cell carcinoma

Objective: Using model algorithms, we constructed an immune-related long non-coding RNAs (lncRNAs) risk coefficient model to predict outcomes for patients with clear cell renal cell carcinoma (ccRCC) to understand the infiltration of tumor immune cells and the sensitivity to immune-targeted drugs.

Methods: Open genes data were downloaded from The Cancer Genome Atlas and The Immunology Database and Analysis Portal, and immune-related lncRNAs were obtained through Pearson correlation analysis. R language software was used to obtain differentially expressed immune-related lncRNAs and immune-related lncRNA pairs. The model was constructed using least absolute shrinkage and selector operation regression analysis, and receiver operator characteristic curves were drawn. The Akaike information criterion was used to distinguish the high-risk from the low-risk group. We also conducted correlation analysis for the high- and low-risk subgroups.

Results: We identified 27 immune-related lncRNAs pairs, 16 of which were included in the model construction. After merging clinical data, the areas under the curve of 1 -year, 3-year, and 5-year survival times of ccRCC patients were 0.867, 0.832, and 0.838, respectively. Subgroup analyses were conducted according to the cut-off value. We found that the high-risk group was associated with poor outcomes. The risk score and tumor stage were independent predictors of the outcome of ccRCC. The risk model predicted specific immune cell infiltration, immune checkpoint gene expression levels, and high-risk groups more sensitive to sunitinib targeted therapy.

Conclusion: We obtained prognostic-related novel ccRCC markers and risk model that predicts the outcome of patients with ccRCC and helps identify those who can benefit from sunitinib.

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

Purpose

This study aimed to evaluate the functions of critical N6-methyladenosine (m6A)-related long non-coding RNAs (lncRNAs) and their correlations with immunotherapeutic targets in clear cell renal cell carcinoma (ccRCC).

Methods

m6A-related lncRNAs were analyzed using the dataset from The Cancer Genome Atlas database via Pearson correlation analysis. Then, their prognostic functions in patients with ccRCC were determined via univariate Cox analysis. A prognostic m6A-related lncRNA signature (MRLS) in ccRCC was established using the least absolute shrinkage and selection operator (LASSO) Cox regression model. In addition, the correlations between these prognostic m6A-related lncRNAs with immune checkpoints were further evaluated in clinical samples.

Results

MRLS was established by the LASSO Cox regression model on the basis of seven prognostic m6A-related lncRNAs. The risk score for each patient was calculated using the MRLS model, and the patients were further stratified into high- and low-risk subgroups. The MRLS model was validated with a robust prognostic ability by the stratification analysis. On the basis of age, grade, stage, and risk score, a nomogram was developed with a strong reliability in forecasting the overall survival percentages of the patients with ccRCC. Moreover, seven prognostic m6A-related lncRNAs enrolled in the MRLS model were found to be correlated with various immunotherapeutic targets, namely, PD-1, PD-L1, CTLA4, and LAG3, and the expression levels of which in the high-risk subgroup were significantly higher than those in the low-risk subgroup. The significant correlations between LINC00342 and the aforementioned immunotherapeutic targets were also confirmed in clinical samples.

Conclusion

In this study, seven m6A-related lncRNAs were identified as potential biomarkers for forecasting the prognosis of patients with ccRCC and evaluating the efficacy of immunotherapy for these patients. Furthermore, a prognostic and predictive MRLS model with a high reliability was constructed to predict the overall survival probability of patients with ccRCC.

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.

