A Prognostic 5-lncRNA Expression Signature for Head and Neck Squamous Cell Carcinoma

Comprehensive analysis of the functions, prognostic and diagnostic values of RNA binding proteins in head and neck squamous cell carcinoma

BACKGROUND

Accumulating evidence has suggested that RNA binding protein (RBP) dysregulation plays an essential role during tumorigenesis. Here, we sought to explore the potential biological functions and clinical significance of RBP and develop diagnostic and prognostic signatures based on RBP in patients with head and neck squamous cell carcinoma (HNSCC).

METHODS

The differently expressed RBPs between HNSCC samples and their normal counterparts were identified using the Limma package. The immunohistochemistry (IHC) images of several RBPs were collected from the Human Protein Atlas database. The diagnostic signature based on RBP was built by LASSO-logistic regression and random forest. The prognostic signature based on RBP was constructed by LASSO and stepwise Cox regression analysis in the training cohort and validated in the validation cohort.

RESULTS

Eighty-four aberrantly expressed RBPs were obtained, comprising 41 up-regulated and 43 down-regulated RBPs. Seven RBP genes (CPEB3, PDCD4, ENDOU, PARP12, DNMT3B, IGF2BP1, EXO1) were identified as diagnostic-related hub genes. They were used to establish a diagnostic RBP signature risk score (DRBPS) model by the coefficients in least absolute shrinkage and selection operator (LASSO)-logistic regression analysis and showed high specificity and sensitivity in the training (area under the receiver operating characteristic curve (AUC) = 0.998), and in all validation cohorts (AUC > 0.95 for all). Similarly, seven RBP genes (MKRN3, ZC3H12D, EIF5A2, AFF3, SIDT1, RBM24, and NR0B1) were identified as prognosis-associated hub genes by LASSO and stepwise multiple Cox regression analyses and were used to construct the prognostic model named as PRBPS. The AUC of the time-dependent receiver operator characteristic curve of the prognostic model was 0.664 at 3 years and 0.635 at 5 years in the training cohort and 0.720, 0.777 in the validation cohort, showing a favorable predictive efficacy for prognosis in HNSCC.

CONCLUSIONS

Our results demonstrate the value of consideration of RBP in the diagnosis and prognosis for HNSCC and provide a novel insight into understanding the potential role of dysregulated RBP in HNSCC.

Molecular Signature of Long Non-Coding RNA Associated with Areca Nut-Induced Head and Neck Cancer

The areca nut is a high-risk carcinogen for head and neck cancer (HNC) patients in Southeast Asia. The underlying molecular mechanism of areca nut-induced HNC remains unclear, especially regarding the role of long non-coding RNA (lncRNA). This study employed a systemic strategy to identify lncRNA signatures related to areca nut-induced HNC. In total, 84 cancer-related lncRNAs were identified. Using a PCR array method, 28 lncRNAs were identified as being dysregulated in HNC cells treated with areca nut (17 upregulated and 11 downregulated). Using bioinformatics analysis of The Cancer Genome Atlas Head-Neck Squamous Cell Carcinoma (TCGA-HNSC) dataset, 45 lncRNAs were differentially expressed in tumor tissues from HNC patients (39 over- and 6 under-expressions). The integrated evaluation showed 10 lncRNAs dysregulated by the areca nut and altered expression in patients, suggesting that these panel molecules participate in areca nut-induced HNC. Five oncogenic (LUCAT1, MIR31HG, UCA1, HIF1A-AS2, and SUMO1P3) and tumor-suppressive (LINC00312) lncRNAs were independently validated, and three key molecules were further examined. Pathway prediction revealed that LUCAT1, UCA1, and MIR31HG modulate multiple oncogenic mechanisms, including stress response and cellular motility. Clinical assessment showed that these lncRNAs exhibited biomarker potentials in diagnosis (area under the curve = 0.815 for LUCAT1) and a worse prognosis (both p < 0.05, survival analysis). Cellular studies further demonstrated that MIR31HG facilitates areca nut-induced cancer progression, as silencing this molecule attenuated arecoline-induced invasion ability in HNC cells. This study identified lncRNA signatures that play a role in areca nut-induced HNC. These molecules may be further applied in risk assessment, diagnosis, prognosis, and therapeutics for areca nut-associated malignancies.

Identification of key snoRNAs serves as biomarkers for hepatocellular carcinoma by bioinformatics methods

BACKGROUND

Hepatocellular carcinoma (HCC) is a common malignancy with high mortality and poor prognosis due to a lack of predictive markers. However, research on small nuclear RNAs (snoRNAs) in HCC were very little. This study aimed to identify a potential diagnostic and prognostic snoRNA signature for HCC.

