Identification and Experimental Validation of Immune-Associate lncRNAs for Predicting Prognosis in Cervical Cancer

Plasma-derived exosomal long noncoding RNAs of pancreatic cancer patients as novel blood-based biomarkers of disease

BACKGROUND

Pancreatic cancer (PaCa) is one of the most intractable and fatal malignancies and is associated with the dysregulation of long noncoding RNAs (lncRNAs), which are a large class of noncoding RNAs larger than 200 nt that act as competing endogenous RNAs or sponges for miRNAs to induce tumour biological behaviours. However, their clinical value in treating pancreatic cancer has been poorly explained, but they are essential for improving the prognosis of PaCa patients.

METHODS

We analysed the plasma-derived exosomal lncRNA profiles of PaCa patients by using whole-transcriptome sequencing analysis and identified significantly differentially expressed lncRNAs, including LINC01268, LINC02802, AC124854.1, and AL132657.1. In the current study, the expression levels of four plasma-derived exosomal lncRNAs in PaCa plasma were validated via quantitative real-time polymerase chain reaction (qRT‒PCR). The relationship between the expression of the four lncRNAs and the clinicopathological features of patients with PaCa was also evaluated.

RESULTS

We demonstrated that exosomal LINC01268, LINC02802, AC124854.1 and AL132657.1 were highly expressed in PaCa plasma compared with those in normal controls; moreover, they were positively correlated with the serum expression of carbohydrate antigen 19-9 (CA19-9). The receiver operating characteristic curves (AUCs) of the four lncRNAs were 0.8421, 0.6544, 0.7190, and 0.6321, and the AUC value of the combination of the four exosomal lncRNAs increased to 0.8476, with a sensitivity of 0.72 and specificity of 0.89. These results suggested that the plasma-derived exosomal genes LINC01268, LINC02802, AC124854.1, and AL132657.1 may be novel diagnostic markers for PaCa.

CONCLUSIONS

Our research demonstrated that the plasma-derived exosomal lncRNAs of PaCa patients are novel blood-based biomarkers of disease.

Bioinformatic analysis indicated that STARD4-AS1 might be a novel ferroptosis-related biomarker of oral squamous cell carcinoma

Background

Oral squamous cell carcinoma (OSCC) stands as the predominant form of oral cancer, marked by a poor prognosis. Ferroptosis, a type of programmed cell death, plays a critical role in the initiation and progression of various cancers. Long non-coding RNAs (lncRNAs) are prominent in modulating cancer development. Nevertheless, the prognostic significance of ferroptosis-related lncRNAs (FRLs) in OSCC remains inadequately explored. This study aims to develop a predictive signature based on FRLs to forecast the prognosis of OSCC patients.

Methods

We gathered expression profiles of FRLs along with clinical data from The Cancer Genome Atlas (TCGA) and FerrDb databases. A prognostic model based on 10 FRLs were constructed using Cox regression analyses with LASSO algorithms, and their predictive power was evaluated. Then, the model was used to investigate functional enrichment, immune landscape, m6A genes, somatic variations, and drug response in different risk cohorts of patients. Finally, the expression and function of STARD4-AS1 (steroidogenic acute regulator protein-related lipid transfer domain containing 4-antisense RNA 1), a potential prognostic marker for OSCC screening based on our bioinformatics analysis, were investigated in vitro.

Results

We developed a signature comprising 10 FRLs to stratify patients into two risk cohorts according to their calculated risk scores. Patients classified as high-risk exhibited significantly poorer prognoses compared to those in the low-risk cohort. Furthermore, survival analysis, patient risk heat plot, and risk curve verified the accuracy of the signature. The role of this signature in OSCC was well investigated using immune microenvironment, mutational, and gene set enrichment analysis (GSEA). Moreover, seven drugs, including cisplatin and docetaxel, were identified as potential treatments for patients with high-risk cancers. In addition, the knockdown of STARD4-AS1 in OSCC cell lines markedly inhibited cell proliferation and migration and induced ferroptosis.

Conclusion

Using this signature may improve overall survival predictions in OSCC, throwing new light on immunotherapies and targeted therapies. Moreover, STARD4-AS1 might regulate the process of ferroptosis and could be used as a novel biomarker of OSCC.