Long non-coding RNA profile study identifies an immune-related lncRNA prognostic signature for prostate adenocarcinoma

Prostate adenocarcinoma (PRAD) is the highest incidence rate of male urogenital morbidity worldwide. Long non-coding RNAs (lncRNAs), as a significant class of gene expression regulators, play a critical role in immune regulation. The purpose of this study is to explore a new immune related lncRNA signature to exactly predict the prognosis of PRAD patients. In this study, we conducted a genome-wide comparative analysis of lncRNA expression profiles in 532 patients with PRAD from the Cancer Genome Atlas (TCGA) database. The immune-related lncRNAs were identified by Cox regression model, and then a new five immune-related lncRNAs signature (FRMD6-AS2, AC008770.3, AC109460.3, AC011899.2, and AC008063.1) were constructed, which could predict the prognosis of PRAD patients. Univariate and multivariate Cox regression analysis showed that the signature could be an independent prognostic indicator of overall survival (OS). Through further study of different clinic-pathological parameters, we found that PRAD samples can be divided into high-risk groups with shorter OS and low-risk groups with longer OS by the signature. Principal component analysis showed that five immune-related lncRNA signature could distinguish the high-risk group from low-risk group in view of the immune-related lncRNAs. The difference of immune status between the two groups was observed by gene set enrichment analysis and the ESTIMATE algorithm. Except FRMD6-AS2, the expression of the other 4 lncRNAs were remarkably up-regulated in tumor tissues. In conclusion, the identified five immune-related lncRNAs signature had important clinical significance in prognosis prediction, and can be used as potential immunotherapy targets for PRAD patients.

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.

Prognostic Value of an m5C RNA Methylation Regulator-Related Signature for Clear Cell Renal Cell Carcinoma

Purpose

Clear cell renal cell carcinoma (ccRCC) is highly heterogeneous and is one of the most lethal types of cancer within the urinary system. Aberrant expression of 5-methylcytosine (m⁵C) RNA methylation regulators has been shown to result in occurrence and progression of tumors. However, the role of these regulators in ccRCC remains unclear.

Materials and Methods

We extracted RNA sequencing expression data with corresponding clinical information of patients with ccRCC from The Cancer Genome Atlas (TCGA) database. We then compared the expression profiles of m⁵C RNA methylation regulators between normal and ccRCC tissues, and determined different subtypes through consensus clustering analysis. In addition, we constructed a prognostic signature and evaluated it using a range of bioinformatics approaches. The expression of signature-related genes was subsequently verified in the clinical samples using qRT-PCR.

Results

We identified 12 differentially expressed m⁵C RNA methylation regulators between cancer and normal control samples. Two clusters of patients with ccRCC and diverse clinicopathological characteristics and prognoses were then determined through consensus clustering analysis. Functional annotations revealed that m⁵C RNA regulators were significantly correlated with the ccRCC progression. Moreover, we constructed a four-gene risk score signature (comprised of NOP2, NSUN4, NSUN6, and TET2) and divided the patients with ccRCC into high- and low-risk groups based on the median risk score. The risk score was associated with clinicopathological features and was an independent prognostic indicator of ccRCC. Our stratified analysis results suggest that the signature has high prognostic value. Based on qRT-PCR results, the NOP2 and NSUN4 mRNA expressions were higher and those of NSUN6 and TET2 were lower in ccRCC tissues than in normal tissues.

Conclusion

Our results demonstrate that m⁵C RNA methylation regulators may affect ccRCC progression and could be exploited for diagnostic and prognostic purposes.

Epigenetic Biomarkers of Renal Cell Carcinoma for Liquid Biopsy Tests

Renal cell carcinomas (RCC) account for 2–3% of the global cancer burden and are characterized by the highest mortality rate among all genitourinary cancers. However, excluding conventional imagining approaches, there are no reliable diagnostic and prognostic tools available for clinical use at present. Liquid biopsies, such as urine, serum, and plasma, contain a significant amount of tumor-derived nucleic acids, which may serve as non-invasive biomarkers that are particularly useful for early cancer detection, follow-up, and personalization of treatment. Changes in epigenetic phenomena, such as DNA methylation level, expression of microRNAs (miRNAs), and long noncoding RNAs (lncRNAs), are observed early during cancer development and are easily detectable in biofluids when morphological changes are still undetermined by conventional diagnostic tools. Here, we reviewed recent advances made in the development of liquid biopsy-derived DNA methylation-, miRNAs- and lncRNAs-based biomarkers for RCC, with an emphasis on the performance characteristics. In the last two decades, a mass of circulating epigenetic biomarkers of RCC were suggested, however, most of the studies done thus far analyzed biomarkers selected from the literature, used relatively miniature, local, and heterogeneous cohorts, and suffered from a lack of sufficient validations. In summary, for improved translation into the clinical setting, there is considerable demand for the validation of the existing pool of RCC biomarkers and the discovery of novel ones with better performance and clinical utility.

