Prognostic 4-lncRNA-based risk model predicts survival time of patients with head and neck squamous cell carcinoma

5-Methylcytosine-related lncRNAs: predicting prognosis and identifying hot and cold tumor subtypes in head and neck squamous cell carcinoma

BACKGROUND

5-Methylcytosine (m⁵C) methylation is recognized as an mRNA modification that participates in biological progression by regulating related lncRNAs. In this research, we explored the relationship between m⁵C-related lncRNAs (mrlncRNAs) and head and neck squamous cell carcinoma (HNSCC) to establish a predictive model.

METHODS

RNA sequencing and related information were obtained from the TCGA database, and patients were divided into two sets to establish and verify the risk model while identifying prognostic mrlncRNAs. Areas under the ROC curves were assessed to evaluate the predictive effectiveness, and a predictive nomogram was constructed for further prediction. Subsequently, the tumor mutation burden (TMB), stemness, functional enrichment analysis, tumor microenvironment, and immunotherapeutic and chemotherapeutic responses were also assessed based on this novel risk model. Moreover, patients were regrouped into subtypes according to the expression of model mrlncRNAs.

RESULTS

Assessed by the predictive risk model, patients were distinguished into the low-MLRS and high-MLRS groups, showing satisfactory predictive effects with AUCs of 0.673, 0.712, and 0.681 for the ROCs, respectively. Patients in the low-MLRS groups exhibited better survival status, lower mutated frequency, and lower stemness but were more sensitive to immunotherapeutic response, whereas the high-MLRS group appeared to have higher sensitivity to chemotherapy. Subsequently, patients were regrouped into two clusters: cluster 1 displayed immunosuppressive status, but cluster 2 behaved as a hot tumor with a better immunotherapeutic response.

CONCLUSIONS

Referring to the above results, we established a m⁵C-related lncRNA model to evaluate the prognosis, TME, TMB, and clinical treatments for HNSCC patients. This novel assessment system is able to precisely predict the patients' prognosis and identify hot and cold tumor subtypes clearly for HNSCC patients, providing ideas for clinical treatment.

Identification and panoramic analysis of drug response-related genes in triple negative breast cancer using as an example NVP-BEZ235

Taking NVP-BEZ235 (BEZ235) as an example to screen drug response-related genes (DRRGs) and explore their potential value in triple-negative breast cancer (TNBC). Through high-throughput technique, multidimensional transcriptome expression data (mRNA, miRNA and lncRNA) of BEZ235-treated and -untreated MDA-MB-468 cell lines were obtained. Combined with transcriptome data of the MDA-MB-468 cells and TCGA-TNBC tissues, differential gene expression analysis and WGCNA were performed to identify DRRGs associated with tumor trait by simulating the drug response microenvironment (DRM) of BEZ235-treated patients. Based on DRRGs, we constructed a ceRNA network and verified the expression levels of three key molecules by RT-qPCR, which not only demonstrated the successful construction of a BEZ235-treated cell line model but also explained the antitumor mechanism of BEZ235. Four molecular subtypes related to the DRM with survival difference were proposed using cluster analysis, namely glycolysis subtype, proliferation depression subtype, immune-suppressed subtype, and immune-activated subtype. A novel prognostic signature consisting of four DRRGs was established by Lasso-Cox analysis, which exhibited outstanding performance in predicting overall survival compared with several excellent reported signatures. The high- and low-risk groups were characterized by enrichment of metabolism-related pathways and immune-related pathways, respectively. Of note, the low-risk group had a better response to immune checkpoint blockade. Besides, pRRophetic analysis found that patients in the low-risk group were more sensitive to methotrexate and cisplation, whereas more resistant to BEZ235, docetaxel and paclitaxel. In conclusion, the DRRGs exemplified by BEZ235 are potential biomarkers for TNBC molecular typing, prognosis prediction and targeted therapy. The novel DRRGs-guided strategy for predicting the subtype, survival and therapy efficacy, might be also applied to more cancers and drugs other than TNBC and BEZ235.