GEnDDn: An lncRNA-Disease Association Identification Framework Based on Dual-Net Neural Architecture and Deep Neural Network

Accumulating studies have demonstrated close relationships between long non-coding RNAs (lncRNAs) and diseases. Identification of new lncRNA-disease associations (LDAs) enables us to better understand disease mechanisms and further provides promising insights into cancer targeted therapy and anti-cancer drug design. Here, we present an LDA prediction framework called GEnDDn based on deep learning. GEnDDn mainly comprises two steps: First, features of both lncRNAs and diseases are extracted by combining similarity computation, non-negative matrix factorization, and graph attention auto-encoder, respectively. And each lncRNA-disease pair (LDP) is depicted as a vector based on concatenation operation on the extracted features. Subsequently, unknown LDPs are classified by aggregating dual-net neural architecture and deep neural network. Using six different evaluation metrics, we found that GEnDDn surpassed four competing LDA identification methods (SDLDA, LDNFSGB, IPCARF, LDASR) on the lncRNADisease and MNDR databases under fivefold cross-validation experiments on lncRNAs, diseases, LDPs, and independent lncRNAs and independent diseases, respectively. Ablation experiments further validated the powerful LDA prediction performance of GEnDDn. Furthermore, we utilized GEnDDn to find underlying lncRNAs for lung cancer and breast cancer. The results elucidated that there may be dense linkages between IFNG-AS1 and lung cancer as well as between HIF1A-AS1 and breast cancer. The results require further biomedical experimental verification. GEnDDn is publicly available at https://github.com/plhhnu/GEnDDn.

LncRNAs exhibit subtype-specific expression, survival associations, and cancer-promoting effects in breast cancer

Long non-coding RNAs (lncRNAs) play important roles in cancer progression, influencing processes such as invasion, metastasis, and drug resistance. Their reported cell type-dependent expression patterns suggest the potential for specialized functions in specific contexts. In breast cancer, lncRNA expression has been associated with different subtypes, highlighting their relevance in disease heterogeneity. However, our understanding of lncRNA function within breast cancer subtypes remains limited, warranting further investigation. We conducted a comprehensive analysis using the TANRIC dataset derived from the TCGA-BRCA cohort, profiling the expression, patient survival associations and immune cell type correlations of 12,727 lncRNAs across subtypes. Our findings revealed subtype-specific associations of lncRNAs with patient survival, tumor infiltrating lymphocytes and other immune cells. Targeting of lncRNAs exhibiting subtype-specific survival associations and expression in a panel of breast cancer cells demonstrated a selective reduction in cell proliferation within their associated subtype, supporting subtype-specific functions of certain lncRNAs. Characterization of HER2 + -specific lncRNA LINC01269 and TNBC-specific lncRNA AL078604.2 showed nuclear localization and altered expression of hundreds of genes enriched in cancer-promoting processes, including apoptosis, cell proliferation and immune cell regulation. This work emphasizes the importance of considering the heterogeneity of breast cancer subtypes and the need for subtype-specific analyses to fully uncover the relevance and potential impact of lncRNAs. Collectively, these findings demonstrate the contribution of lncRNAs to the distinct molecular, prognostic, and cellular composition of breast cancer subtypes.

Effects of BRD4 inhibitor JQ1 on the expression profile of super-enhancer related lncRNAs and mRNAs in cervical cancer HeLa cells

Objective

To investigate the effects of bromine domain protein 4 (BRD4) inhibitor JQ1 on the expression profile of super-enhancer-related lncRNAs (SE-lncRNAs) and mRNAs in cervical cancer (CC) HeLa-cells.

Methods

The CCK8 method was implemented to detect the inhibitory effect of JQ1 on HeLa cells and explore the best inhibitory concentration. Whole transcriptome sequencing was performed to detect the changes of lncRNAs and mRNAs expression profiles in cells of the JQ1 treatment group and control group, respectively. The differentially expressed SE-lncRNAs were obtained by matching, while the co-expressed mRNAs were obtained by Pearson correlation analysis.

Results

The inhibitory effect of JQ1 on HeLa cell proliferation increased significantly with increasing concentration and treatment time (P < 0.05). Under the experimental conditions of three concentrations of 0.01, 0.1 and 1 μmol/L of JQ1 on HeLa cells at 24, 48, 72 and 120 h, 1 μmol/L of JQ1 at 72 and 120 h had the same cell viability and the strongest cell proliferation inhibition. In order to understand the inhibitory mechanism of JQ1 on HeLa cells, this study analyzed the expression profile differences from the perspective of SE-lncRNAs and mRNAs. A total of 162 SE-lncRNAs were identified, of which 8 SE-lncRNAs were down-regulated and seven SE-lncRNAs were up-regulated. A total of 418 differentially expressed mRNAs related to SE-lncRNAs were identified, of which 395 mRNAs had positive correlation with 12 SE-lncRNAs and 408 mRNAs had negative correlation with 15 SE-lncRNAs.