METHODS

HCC datasets from the cancer genome atlas (TCGA) and international cancer genome consortium (ICGC) cohorts were used. Differentially expressed snoRNA (DEs) were identified using the limma package. Based on the DEs, diagnostic and prognostic models were established by the least absolute shrinkage and selection operator (LASSO) regression and COX analysis, and Kaplan-Meier (K-M) survival analysis and receiver operating characteristic (ROC) curve analysis were conducted to evaluate the efficiency of signatures. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were used to analyze the risk score and further explore the potential correlation between the risk groups and tumor immune status in TCGA. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to determine the functions of key snoRNAs.

RESULTS

We constructed a 6-snoRNAs signature which could classify patients into high- or low-risk groups and found that patients in the high-risk group had a worse prognosis than those in the low-risk group and were significantly involved in p53 processes. Tumor immune status analysis revealed that CTLA4 and PDCD1 (PD1) were highly expressed in the high-risk group, which responded to PD1 inhibitor therapy. Additionally, a 25-snoRNAs diagnostic signature was constructed with an area under the curve (AUC) of 0.933 for distinguishing HCCs from normal controls. Finally, 3 key snoRNAs (SNORA11, SNORD124, and SNORD46) were identified with both diagnostic and prognostic efficacy, some of which were closely related to the spliceosome and Notch signaling pathways.

CONCLUSIONS

Our study identified 6 snoRNAs that may serve as novel prognostic models and 3 key snoRNAs with both diagnostic and prognostic efficacy for HCC.

Characterization of tumor immune microenvironment and cancer therapy for head and neck squamous cell carcinoma through identification of a genomic instability-related lncRNA prognostic signature

Head and neck squamous cell carcinoma (HNSCC) represents one of the most prevalent and malignant tumors of epithelial origins with unfavorable outcomes. Increasing evidence has shown that dysregulated long non-coding RNAs (lncRNAs) correlate with tumorigenesis and genomic instability (GI), while the roles of GI-related lncRNAs in the tumor immune microenvironment (TIME) and predicting cancer therapy are still yet to be clarified. In this study, transcriptome and somatic mutation profiles with clinical parameters were obtained from the TCGA database. Patients were classified into GI-like and genomic stable (GS)-like groups according to the top 25% and bottom 25% cumulative counts of somatic mutations. Differentially expressed lncRNAs (DElncRNAs) between GI- and GS-like groups were identified as GI-related lncRNAs. These lncRNA-related coding genes were enriched in cancer-related KEGG pathways. Patients totaling 499 with clinical information were randomly divided into the training and validation sets. A total of 18 DElncRNAs screened by univariate Cox regression analysis were associated with overall survival (OS) in the training set. A GI-related lncRNA signature that comprised 10 DElncRNAs was generated through least absolute shrinkage and selection operator (Lasso)-Cox regression analysis. Patients in the high-risk group have significantly decreased OS vs. patients in the low-risk group, which was verified in internal validation and entire HNSCC sets. Integrated HNSCC sets from GEO confirmed the notable survival stratification of the signature. The time-dependent receiver operating characteristic curve demonstrated that the signature was reliable. In addition, the signature retained a strong performance of OS prediction for patients with various clinicopathological features. Cell composition analysis showed high anti-tumor immunity in the low-risk group which was evidenced by increased infiltrating CD8+ T cells and natural killer cells and reduced cancer-associated fibroblasts, which was convinced by immune signatures analysis via ssGSEA algorithm. T helper/IFNγ signaling, co-stimulatory, and co-inhibitory signatures showed increased expression in the low-risk group. Low-risk patients were predicted to be beneficial to immunotherapy, which was confirmed by patients with progressive disease who had high risk scores vs. complete remission patients. Furthermore, the drugs that might be sensitive to HNSCC were identified. In summary, the novel prognostic GILncRNA signature provided a promising approach for characterizing the TIME and predicting therapeutic strategies for HNSCC patients.