LncRNA ALMS1-IT1 is a novel prognostic biomarker and correlated with immune infiltrates in colon adenocarcinoma

Colon adenocarcinoma (COAD) is one of the most serious cancers. It is important to accurately predict prognosis and provide individualized treatment. Evidence suggests that clinicopathological features and immune status of the body are related to the occurrence and development of cancer. Expression of long non-coding RNA (LncRNA) ALMS1 intronic transcript 1 (ALMS1-IT1) is observed in some cancer types, and we believe that it may have the potential to serve as a marker of COAD. Therefore, we used the data obtained from the cancer genome atlas (TCGA) database to prove the relationship between ALMS1-IT1 and COAD. Wilcoxon rank sum test, Chi-square test, Fisher exact test and logistic regression were used to evaluate relationships between clinical-pathologic features and ALMS1-IT1 expression. Receiver operating characteristic curves were used to describe binary classifier value of ALMS1-IT1 using area under curve score. Kaplan-Meier method and Cox regression analysis were used to evaluate factors contributing to prognosis. Gene oncology (GO) and (Kyoto Encyclopedia of Genes and Genomes) KEGG enrichment analysis were used to predict the function of differentially expressed genes associated with ALMS1-IT1. Gene set enrichment analysis (GSEA) was used to predict canonical pathways associated with ALMS1-IT1.Immune infiltration analysis was performed to identify the significantly involved functions of ALMS1-IT1. Starbase database was used to predict miRNAs and RNA binding proteins (RBPs) that may interact with ALMS1-IT1. Increased ALMS1-IT1 expression in COAD was associated with N stage (P < .001), M stage (P = .003), Pathologic stage (P = .002), and Primary therapy outcome (P = .009). Receiver operating characteristic curve suggested the significant diagnostic and prognostic ability of ALMS1-IT1 (area under curve = 0.857). High ALMS1-IT1 expression predicted a poorer overall-survival (P = .005) and poorer progression-free interval (PFI) (P = .012), and ALMS1-IT1 expression was independently correlated with PFI in COAD patients (hazard ratio (HR) :1.468; 95% CI: 1.029-2.093; P =.034) (HR: 1.468; 95% CI: 1.029-2.093; P = .034). GO, KEGG, GSEA, and immune infiltration analysis showed that ALMS1-IT1 expression was correlated with regulating the function of DNA and some types of immune infiltrating cells. ALMS1-IT1 expression was significantly correlated with poor survival and immune infiltrations in COAD, and it may be a promising prognostic biomarker in COAD.

Development and Validation of a Ferroptosis-Related lncRNAs Prognosis Model in Oral Squamous Cell Carcinoma

Objectives: Ferroptosis is an iron-dependent form of programmed cell death, which affects the prognosis of many cancers. Some long non-coding RNA (lncRNA) can affect the prognosis of cancer by regulating the process of ferroptosis. However, the role of ferroptosis-related lncRNA (frlncRNA) in oral squamous cell carcinoma (OSCC) is not yet clear.

Materials and Methods: The data of OSCC patients were downed from The Cancer Genome Atlas (TCGA). After univariate and multivariate Cox regression analysis, the prognosis-related ferroptosis-related lncRNAs were obtained to construct a prognostic model. Calculated the risk score to divide patients into high and low risk groups, and evaluated the predictive ability of the model and the differential expression of immunity in the high and low risk groups.

Results: The prognostic model for OSCC was constructed based on 8 prognostic-related frlncRNAs which co-expressed with 25 mRNAs. Kaplan-Meier analyses displayed that the risk score is inversely proportional to patient survival. Receiver operating characteristic (ROC) and decision curve analysis (DCA) indicated that the risk score is superior to other clinical characteristics, and independent prognostic analysis demonstated that risk score is independent factor for the overall survival (OS) rate. The results of immunological analysis showed differences in immune cells, functions, immune checkpoints, and m6A expression between high and low risk groups.