Conclusion

JQ1 can significantly inhibit the proliferation of HeLa cells and affect the expression profile of SE-lncRNAs and mRNAs.

Disulfideptosis-associated lncRNAs reveal features of prognostic, immune escape, tumor mutation, and tumor malignant progression in renal clear cell carcinoma

PURPOSE

Investigating the role of lncRNAs associated with the latest cell death mode (Disulfideptosis) in renal clear cell carcinoma, as well as their correlation with tumor prognosis, immune escape, immune checkpoints, tumor mutational burden, and malignant tumor progression. Searching for potential biomarkers and targets for renal clear cell carcinoma.

METHODS

Downloaded the expression profile data and clinical data of 533 cases of renal clear cell carcinoma from the TCGA database, and randomly divided them into a test set (267 cases) and a validation set (266 cases). Based on previous research, 13 genes associated with Disulfideptosis were obtained. Using R software, lncRNAs with a differential expression that is related to the prognosis of renal clear cell carcinoma and associated with Disulfideptosis were screened out. After univariate Cox regression analysis, Lasso regression analysis, and multivariate Cox regression analysis, lncRNAs with independent predictive ability were obtained. A predictive risk model was established based on the risk scores. Verification was carried out between the obtained high-risk and low-risk groups and their subgroups (including Age, Gender, tumor mutational burden (TMB), tumor grading, and staging). Subsequently, a nomogram was established, and a calibration curve was generated for verification. Performed GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) functional enrichment analyses. Downloaded the values of Tumor Immune Dysfunction and Exclusion (TIDE) for all samples and calculated the difference between the high and low-risk groups. Selected human renal tumor cell lines (786-O, OS-RC-2, A-498, ACHN) and human renal cortex proximal tubule epithelial cell line (HK-2). The RNA expression levels of the above lncRNAs in each cell line were analyzed using RT-qPCR (Real-time Quantitative PCR Detecting System). Used siRNA (small interfering RNA) to knock down FAM225B in 786-O and OS-RC-2 cell lines, and then performed in vitro cell experiments to validate the functional characteristics of FAM225B.

RESULTS

Our constructed predictive model includes 5 lncRNAs with an independent predictive ability (FAM225B, ZNF503-AS1, SPINT1-AS1, WWC2-AS2, LINC01338), which can effectively distinguish between patients in high and low-risk groups and their subgroups. The 1, 3, and 5-year AUC (Area Under the ROC Curve) values of the established nomogram are 0.756, 0.752, and 0.781, respectively. The 5-year AUC value is higher compared to other clinical characteristics (Age: 0.598, Gender: 0.488, Grade: 0.680, Stage: 0.717). After the knockdown of FAM225B, the proliferation, migration, and invasion abilities of renal cancer cell lines OS-RC-2 and 786-O all decreased.

CONCLUSION

We have constructed and validated a prognostic model based on Disulfideptosis-associated lncRNAs. This model can effectively predict the high or low risk of patient prognosis and can distinguish the tumor cell mutational burden and immune escape capabilities among high-risk and low-risk patients. This predictive model can serve as an independent prognostic factor for renal clear cell carcinoma, providing a new direction for personalized treatment of patients with renal clear cell carcinoma.

Molecular subgroup establishment and signature creation of lncRNAs associated with acetylation in lung adenocarcinoma

BACKGROUND

The significance of long non-coding RNAs (lncRNAs) as pivotal mediators of histone acetylation and their influential role in predicting the prognosis of lung adenocarcinoma (LUAD) has been increasingly recognized. However, there remains uncertainty regarding the potential utility of acetylation-related lncRNAs (ARLs) in prognosticating the overall survival (OS) of LUAD specimens.

METHODS

The RNA-Seq and clinical information were downloaded from The Cancer Genome Atlas (TCGA). Through the differential analysis, weighted correlation network analysis (WGCNA), Pearson correlation test and univariate Cox regression, we found out the prognosis associated ARLs and divided LUAD specimens into two molecular subclasses. The ARLs were employed to construct a unique signature through the implementation of the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. Subsequently, the predictive performance was evaluated using ROC analysis and Kaplan-Meier survival curve analysis. Finally, ARL expression in LUAD was confirmed by quantitative real-time PCR (qRT-PCR).

RESULTS

We triumphantly built a ARLs prognostic model with excellent predictive accuracy for LUAD. Univariate and multivariate Cox analysis illustrated that risk model served as an independent predictor for influencing the overall survival OS of LUAD. Furthermore, a nomogram exhibited strong prognostic validity. Additionally, variations were observed among subgroups in the field of immunity, biological functions, drug sensitivity and gene mutations within the field.