The role of long non-coding RNAs in the pathogenesis of head and neck squamous cell carcinoma

Head and neck cancers are a heterogeneous collection of malignancies of the upper aerodigestive tract, salivary glands, and thyroid. However, the molecular mechanisms underlying the carcinogenesis of head and neck squamous cell carcinomas (HNSCCs) remain poorly understood. Over the past decades, overwhelming evidence has demonstrated the regulatory roles of long non-coding RNAs (lncRNAs) in tumorigenesis, including HNSCC. Notably, these lncRNAs have vital roles in gene regulation and affect various aspects of cellular homeostasis, including proliferation, survival, and metastasis. They exert regulating functions by interacting with nucleic acids or proteins and affecting cancer cell signaling. LncRNAs represent a burgeoning field of cancer research, and we are only beginning to understand the importance and complicity of lncRNAs in HNSCC. In this review, we summarize the deregulation and function of lncRNAs in human HNSCC. We also review the working mechanism of lncRNAs in HNSCC pathogenesis and discuss the potential application of lncRNAs as diagnostic/prognostic tools and therapeutic targets in human HNSCC.

Identification and Construction of a Predictive Immune-Related lncRNA Signature Model for Melanoma

Objective

The occurrence and development mechanisms of melanoma are related to immunity and lncRNAs. Therefore, it is necessary to systematically explore immune-related lncRNA profiles to help improve the prognosis of melanoma.

Methods

We integrated immune-related lncRNAs and the basic clinical information of melanoma patients in the TCGA dataset. Immune-associated lncRNAs were selected by differential expression screening and enriched for analysis. After univariate and multivariate Cox regression analyses, a new prognostic indicator based on immune-associated lncRNAs was established.

Results

Overall, differentially expressed immune-related lncRNAs were significantly associated with clinical outcomes in patients with melanoma. A prognostic model was then established based on 14 immune-associated lncRNAs (LRRC8C-DT, AC021188.1, MALINC1, CCR5AS, EIF2AK3-DT, AC022306.2, AC242842.1, AL034376.1, AL662844.4, AC009065.3, AC099811.3, AC125807.2, SPINT1-AS1 and AC009495.2). Melanoma patients in the high-risk group had worse overall survival than those in the low-risk group. The AUC of the risk score was 0.786.

Conclusion

This study identified several clinically significant immune-related lncRNAs and established a relevant prognostic model, which provided a molecular analysis of immunity in melanoma and potential prognostic lncRNAs for melanoma.

H19- and hsa-miR-338-3p-mediated NRP1 expression is an independent predictor of poor prognosis in glioblastoma

Glioblastoma multiforme (GBM) is the most common and also the most invasive brain cancer. GBM progression is rapid and its prognosis is poor. Therefore, finding molecular targets in GBM is a critical goal that could also play important roles in clinical diagnostics and treatments to improve patient prognosis. We jointly analyzed the GSE103227, GSE103229, and TCGA databases for differentially expressed RNA species, obtaining 52 long non-coding RNAs (lncRNAs), 31 microRNAs (miRNAs), and 186 mRNAs, which were used to build a competing endogenous RNA network. Kaplan–Meier and receiver operating characteristic (ROC) analyses revealed five survival-related lncRNAs: H19, LINC01574, LINC01614, RNF144A-AS1, and OSMR-AS1. With multiple optimization mRNAs, we found the H19-hsa-miR-338-3P-NRP1 regulatory pathway. Additionally, we noted high NRP1 expression in GBM patients, and Kaplan–Meier and ROC analyses showed that NRP1 expression was associated with GBM prognosis. Cox analysis indicated that NRP1 is an independent prognostic factor in GBM patients. In conclusion, H19 and hsa-miR-338-3P regulate NRP1 expression, and this pathway plays an important role in GBM.

Systematic Analysis and Identification of Dysregulated Panel lncRNAs Contributing to Poor Prognosis in Head-Neck Cancer

Head and neck cancer (HNC) is one of the most prevalent cancers worldwide, accounting for approximately 5% of all cancers. While the underlying molecules and their pathogenetic mechanisms in HNC have yet to be well elucidated, recent studies have shown that dysregulation of lncRNAs may disrupt the homeostasis of various biological pathways. However, the understanding of lncRNAs in HNC is still limited by the lack of expression profiling. In the present study, we employed a systematic strategy to identify a panel of lncRNA associated with HNC. A cancer-related lncRNA profile PCR array was screened to explore potential molecules specific for HNC. A total of 55 lncRNAs were found to be dysregulated in HNC cells when compared to normal keratinocytes. Further analysis of the prognostic significance using The Cancer Genome Atlas (TCGA) database revealed 15 lncRNAs highly correlated with overall survival in HNC patients. Additionally, clinical sample expression analysis of the TCGA-HNSC cohort revealed 16 highly dysregulated lncRNAs in HNC, resulting in a combined 31-lncRNA signature panel that could predict prognosis. Validation of these molecules confirmed the considerable level of altered expressions in HNC cells, with XIST, HOXA11-AS, TSIX, MALAT1, WT1-AS, and IPW being the most prominently dysregulated. We further selected a molecule from our panel (XIST) to confirm the validity of these lncRNAs in the regulation of cancer aggressiveness. Gene ontology (GO) and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analyses demonstrated that XIST participated in various cancer-related functions, including cell proliferation and metastasis. XIST silencing with the RNAi technique substantially reduced invasion and migration in several HNC cell lines. Thus, our study defined a 31-lncRNA panel as prognostic signatures in HNC. These perspective results provide a knowledge foundation for further application of these molecules in precision medicine.