Conclusion: We constructed an OSCC patients prognosis model based on 8 frlncRNAs, which can provide prognostic evaluation and immune analysis for OSCC patients, and provided new direction for OSCC targeted therapy.

Identification and Validation of an EMT-Related LncRNA Signature for HNSCC to Predict Survival and Immune Landscapes

Long noncoding RNAs (lncRNAs) are increasingly recognized as decisive factors in the progression of head and neck squamous cell carcinoma (HNSCC), and they participate in the epithelial–mesenchymal transformation (EMT) of HNSCC. LncRNAs are closely related to the prognosis of patients with HNSCC; thus, it is essential to identify EMT-related lncRNAs with prognostic value for HNSCC. The coexpression network of EMT-related lncRNAs was constructed using The Cancer Genome Atlas (TCGA). An EMT-related eight-lncRNA-based prognostic signature was constructed using LASSO Cox regression and Cox proportional hazards analyses. Univariate and multivariate analyses and stratified prognosis confirmed that the prognostic signature was an independent predictive factor. Subsequently, we performed immune cell infiltration analysis, gene set enrichment analysis (GSEA), and single-sample GSEA (ssGSEA) pathway enrichment analysis to uncover the potential molecular mechanisms of prognostic differences in the high- and low-risk groups. Next, we discussed the relationship between the prognostic signature and immune checkpoint-related genes, their TIDE scores, and the sensitivity of common chemotherapeutics. Finally, we further verified the expression differences in lncRNAs that were included in our signature via RT–qPCR in eighteen paired tissues. In summary, this prognostic signature provides powerful prognostic biomarkers for HNSCC and could serve as a predictor for the sensitivity of common chemotherapeutics and immunotherapy responses as well as providing a reference for further personalized treatment.

Comprehensive Analysis of N6-Methyladenosine-Related lncRNA Signature for Predicting Prognosis and Immune Cell Infiltration in Patients with Colorectal Cancer

Background

Colorectal cancer (CRC) is the third most common tumor worldwide. Aberrant N6-methyladenosine (m6A) modification can influence the progress of the CRC. Additionally, long noncoding RNA (lncRNA) plays a critical role in CRC and has a close relationship with m6A modification. However, the prognostic potential of m6A-related lncRNAs in CRC patients still remains to be clarified.

Methods

We use “limma” R package, “glmnet” R package, and “survival” R package to screen m6A-related-lncRNAs with prognostic potential. Then, we comprehensively analysed and integrated the related lncRNAs in different TNM stages from TCGA database using the LASSO Cox regression. Meanwhile, the relationship between functional enrichment of m6A-related lncRNAs and immune microenvironment in CRC was also investigated using the TCGA database. A prognostic model was constructed and validated to determine the association between m6A-related lncRNAs in different TNM stages and the prognosis of CRC.

Result

We demonstrated that three related m6A lncRNAs in different TNM stages were associated with the prognosis of CRC patients. Patients from the TCGA database were classified into the low-risk and the high-risk groups based on the expression of these lncRNAs. The patients in the low-risk group had longer overall survival than the patients in the high-risk group (P < 0.001). We further constructed and validated a prognostic nomogram based on these genes with a C-index of 0.80. The receiver operating characteristic curves confirmed the predictive capacity of the model. Meanwhile, we also found that the low-risk group has the correlation with the dendritic cell (DC). Finally, we discovered the relationship between the m6A regulators and the three lncRNAs.

Conclusion

The prognostic model based on three m6A-related lncRNAs exhibits superior predictive performance, providing a novel prognostic model for the clinical evaluation of CRC patients.

Upregulated Long Non-coding RNA ALMS1-IT1 Promotes Neuroinflammation by Activating NF-κB Signaling in Ischemic Cerebral Injury

BACKGROUND

ALMS1-IT1, a recently identified lncRNA, has been proven to play a crucial role in regulating tumor progression and predicting the survival time of tumor patients. Data analysis from the Human Body Map (HBM) revealed that ALMS1-IT1 is expressed mainly in brain tissues.