CONCLUSIONS

Nine ARLs were identified as promising indicators of personalized prognosis and drug selection for people suffering with LUAD.

Comprehensive analysis of E47‑like factors and verification of ELF4 in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most prominent subtype of renal cancer and E47-like factors (ELFs) are important in tumorigenesis; however, the specific role of key ELFs in ccRCC remains unclear. The present study comprehensively analyzed RNA sequencing and clinical data from multiple databases, and identified differentially expressed ELFs (ELF3-5) in ccRCC. The DNA promoter methylation, genetic variation and clinical significance of ELF3-5 in ccRCC were analyzed using the cBioPortal and UALCAN databases. The association between ELF3-5 and multiple immune cell infiltration was analyzed using Tumor Immune Estimation Resource. Subsequently, ELF4 was selected and its association with biological functions was assessed. Cell counting kit-8 (CCK-8), colony formation, Transwell, macrophage chemotaxis and polarization assays were conducted to validate the functions of ELF4. Notably, the mRNA expression levels of ELF4 were significantly upregulated in ccRCC, whereas ELF3 and ELF5 mRNA expression levels were significantly downregulated. Clinical significance analysis revealed that ELF4 showed a high clinical significance with tumor grade, clear cell type A and B subtypes, and incidence rates of amplification in genetic variation. Further analyses indicated that ELF4 may be involved in multiple immune cell differentiation. Additionally, cell experiments revealed that ELF4 inhibition downregulated 769-P and 786-O proliferation, migration and invasion. Knockdown of ELF4 in cancer cells also inhibited M2 macrophage polarization and chemotaxis towards 769-P and 786-O cells. Conclusively, the present findings indicated the clinical significance of ELF4 in ccRCC, and verified its key role in driving cell proliferation, migration and invasion, and promoting M2 macrophage polarization and chemotaxis in ccRCC.

Comprehensive analyses of molecular features, prognostic values, and regulatory functionalities of m6A-modified long non-coding RNAs in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) has a high incidence and recurrence rate. N6-methyladenosine (m⁶A) modification of RNA has become a promising epigenetic marker in tumors. The dysregulation of both RNA m⁶A levels and m⁶A regulator expression levels reportedly affects essential biological processes in various tumors. Long non-coding RNAs (lncRNAs), a subgroup of RNAs over 200 nucleotides in length that do not code for protein, can be modified and regulated by m⁶A, but the relevant profile in LUAD remains unclear.

RESULTS

The m⁶A levels of total RNA were decreased in LUAD tumor tissues and cells. Multiple m⁶A regulators were abnormally expressed at both the RNA and protein levels, and were related in expression patterns and functionally synergistic. Our microarray revealed 2846 m⁶A-modified lncRNA transcripts as well as its molecular features, 143 of which were differentially m⁶A-modified and manifested a negative correlation between expression levels and m⁶A modification levels. More than half of the differentially m⁶A-modified lncRNAs associated with dysregulated expression. The 6-MRlncRNA risk signature was a reliable indicator for assessing survival time of LUAD patients. The competitive endogenous regulatory network suggested a potential m⁶A-induced pathogenicity in LUAD.

CONCLUSIONS

These data have demonstrated that differential RNA m⁶A modification and m⁶A regulator expression levels were identified in LUAD patients. In addition, this study provides evidence increasing the understanding of molecular features, prognostic values, and regulatory functionalities of m⁶A-modified lncRNAs in LUAD.

Metabolism-related long non-coding RNA in the stomach cancer associated with 11 AMMLs predictive nomograms for OS in STAD

Background: The metabolic processes involving amino acids are intimately linked to the onset and progression of cancer. Long non-coding RNAs (LncRNAs) perform an indispensable function in the modulation of metabolic processes as well as the advancement of tumors. Non-etheless, research into the role that amino acid metabolism-related LncRNAs (AMMLs) might play in predicting the prognosis of stomach adenocarcinoma (STAD) has not been done. Therefore, This study sought to design a model for AMMLs to predict STAD-related prognosis and elucidate their immune properties and molecular mechanisms.

Methods: The STAD RNA-seq data in the TCGA-STAD dataset were randomized into the training and validation groups in a 1:1 ratio, and models were constructed and validated respectively. In the molecular signature database, This study screened for genes involved in amino acid metabolism. AMMLs were obtained by Pearson’s correlation analysis, and predictive risk characteristics were established using least absolute shrinkage and selection operator (LASSO) regression, univariate Cox analysis, and multivariate Cox analysis. Subsequently, the immune and molecular profiles of high- and low-risk patients and the benefit of the drug were examined.