Identification of a Novel Signature Predicting Overall Survival in Head and Neck Squamous Cell Carcinoma

Background: Head and neck squamous cell carcinoma (HNSCC) is a highly heterogeneous tumor with a high incidence and poor prognosis. Therefore, effective predictive models are needed to evaluate patient outcomes and optimize treatment. Methods: Robust Rank Aggregation (RRA) method was used to identify highly robust differentially-expressed genes (DEGs) between HNSCC and normal tissue in 9 GEO and TCGA datasets. Univariate Cox regression analysis and Lasso Cox regression analysis were performed to identify DEGs related to the Overall survival (OS) and to construct a prognostic gene signature (HNSCCSig). External validation was performed using GSE65858 dataset. Moreover, comprehensive bioinformatics analyses were used to identify the association between HNSCCSig and tumor immune environment. Results: A total of 257 reliable DEGs were identified by differentially analysis result of TCGA and GSE65858 datasets. The HNSCCSig including 7 mRNAs (SLURP1, SCARA5, CLDN10, MYH11, CXCL13, HLF, and ITGA3) were developed and validated to identify high-risk group who had a worse OS than low-risk group in TCGA and GSE65858 datasets. Cox regression analysis showed that the HNSCCSig could independently predict OS in both the TCGA and the GSE65858 datasets. Further research demonstrated that the infiltration bundance of CD8 + T cells, B cells, neutrophils, and NK cells were significantly lower in the high-risk group. A nomogram was also constructed by combining the HNSCCSig and clinical characters. Conclusion: We established and validated the HNSCCSig consisting of SLURP1, SCARA5, CLDN10, MYH11, CXCL13, HLF, and ITGA3. A nomogram combining HNSCCSig and some clinical parameters was constructed to identify high-risk HNSCC-patients with poor prognosis.

Immune-Related lncRNA Signature for Predicting the Immune Landscape of Head and Neck Squamous Cell Carcinoma

Background: Long non-coding RNA (lncRNA) plays a significant role in the development, establishment, and progression of head and neck squamous cell carcinoma (HNSCC). This article aims to develop an immune-related lncRNA (irlncRNA) model, regardless of expression levels, for risk assessment and prognosis prediction in HNSCC patients. Methods: We obtained clinical data and corresponding full transcriptome expression of HNSCC patients from TCGA, downloaded GTF files to distinguish lncRNAs from Ensembl, discerned irlncRNAs based on co-expression analysis, distinguished differentially expressed irlncRNAs (DEirlncRNAs), and paired these DEirlncRNAs. Univariate Cox regression analysis, LASSO regression analysis, and stepwise multivariate Cox regression analysis were then performed to screen lncRNA pairs, calculate the risk coefficient, and establish a prognosis model. Finally, the predictive power of this model was validated through the AUC and the ROC curves, and the AIC values of each point on the five-year ROC curve were calculated to select the maximum inflection point, which was applied as a cut-off point to divide patients into low- or high-risk groups. Based on this methodology, we were able to more effectively differentiate between these groups in terms of survival, clinico-pathological characteristics, tumor immune infiltrating status, chemotherapeutics sensitivity, and immunosuppressive molecules. Results: A 13-irlncRNA-pair signature was built, and the ROC analysis demonstrated high sensitivity and specificity of this signature for survival prediction. The Kaplan-Meier analysis indicated that the high-risk group had a significantly shorter survival rate than the low-risk group, and the chi-squared test certified that the signature was highly related to survival status, clinical stage, T stage, and N stage. Additionally, the signature was further proven to be an independent prognostic risk factor via the Cox regression analyses, and immune infiltrating analyses showed that the high-risk group had significant negative relationships with various immune infiltrations. Finally, the chemotherapeutics sensitivity and the expression level of molecular markers were also significantly different between high- and low-risk groups. Conclusion: The signature established by paring irlncRNAs, with regard to specific expression levels, can be utilized for survival prediction and to guide clinical therapy in HNSCC.