Identification and validation of a novel redox-related lncRNA prognostic signature in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is one of the main causes of cancer deaths globally. Redox is emerging as a crucial contributor to the pathophysiology of LUAD, which can be regulated by long non-coding RNAs (lncRNAs). The aim of our research is to identify a novel redox-related lncRNA prognostic signature (redox-LPS) for better prediction of LUAD prognosis. 535 LUAD samples from The Cancer Genome Atlas (TCGA) database and 226 LUAD samples from the Gene Expression Omnibus (GEO) database were included in our study. 67 redox genes and 313 redox-related lncRNAs were identified. After performing LASSO-Cox regression analysis, a redox-LPS consisting of four lncRNAs (i.e., CRNDE, CASC15, LINC01137, and CYP1B1-AS1) was developed and validated. Our redox-LPS was superior to another three established models in predicting survival probability of LUAD patients. Univariate and multivariate Cox regression analysis revealed that risk score and stage were independent prognostic indicators. A nomogram plot including risk score and stage was constructed to predict survival probability of LUAD patients; this was further verified by calibration curves. Functional enrichment analysis and gene set enrichment analysis, were performed to determine the differences in cellular processes and signaling pathways between the high – and low-risk subgroups. A variety of algorithms (such as single-sample gene set enrichment analysis and CIBERSOFT) were conducted to uncover the landscape of tumor immune microenvironment in the high- and low-risk subgroups. In conclusion, a novel independent redox-LPS was constructed and validated for LUAD patients, which might provide new insights for clinical decision-making and precision medicine.

Genome instability-related long non-coding RNA in clear renal cell carcinoma determined using computational biology

Background

There is evidence that long non-coding RNA (lncRNA) is related to genetic stability. However, the complex biological functions of these lncRNAs are unclear.

Method

TCGA - KIRC lncRNAs expression matrix and somatic mutation information data were obtained from TCGA database. “GSVA” package was applied to evaluate the genomic related pathway in each samples. GO and KEGG analysis were performed to show the biological function of lncRNAs-mRNAs. “Survival” package was applied to determine the prognostic significance of lncRNAs. Multivariate Cox proportional hazard regression analysis was applied to conduct lncRNA prognosis model.

Results

In the present study, we applied computational biology to identify genome-related long noncoding RNA and identified 26 novel genomic instability-associated lncRNAs in clear cell renal cell carcinoma. We identified a genome instability-derived six lncRNA-based gene signature that significantly divided clear renal cell samples into high- and low-risk groups. We validated it in test cohorts. To further elucidate the role of the six lncRNAs in the model’s genome stability, we performed a gene set variation analysis (GSVA) on the matrix. We performed Pearson correlation analysis between the GSVA scores of genomic stability-related pathways and lncRNA. It was determined that LINC00460 and LINC01234 could be used as critical factors in this study. They may influence the genome stability of clear cell carcinoma by participating in mediating critical targets in the base excision repair pathway, the DNA replication pathway, homologous recombination, mismatch repair pathway, and the P53 signaling pathway.

Conclusion subsections

These data suggest that LINC00460 and LINC01234 are crucial for the stability of the clear cell renal cell carcinoma genome.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08356-9.