Results: Eleven AMMLs (LINC01697, LINC00460, LINC00592, MIR548XHG, LINC02728, RBAKDN, LINCOG, LINC00449, LINC01819, and UBE2R2-AS1) were used to develop a prognostic model. Moreover, high-risk individuals had worse overall survival (OS) than low-risk patients in the validation and comprehensive groups. A high-risk score was associated with cancer metastasis as well as angiogenic pathways and high infiltration of tumor-associated fibroblasts, Treg cells, and M2 macrophages; suppressed immune responses; and a more aggressive phenotype.

Conclusion: This study identified a risk signal associated with 11 AMMLs and established predictive nomograms for OS in STAD. These findings will help us personalize treatment for gastric cancer patients.

The hub genes and their potential regulatory mechanisms in chronic spontaneous urticaria revealed by integrated transcriptional expression analysis

Chronic spontaneous urticaria (CSU) is a recurrent disease characterized by wheals and or angioedema, and its pathogenesis is still unclear. The microarray datasets of skin tissue from CSU patients and healthy controls were integrated and analysed in Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were identified using the NetworkAnalyst tool. Then, the Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Subsequently, a protein-protein interaction (PPI) network of DEGs was constructed by STRING and the related hub genes were identified through the MOCDE tool. The potential miRNAs targeting hub genes were predicted based on the intersection of three online databases, namely TargetScanHuman, TargetBase and miRNet. Differentially expressed lncRNAs (DElncRNAs) was performed using the GEO2R tool. The potential miRNAs targeting DElncRNAs were predicted through miRNet. Finally, the shared miRNAs targeting both hub genes and DElncRNAs were used to construct an mRNA/miRNA/lncRNA regulatory network. A total of 296 DEGs were obtained, which were mainly enriched in inflammatory and immune responses. Further, 14 hub genes were identified by the PPI network of DEGs. Clinical correlation analysis showed that the mRNA expressions of S100A7, S100A8, S100A9, S100A12, IL6 and SOCS3 in CSU were positively correlated with the 7-day urticaria activity score (UAS7), and their potential diagnostic value was supported by the receiver operating characteristic curve (ROC) analysis. Five up-regulated lncRNAs in the cytoplasm were obtained by DElncRNAs analysis. The ROC analysis showed that PVT1, SNHG3 and ZBTB20 - AS1 was of potential diagnostic value for CSU. Eight shared miRNAs targeting both hub genes and DElncRNAs were identified and used to construct a competing endogenous RNA (ceRNA) network. It was found that the IL-6/miR - 149 - 5p/ZBTB20 - AS1 axis might play an important role in the activation of mast cells in CSU. IL-6 and its related regulatory molecules may be used as potential diagnostic markers and therapeutic targets for CSU.

Role of noncoding RNAs with emphasis on long noncoding RNAs as cervical cancer biomarkers

Cervical cancer is a significant public health problem in developing countries, as most cases present at an advanced stage. This review aimed to analyze the role of noncoding RNAs as diagnostic and prognostic biomarkers in cervical cancers. Published studies on specific microRNA signatures in body fluids and cervical cancer tissues are highly heterogeneous, and there are no validated assays. The precision of the various immune-associated long noncoding (lncRNA) signatures should be assessed in clinical samples. Even though lncRNAs are tissue and cancer-specific, safe and appropriate methods for delivery to tumor tissues, toxicities and side effects are to be explored. Few studies have evaluated deregulated lncRNA expression levels with clinicopathological factors in a limited number of clinical samples. Prospective studies assessing the diagnostic and prognostic roles of circulating lncRNAs and P-Element-induced wimpy testis interacting PIWI RNAs (Piwil RNAs) in cervical cancer cases are essential. For the clinical application of lnc-RNA-based biomarkers, comprehensive research is needed as the impact of noncoding transcripts on molecular pathways is complex. The standardization and validation of deregulated ncRNAs in noninvasive samples of cervical cancer cases are needed.

Identification and Validation of a Novel Necroptosis-Related Long Noncoding RNA Prognostic Signature for Lung Adenocarcinoma

Background

Several cancers, including lung adenocarcinoma (LUAD), are caused by genes related to necroptosis. However, it is still unknown how necroptosis-related long noncoding RNAs (lncRNAs) may be involved in LUAD. In order to predict the prognosis of LUAD patients and personalize immunotherapy, this study set out to construct a necroptosis-related lncRNA prognostic signature (NLPS).