METHODS

In this study, the role of ALMS1-IT in regulating neuro-inflammation and functional recovery was investigated after ischemic cerebral damage. To this end, the rat model of transient middle cerebral artery occlusion (tMCAO) was constructed, the cell model of oxygen-glucose deprivation (OGD) was established using BV2 microglial cells, and the aberrant expression of ALMS1-IT1 was assessed in brain tissues. After ALMS1- IT1 knockdown through intrathecal injection of Lv-shALMS1-IT1, neuro-inflammatory response and functional tests including a modified neurological severity score (mNSS) and a foot-fault test were assessed.

RESULTS

The level of ALMS1-IT1 was promptly enhanced at 12 hours (h) following MCAO, peaking at 48 h, and remaining high at day 14 compared to the sham group. Pro-inflammatory cytokines (IL-1β, IL-6, and TNF- α) were increased after MCAO, whereas ALMS1-IT1 inhibition suppressed the expression of IL-1β, IL-6 and TNF-α in MCAO rats. The results from mNSS and foot-fault test showed that ALMS1-IT1 knockdown significantly improved spatial learning and sensorimotor function of MCAO rats. Mechanistically, ALMS1-IT1 knockdown suppressed the activation of NF-κB signaling in vitro and in vivo, as evidenced by decreased p65 expression and p65 nuclear translocation. ALMS1-IT1 overexpression facilitated pro-inflammatory cytokines expression in microglia, whereas the effect was blocked by treatment with JSH-23 (a specific NF-κB inhibitor).

CONCLUSION

These data demonstrated that ALMS1-IT1 inhibition improved neurological function of MCAO rats, at least in part by repressing NF-κB-dependent neuro-inflammation.

Fatty Acid Metabolism-Related lncRNAs Are Potential Biomarkers for Predicting the Overall Survival of Patients With Colorectal Cancer

Abnormal metabolism, including abnormal fatty acid metabolism, is an emerging hallmark of cancer. The current study sought to investigate the potential prognostic value of fatty acid metabolism-related long noncoding RNAs (lncRNAs) in colorectal cancer (CRC). To this end, we obtained the gene expression data and clinical data of patients with CRC from The Cancer Genome Atlas (TCGA) database. Through gene set variation analysis (GSVA), we found that the fatty acid metabolism pathway was related to the clinical stage and prognosis of patients with CRC. After screening differentially expressed RNAs, we constructed a fatty acid metabolism-related competing endogenous RNA (ceRNA) network based on the miRTarBase, miRDB, TargetScan, and StarBase databases. Next, eight fatty acid metabolism-related lncRNAs included in the ceRNA network were identified to build a prognostic signature with Cox and least absolute shrinkage and selection operator (LASSO) regression analyses, and a nomogram was established based on the lncRNA signature and clinical variables. The signature and nomogram were further validated by Kaplan–Meier survival analysis, Cox regression analysis, calibration plots, receiver operating characteristic (ROC) curves, decision curve analysis (DCA). Besides, the TCGA internal and the quantitative real-time polymerase chain reaction (qRT-PCR) external cohorts were applied to successfully validate the robustness of the signature and nomogram. Finally, in vitro assays showed that knockdown of prognostic lncRNA TSPEAR-AS2 decreased the triglyceride (TG) content and the expressions of fatty acid synthase (FASN) and acetyl-CoA carboxylase 1 (ACC1) in CRC cells, which indicated the important role of lncRNA TSPEAR-AS2 in modulating fatty acid metabolism of CRC. The result of Oil Red O staining showed that the lipid content in lncRNA TSPEAR-AS2 high expression group was higher than that in lncRNA TSPEAR-AS2 low expression group. Our study may provide helpful information for fatty acid metabolism targeting therapies in CRC.