A novel immune-related long non-coding RNA signature improves the prognosis prediction in the context of head and neck squamous cell carcinoma

The tumor immune microenvironment plays an important role in head and neck squamous cell carcinoma (HNSCC). Reliable prognostic signatures able to accurately predict the immune landscape and survival rate of HNSCC patients are crucial to ensure an individualized/effective treatment. Here, we used HNSCC transcriptomic and clinical data retrieved from The Cancer Genome Atlas and identified differentially expressed immune-related long non-coding RNAs (DEirlncRNAs). DEirlncRNA pairs were recognized using univariate analysis. Cox and Lasso regression analyses were used to determine the association between DEirlncRNA pairs and the patients’ overall survival and build the prediction model. Receiver operating characteristic curves and Kaplan–Meier survival curves were used to validate the prediction model. We then reevaluated the model based on the clinical factors, tumor-infiltrating immune cells, chemotherapeutic efficacy, and immunosuppression biomarkers. We built a risk score model based on 18 DEirlncRNA pairs, closely related to the overall survival of patients (hazard ratio: 1.376; 95% confidence interval: 1.302–1.453; P < 0.0001). Compared with two recently published lncRNA signatures, our DEirlncRNA pair signature had a higher area under the curve, indicating better prognostic performance. Additionally, the signature score positively correlated with aggressive HNSCC outcomes (low immunity score, significantly reduced CD8 + T cell infiltration, and low expression of immunosuppression biomarkers). However, high-risk patients might have high chemosensitivity. Overall, the lncRNAs signature established here shows promising clinical prediction and the effective disclosure of the tumor immune microenvironment in HNSCC patients; therefore, such signature might help distinguish patients that could benefit from immunotherapy.

Integrative Analysis of DNA Methylation Data and Transcriptome Data Identified a DNA Methylation-Dysregulated Four-LncRNA Signature for Predicting Prognosis in Head and Neck Squamous Cell Carcinoma

Increasing evidence has demonstrated the crosstalk between DNA epigenetic alterations and aberrant expression of long non-coding RNAs (lncRNAs) during carcinogenesis. However, epigenetically dysregulated lncRNAs and their functional and clinical roles in Head and Neck Squamous Cell Carcinoma (HNSCC) are still not explored. In this study, we performed an integrative analysis of DNA methylation data and transcriptome data and identified a DNA methylation-dysregulated four-lncRNA signature (DNAMeFourLncSig) from 596 DNA methylation-dysregulated lncRNAs using a machine-learning-based feature selection method, which classified the patients of the discovery cohort into two risk groups with significantly different survival including overall survival, disease-specific survival, and progression-free survival. Then the DNAMeFourLncSig was implemented to another two HNSCC patient cohorts and showed similar prognostic values in both. Results from multivariable Cox regression analysis revealed that the DNAMeFourLncSig might be an independent prognostic factor. Furthermore, the DNAMeFourLncSig was substantially correlated with the complete response rate of chemotherapy and may predict chemotherapy response. Functional in silico analysis found that DNAMeFourLncSig-related mRNAs were mainly enriched in cell differentiation, tissue development and immune-related pathways. Overall, our study will improve our understanding of underlying transcriptional and epigenetic mechanisms in HNSCC carcinogenesis and provided a new potential biomarker for the prognosis of patients with HNSCC.

Oral microbiota and vitamin D impact on oropharyngeal squamous cell carcinogenesis: a narrative literature review

An emerging body of research is revealing the microbiota pivotal involvement in determining the health or disease state of several human niches, and that of vitamin D also in extra-skeletal regions. Nevertheless, much of the oral microbiota and vitamin D reciprocal impact in oropharyngeal squamous cell carcinogenesis (OPSCC) is still mostly unknown. On this premise, starting from an in-depth scientific bibliographic analysis, this narrative literature review aims to show a detailed view of the state of the art on their contribution in the pathogenesis of this cancer type. Significant differences in the oral microbiota species quantity and quality have been detected in OPSCC-affected patients; in particular, mainly high-risk human papillomaviruses (HR-HPVs), Fusobacterium nucleatum, Porphyromonas gingivalis, Pseudomonas aeruginosa, and Candida spp. seem to be highly represented. Vitamin D prevents and fights infections promoted by the above identified pathogens, thus confirming its homeostatic function on the microbiota balance. However, its antimicrobial and antitumoral actions, well-described for the gut, have not been fully documented for the oropharynx yet. Deeper investigations of the mechanisms that link vitamin D levels, oral microbial diversity and inflammatory processes will lead to a better definition of OPSCC risk factors for the optimization of specific prevention and treatment strategies.