Methods

The Cancer Genome Atlas (TCGA) database was used to download the LUAD transcriptome data and the associated clinical data. lncRNAs associated with necroptosis were screened using coexpression analysis. An NLPS was built using univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses. The Gene Expression Omnibus (GEO) database's GSE30219 was used to validate the NLPS. The prognostic value of the risk score was assessed using Kaplan-Meier survival, receiver operating characteristic (ROC) Cox regression, multivariate Cox regression, and nomogram analyses. Then, we looked into the differences between the low- and high-risk groups in the tumor immune microenvironment, immunotherapy response, and half-maximal inhibitory concentration (IC50).

Results

The 14 lncRNAs in a novel NLPS were created. With further validation in the GSE30219 dataset, the risk score according to the NLPS was an independent prognostic indicator for LUAD patients. Patients with better overall survival (OS) in the low-risk group, who were characterized by increased immune cell infiltration, tumor mutational burden (TMB), and immunophenoscore (IPS), may have hot tumors and higher immunotherapy response rates. In addition, the risk score was also closely linked to sensitivity to various anticancer medications.

Conclusions

We constructed a novel NLPS that could predict OS and sensitivity to immunotherapy in LUAD patients.

Identification and validation of ferroptosis-related lncRNA signature as a prognostic model for skin cutaneous melanoma

Background

Melanoma is a type of skin cancer, which originates from the malignant transformation of epidermal melanocytes, with extremely high lethality. Ferroptosis has been documented to be highly related to cancer pathogenesis and the effect of immunotherapy. In addition, the dysregulation of lncRNAs is greatly implicated in melanoma progression and ferroptosis regulation. However, the significance of ferroptosis-related lncRNA in melanoma treatment and the prognosis of melanoma patients remains elusive.

Methods

Via Least Absolute Shrinkage Selection Operator (LASSO) regression analysis in the TCGA SKCM database, a cutaneous melanoma risk model was established based on differentially-expressed ferroptosis-related lncRNAs (DEfrlncRNAs). The nomogram, receiver operating characteristic (ROC) curves, and calibration plots were conducted to examine the predictive performance of this model. Sequentially, we continued to analyze the differences between the high- and low-risk groups, in terms of clinical characteristics, immune cell infiltration, immune-related functions, and chemotherapy drug sensitivity. Moreover, the expressions of DEfrlncRNAs, PD-L1, and CD8 were also examined by qRT-PCR and immunohistochemical staining in melanoma tissues to further confirm the potential clinical implication of DEfrlncRNAs in melanoma immunotherapy.

Results

16 DEfrlncRNAs were identified, and a representative risk score for patient survival was constructed based on these 16 genes. The risk score was found to be an independent prognostic factor for the survival of melanoma patients. In addition, the low-risk group of patients had higher immune cell infiltration in the melanoma lesions, higher sensitivity to chemotherapeutic agents, and a better survival prognosis. Besides, the high expression of the identified 5 DEfrlncRNA in the low-risk group might suggest a higher possibility to benefit from immune checkpoint blockade therapy in the treatment of melanoma.

Conclusion

The DEfrlncRNA risk prediction model related to ferroptosis genes can independently predict the prognosis of patients with melanoma and provide a basis for evaluating the response of clinical treatment in melanoma.

Characterization of fatty acid metabolism-related lncRNAs in lung adenocarcinoma identifying potential novel prognostic targets

Lung adenocarcinoma (LUAD), a malignant respiratory tumor with an extremely poor prognosis, has troubled the medical community all over the world. According to recent studies, fatty acid metabolism (FAM) and long non-coding RNAs (lncRNAs) regulation have shown exciting results in tumor therapy. In this study, the original LUAD patient data was obtained from the TCGA database, and 12 FAM-related lncRNAs (AL390755.1, AC105020.6, TMPO-AS1, AC016737.2, AC127070.2, LINC01281, AL589986.2, GAS6-DT, AC078993.1, LINC02198, AC007032.1, and AL021026.1) that were highly related to the progression of LUAD were finally identified through bioinformatics analysis, and a risk score model for clinical reference was constructed. The window explores the immunology and molecular mechanism of LUAD, aiming to shed the hoping light on LUAD treatment.

Prognostic significance of a 4-lncRNA glycolysis-related signature in oral squamous cell carcinoma

BACKGROUND/PURPOSE

Oral squamous cell carcinoma (OSCC) is a highly malignant tumor, and the overall survival (OS) time of patients with OSCC varies considerably. This study aimed to identify reliable biomarkers for OSCC and construct a new prognostic signature, which may guide personalized precision treatment.