Silencing lncRNA HOXA10-AS decreases cell proliferation of oral cancer and HOXA10-antisense RNA can serve as a novel prognostic predictor

Objective

Long noncoding (lnc)RNAs regulate multiple biological processes including cancer. Oral squamous cell carcinoma (OSCC) is a common malignancy with poor prognosis. We aimed to identify the function of lncRNA HOXA10 antisense RNA (HOXA10-AS) and its clinical significance.

Methods

We used differential expression analysis to identify aberrantly expressed lncRNAs associated with OSCC. We identified key genes related to HOXA10-AS and their biological functions using bioinformatics tools and functional enrichment analyses. We predicted the function of HOXA10-AS using gene set enrichment and variation analyses and analyzed proliferation markers at the mRNA and protein levels. Finally, we silenced HOXA10-AS using antisense oligonucleotide and assessed proliferation ability using a cell counting kit (CCK8) and clone formation assays.

Results

In total, 506 aberrantly expressed lncRNAs were identified. HOXA10-AS was identified as a risk factor for OSCC and its expression was positively associated with tumor grade. We identified hub genes involved in regulating proliferation and predicted that HOXA10-AS is associated with an active cell cycle and increased proliferation. Silencing HOXA10-AS decreased proliferation in OSCC cell lines.

Conclusions

HOXA10-AS is involved in cell proliferation and silencing it decreases proliferation. Thus, HOXA10-AS could serve as prognostic biomarker and therapeutic target for OSCC.

The lncRNA ALMS1-IT1 may promote malignant progression of lung adenocarcinoma via AVL9-mediated activation of the cyclin-dependent kinase pathway

Lung adenocarcinoma (LUAD) is the primary epithelial tumor of the lung. The lack of clinical symptoms and specific molecular diagnostic indicators during the early stages of LUAD mean that the disease may not be detected until late stages, and the 5‐year survival rate is only approximately 15%. Long non‐coding RNA ALMS1 intronic script 1 (ALMS1‐IT1) was previously reported to be correlated with the poor prognosis of head and neck squamous cell carcinoma patients. Here, we investigated whether ALMS1‐IT1 has prognostic potential for LUAD. Bioinformatics analyses were performed to examine the expression and prognostic value of ALMS1 and AVL9 (for which gene expression is positively correlated with ALMS1‐IT1 expression in LUAD) in LUAD based on TCGA and Oncomine databases. We report that ALMS1‐IT1 and AVL9 were both highly expressed in LUAD and correlated with poor outcomes in LUAD patients. Of note, the prognosis of LUAD patients with low expression of both ALMS1‐IT1 and AVL9 was superior to that of other patients. Furthermore, the proliferation, migration and invasion of LUAD cells were decreased in cells lacking ALMS1‐IT1, and this decrease could be almost completely reversed through overexpression of AVL9. Gene set enrichment analysis revealed that expression of genes related to the cell cycle pathway is closely related to both the high expression of ALMS1‐IT1 and AVL9 in LUAD. Finally, up‐regulation of ALMS1‐IT1 can activate the cyclin‐dependent kinase pathway, whereas absence of AVL9 can reverse this activation, as shown by western blotting. In summary, ALMS1‐IT1/AVL9 may promote the malignant progression of LUAD, at least in part by regulating the cyclin‐dependent kinase pathway.

Distinguishable Prognostic miRNA Signatures of Head and Neck Squamous Cell Cancer With or Without HPV Infection