Identification of a Novel Protein-Based Signature to Improve Prognosis Prediction in Renal Clear Cell Carcinoma

Background

Clear cell renal cell carcinoma (ccRCC) is one of the most common types of malignant adult kidney cancer, and its incidence and mortality are not optimistic. It is well known that tumor-related protein markers play an important role in cancer detection, prognosis prediction, or treatment selection, such as carcinoembryonic antigen (CEA), programmed cell death 1 (PD-1), programmed cell death 1 ligand 1 (PD-L1), and cytotoxic T lymphocyte antigen 4 (CTLA-4), so a comprehensive analysis was performed in this study to explore the prognostic value of protein expression in patients with ccRCC.

Materials and Methods

Protein expression data were obtained from The Cancer Proteome Atlas (TCPA), and clinical information were downloaded from The Cancer Genome Atlas (TCGA). We selected 445 patients with complete information and then separated them into a training set and testing set. We performed univariate, least absolute shrinkage and selection operator (LASSO) Cox analyses to find prognosis-related proteins (PRPs) and constructed a protein signature. Then, we used stratified analysis to fully verify the prognostic significance of the prognostic-related protein signature score (PRPscore). Besides, we also explored the differences in immunotherapy response and immune cell infiltration level in high and low score groups. The consensus clustering analysis was also performed to identify potential cancer subgroups.

Results

From the training set, a total of 233 PRPs were selected, and a seven-protein signature was constructed, including ACC1, AR, MAPK, PDK1, PEA15, SYK, and BRAF. Based on the PRPscore, patients could be divided into two groups with significantly different overall survival rates. Univariate and multivariate Cox regression analyses proved that this signature was an independent prognostic factor for patients (P < 0.001). Moreover, the signature showed a high ability to distinguish prognostic outcomes among subgroups, and the low score group had a better prognosis (P < 0.001) and better immunotherapy response (P = 0.003) than the high score group.

Conclusion

We constructed a novel protein signature with robust predictive power and high clinical value. This will help to guide the disease management and individualized treatment of ccRCC patients.

Construction and validation of an autophagy-related long noncoding RNA signature for prognosis prediction in kidney renal clear cell carcinoma patients

Purpose

The purpose of this study was to identify autophagy‐associated long noncoding RNAs (ARlncRNAs) using the kidney renal clear cell carcinoma (KIRC) patient data from The Cancer Genome Atlas (TCGA) database and to construct a prognostic risk‐related ARlncRNAs signature to accurately predict the prognosis of KIRC patients.

Methods

The KIRC patient data were originated from TCGA database and were classified into a training set and testing set. Seven prognostic risk‐related ARlncRNAs, identified using univariate, lasso, and multivariate Cox regression analysis, were used to construct prognostic risk‐related signatures. Kaplan–Meier curves and receiver operating characteristic (ROC) curves as well as independent prognostic factor analysis and correlation analysis with clinical characteristics were utilized to evaluate and verify the specificity and sensitivity of the signature in training set and testing set, respectively. Two nomograms were established to predict the probable 1‐, 3‐, and 5‐year survival of the KIRC patients. In addition, the lncRNA‐mRNA co‐expression network was constructed and Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to identify biological functions of ARlncRNAs.

Results

We constructed and verified a prognostic risk‐related ARlncRNAs signature in training set and testing set, respectively. We found the survival time of KIRC patients with low‐risk scores was significantly better than those with high‐risk scores in training set and testing set. ROC curves suggested that the area under the ROC (AUC) value for prognostic risk score signature was 0.81 in training set and 0.705 in testing set. And AUC values corresponding to 1‐, 3‐, and 5 years of OS were 0.809, 0.753, and 0.794 in training set and 0.698, 0.682, and 0.754 in testing set, respectively. We established the two nomograms that confirmed high C‐index and accomplished good prediction accuracy.

Conclusions

We constructed a prognostic risk‐related ARlncRNAs signature that could accurately predict the prognosis of KIRC patients.