MATERIALS AND METHODS

Transcriptome array data of 317 patients with OSCC from The Cancer Genome Atlas Project (TCGA) cohort were retrospectively analyzed. Single-sample gene set enrichment analysis (ssGSEA) and univariate Cox regression were performed to identify the prognostic significance of the hallmarks of each tumor in OSCC. Subsequently, lncRNAs related to glycolysis were identified through co-expression analysis. A glycolysis-related prognostic signature was constructed by combining univariate Cox regression, least absolute shrinkage and selection operator (Lasso) regression, and multivariate Cox regression analyses. Additionally, the infiltration of immune cells in OSCC was evaluated based on data from ssGSEA and TIMER databases.

RESULTS

Glycolysis was identified as the main risk factor for OS in a variety of cancer hallmarks. The 4-lncRNA glycolysis prognostic signature could distinguish high and low-risk patients. This risk signature was found to be an independent prognostic risk factor for OSCC, showing good predictive power compared with other clinicopathological indicators. Immune correlation analysis showed that patients in the low-risk group exhibited higher levels of immune cell infiltration.

CONCLUSION

The novel 4-lncRNA prognostic signature can predict the clinical outcome of patients with OSCC well, and it is expected to become a promising prognostic biomarker as well as a potential therapeutic target in the future.

Development of a lncRNA-based prognostic signature for oral squamous cell carcinoma

BACKGROUND

We aimed to establish a long noncoding RNA (lncRNA)-based signature for accurately predicting prognosis and guiding the personalized clinical management of oral squamous cell carcinoma (OSCC).

METHODS

OSCC RNA sequencing profiles were acquired from The Cancer Genome Atlas and Gene Expression Omnibus. Univariate Cox regression, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses were performed to construct a lncRNA-based prognostic signature. Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curves and calibration curves were used to assess the effectiveness and accuracy of the signature. Additionally, we conducted single-sample gene-set enrichment analysis to infer the different degrees of immunocyte infiltration. Weighted correlation network analysis, enrichment analysis and Spearman's correlation analysis were implemented to screen immune-related genes that interact with the lncRNA signature.

RESULTS

In total, 14 lncRNAs were defined as potential prognostic biomarkers. Based on these lncRNAs, patients were divided into low- and high-risk subgroups with different survival times (p < 0.001). In addition, the reliability of the prognostic signature was verified by Kaplan-Meier analysis, ROC analysis and calibration curves. Patients in the low-risk group exhibited more significant immune cell infiltration. Simultaneously, a potential regulatory network consisting of 8 lncRNAs and 159 protein-coding genes in the top 10 immune-related biological process terms was constructed.

CONCLUSIONS

Our findings suggested that the 14-lncRNA signature has satisfactory performance in predicting the prognosis of OSCC, thereby providing new insights to the pathogenesis, clinical patient management and therapeutic intervention. The different immune cell infiltration statuses of OSCC patients may encourage immunotherapy.

Identification of Novel Tumor Microenvironment-Related Long Noncoding RNAs to Determine the Prognosis and Response to Immunotherapy of Hepatocellular Carcinoma Patients

Introduction: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with poor prognosis. The tumor microenvironment (TME) plays a vital role in HCC progression. Thus, this research was designed to analyze the correlation between the TME and the prognosis of HCC patients and to construct a TME-related long noncoding RNA (lncRNA) signature to determine HCC patients’ prognosis and response to immunotherapy.

Methods: We assessed the stromal–immune–estimate scores within the HCC microenvironment using the ESTIMATE (Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression Data) algorithm based on The Cancer Genome Atlas database, and their associations with survival and clinicopathological parameters were also analyzed. Thereafter, differentially expressed lncRNAs were filtered out according to the immune and stromal scores. Cox regression analysis was performed to build a TME-related lncRNA risk signature. Kaplan–Meier analysis was used to explore the prognostic value of the risk signature. Furthermore, we explored the biological functions and immune microenvironment features in the high- and low-risk groups. Lastly, we probed the association of the risk model with treatment responses to immune checkpoint inhibitors (ICIs) in HCC.

Results: The stromal, immune, and estimate scores were obtained utilizing the ESTIMATE algorithm for patients with HCC. Kaplan–Meier analysis showed that high scores were significantly correlated with better prognosis in HCC patients. Six TME-related lncRNAs were screened to construct the prognostic model. The Kaplan–Meier curves suggested that HCC patients with low risk had better prognosis than those with high risk. Receiver operating characteristic (ROC) curve and Cox regression analyses indicated that the risk model could predict HCC survival exactly and independently. Functional enrichment analysis revealed that some tumor- and immune-related pathways were activated in the high-risk group. We also revealed that some immune cells, which were important in enhancing immune responses toward cancer, were significantly increased in the low-risk group. In addition, there was a close correlation between ICIs and the risk signature, which can be used to predict the treatment responses of HCC patients.