Since their discovery in the 1990’s, microRNAs (miRNA) have opened up new vistas in the field of cancer biology and are found to have fundamental roles in tumorigenesis and progression. As head and neck squamous cell carcinoma (HNSCC) with positive human papillomavirus (HPV+) is significantly distinct from its HPV negative (HPV−) counterpart in terms of both molecular mechanisms and clinical prognosis, the current study aimed to separately develop miRNA signatures for HPV+ and HPV− HNSCC as well as to explore the potential functions. Both signatures were reliable for the prediction of prognosis in their respective groups. Then Enrichment analysis was performed to predict the potential biological functions of the signatures. Importantly, combining previous studies and our results, we speculated that HPV+ HNSCC patients with low signature score had better immunity against the tumors and enhanced the sensitivity of therapies leading to improved prognosis, while HPV− HNSCC patients with high signature score acquired resistance to therapeutic approaches as well as dysregulation of cell metabolism leading to poor prognosis. Hence, we believe that the identified signatures respectively for HPV+ and HPV− HNSCC, are of great significance in accessing patient outcomes as well as uncovering new biomarkers and therapeutic targets, which are worth further investigation through molecular biology experiments.

Identification of Three Autophagy-Related Long Non-Coding RNAs as a Novel Head and Neck Squamous Cell Carcinoma Prognostic Signature

Head and neck squamous cell carcinoma (HNSCC) has a poor prognosis. Considerable evidence indicates that autophagy and non-coding RNA play essential roles in the biological processes involved in cancers, but associations between autophagy-related long non-coding RNAs (lncRNAs) and HNSCC remain unclear. In the present study, HNSCC RNA sequences and autophagy-related gene data were extracted from The Cancer Genome Atlas database and the Human Autophagy Database. A total of 1,153 autophagy-related lncRNAs were selected via calculating Pearson’s correlation coefficient. Three prognosis-related autophagy lncRNAs were identified via univariate Cox regression, least absolute shrinkage and selection operator analysis, and multivariate Cox regression analysis. We also constructed a prognostic model based on these autophagy-related lncRNAs and evaluated its ability to accurately and independently predict the prognosis of HNSCC patients. The area under the curve (AUC) was 0.864 (3-year) and 0.836 (5-year), and our model can independently predict the prognosis of HNSCC. The prognostic value of the three autophagy lncRNAs was confirmed via analysis of samples from five databases. To further identify the functions of the three lncRNAs, a co-expression network was constructed and pathway analysis was performed. In that analysis the lncRNAs were correlated with 189 related genes and 20 autophagy-related genes, and these lncRNAs mainly involved homologous recombination, the Fanconi anemia pathway, the autophagy-related pathway, and immune-related pathways. In addition, we validated the expression levels of three lncRNAs and autophagy markers (ATG12, BECN1, and MAP1LC3B) based on TIMER, Oncomine, and HPA database analysis. Our results indicated that TTTY15 was increased in HPV positive and HPV negative HNSCC patients, and three autophagy markers were up-regulated in all HNSCCC patients. Lastly, association between three lncRNAs and autophagy markers was performed, and our results showed that TTTY15 and MIF-AS1 were associated with autophagy markers. Collectively, these results suggested that three autophagy-related lncRNAs have prognostic value in HNSCC patients.

Expression scoring of a small-nucleolar-RNA signature identified by machine learning serves as a prognostic predictor for head and neck cancer

Head and neck squamous cell carcinoma (HNSCC) is a common malignancy with high mortality and poor prognosis due to a lack of predictive markers. Increasing evidence has demonstrated small nucleolar RNAs (snoRNAs) play an important role in tumorigenesis. The aim of this study was to identify a prognostic snoRNA signature of HNSCC. Survival-related snoRNAs were screened by Cox regression analysis (univariate, least absolute shrinkage and selection operator, and multivariate). The predictive value was validated in different subgroups. The biological functions were explored by coexpression analysis and gene set enrichment analysis (GSEA). One hundred and thirteen survival-related snoRNAs were identified, and a five-snoRNA signature predicted prognosis with high sensitivity and specificity. Furthermore, the signature was applicable to patients of different sexes, ages, stages, grades, and anatomic subdivisions. Coexpression analysis and GSEA revealed the five-snoRNA are involved in regulating malignant phenotype and DNA/RNA editing. This five-snoRNA signature is not only a promising predictor of prognosis and survival but also a potential biomarker for patient stratification management.