Conclusion: We analyzed the influence of the stromal, immune, and estimate scores on the prognosis of HCC patients. A novel TME-related lncRNA risk model was established, which could be effectively applied as an independent prognostic biomarker and predictor of ICIs for HCC patients.

Autophagy-related long non-coding RNA signature for potential prognostic biomarkers of patients with cervical cancer: a study based on public databases

Background

Metastasis and recurrence are the main causes of death from cervical cancer (CC), thus it is important to identify more effective biomarkers to improve its prognosis. The purpose of our research was to determine the potential role of autophagy-related long non-coding RNA (lncRNA) in CC and to construct an autophagy-related lncRNA signature for survival of CC.

Methods

The lncRNAs in CC were downloaded from The Cancer Genome Atlas (TCGA) database, and autophagy-related lncRNAs were identified through the co-expression of lncRNA genes and autophagy genes. Several autophagy-related lncRNAs with prognostic value (AC012306.2, AL109976.1, ATP2A1-AS1, ILF3-DT, Z83851.2, STARD7-AS1, AC099343.2, AC008771.1, DBH-AS1, and AC097468.3) were identified using univariate and multivariate Cox regression analyses and a prognostic signature was established. The signature effect was detected by univariate Cox regression analysis [hazard ratio (HR) =1.665; 95% confidence interval (CI): 1.331–2.082; P<0.001] and multivariate Cox regression analysis (HR =1.738; 95% CI: 1.359–2.223; P<0.001). A nomogram was drawn by risk score and clinical features.

Results

The prognostic signature could predict the survival of CC by survival-receiver operating characteristic (ROC) curve [area under the curve (AUC) =0.810]. A nomogram was drawn by risk score and clinical features, and its c-index and calibration curve demonstrated that the prognostic signature could independently predict the prognosis of CC (P<0.001). Gene set enrichment analysis (GSEA) confirmed that the genes were significantly enriched in cancer- and autophagy-related pathways (P<0.05).

Conclusions

This 10 autophagy-related lncRNA signature has prognostic potential for CC. More important roles in the CC biology of these lncRNAs may be identified with further study.

An immune-associated ten-long noncoding RNA signature for predicting overall survival in cervical cancer

BACKGROUND

Several immune-associated long non-coding RNA (lncRNA) signatures have been reported as prognostic models in different types of cancers; however, the immune-associated lncRNA signature for predicting overall survival (OS) in cervical cancer is unknown.

METHODS

The lncRNA expression profiles and clinical data of cervical cancer were acquired from The Cancer Genome Atlas (TCGA) dataset. Immune-associated genes were extracted from the Molecular Signatures Database (MSigDB), and the immune-associated lncRNAs were extracted for Cox regression analysis. Principal component analysis (PCA) was used to distinguish the high and low risk status of cervical cancer patients. Gene Set Enrichment Analysis (GSEA) was used for functional analyses.

RESULTS

Cox regression analyses and the least absolute shrinkage and selection operator (LASSO) Cox regression model were used to construct an immune-associated ten-lncRNA signature (containing AL021807.1, AL109976.1, LINC02446, MIR4458HG, AC004540.2, AC009065.8, AC083809.1, AC055822.1, AP000904.1, and FBXL19-AS1) for predicting OS in cervical cancer. The signature segregated the cervical cancer patients into 2 groups (high-risk group and low-risk group). The Kaplan-Meier survival curves of AL021807.1, AL109976.1, LINC02446, and MIR4458HG were statistically significant (P<0.05) and the others (including AC004540.2, AC009065.8, AC083809.1, AC055822.1, AP000904.1, and FBXL19-AS1) were not statistically significant (P>0.05). The Kaplan-Meier survival curves of the signature were statistically significant (P=1.134e-10), and the 5-year survival rate was 0.444 in the high-risk group [95% confidence interval (CI): 0.334 to 0.590] and 0.884 in the low-risk group (95% CI: 0.807 to 0.969). The area under curve (AUC) of the receiver operating characteristic (ROC) curve of the signature was 0.833. The concordance index (C-index) of the signature was 0.788 (95% CI: 0.730 to 0.846, P=1.884778e-22). The PCA successfully distinguished the high-risk group and low-risk group based on the signature. The GSEA showed that the signature-related protein coding genes (PCGs) may participate in immunologic biological processes and pathways.

CONCLUSIONS

This study revealed that the immune-associated ten-lncRNA signature is an independent factor for cervical cancer prognosis prediction, providing a bright future for immunotherapy of cervical cancer patients.