Multifaceted Roles of Long Non-coding RNAs in Head and Neck Cancer

The majority of RNA transcripts are non-coding RNA (ncRNA) transcripts with lengths exceeding 200 nucleotides that are not translated into protein. Unlike microRNAs (miRNAs), long ncRNAs (lncRNAs) are not confined to a single mechanism of action but have a large and diverse role in biological processes as they can function as transcription regulators, decoys, scaffolds, and enhancer RNAs. Currently, many lncRNA molecules are under investigation for their role in tumorigenesis, metastasis, and prognosis in different types of cancer. This review not only summarizes the characteristics and functions of lncRNAs but also discusses the therapeutic implications and applications of lncRNAs with roles associated with head and neck cancer. Our aim is to pinpoint the potential way to perturb specific lncRNAs for future therapeutic use.

TP53-Associated Ion Channel Genes Serve as Prognostic Predictor and Therapeutic Targets in Head and Neck Squamous Cell Carcinoma

TP53 mutations are the most occurred mutation in HNSCC which might affect the ion channel genes. We aim to investigate the ion channel gene alteration under TP53 mutation and their prognostic implication. The overall mutation status of HNSCC were explored. By screening the TP53-associated ion channel genes (TICGs), an ion channel prognostic signature (ICPS) was established through a series of machine learning algorithms. The ICPS was then evaluated and its clinical significance was explored. 82 TICGs differentially expressed between TP53WT and TP53MUT were screened. Using univariate regression analysis and LASSO regression analysis and multivariate regression analysis, an ICPS containing 7 ion channel genes was established. A series of evaluation was carried out which proved the predictive ability of ICPS. Functional analysis of ICPS revealed that cancer-related pathways were enriched in high-risk group. Next, for clinical application, a nomogram was constructed based on ICPS and other independent clinicopathological factors. TP53 mutation status strongly affects the expression of ion channel genes. The ICPM we have identified is a strong indicator for HNSCC prognosis and could help with patient stratification as well as identification of novel drug targets.

Identification of a nomogram based on an 8-lncRNA signature as a novel diagnostic biomarker for head and neck squamous cell carcinoma

Long noncoding RNAs (lncRNAs) have been proposed as diagnostic or prognostic biomarkers of head and neck squamous carcinoma (HNSCC). The current study aimed to develop a lncRNA-based prognostic nomogram for HNSCC. LncRNA expression profiles were downloaded from The Cancer Genome Atlas (TCGA) database. After the reannotation of lncRNAs, the differential analysis identified 253 significantly differentially expressed lncRNAs in training set TCGA-HNSC (n = 300). The prognostic value of each lncRNA was first estimated in univariate Cox analysis, and 41 lncRNAs with P < 0.05 were selected as seed lncRNAs for Cox LASSO regression, which identified 11 lncRNAs. Multivariate Cox analysis was used to establish an 8-lncRNA signature with prognostic value. Patients in the high-signature score group exhibited a significantly worse overall survival (OS) than those in the low-signature score group, and the area under the receiver operating characteristic (ROC) curve for 3-year survival was 0.74. Multivariable Cox regression analysis among the clinical characteristics and signature scores suggested that the signature is an independent prognostic factor. The internal validation cohort, external validation cohort, and 102 HNSCC specimens quantified by qRT-PCR successfully validate the robustness of our nomogram.

Identification and Clinical Validation of 4-lncRNA Signature for Predicting Survival in Head and Neck Squamous Cell Carcinoma

Background

The prognosis of patients with head and neck squamous cell carcinoma (HNSCC) is still poor due to the lack of effective prognostic biomarkers. lncRNA is an important survival prognostic indicator and has important biological functions in tumorigenesis.

Methods

RNA-seq was re-annotated, and comprehensive clinical information was obtained from the GEO database. Univariate and multivariate Cox regression analyses were used to construct the lncRNA prognosis signature. Gene set enrichment analysis (GSEA) enrichment analysis method is used to explore the possible mechanism of the selected lncRNA influencing HNSCC development. The rms package was used to calculate the C-index to evaluate the overall prediction performance between different signature. PCR is used to detect the expression of selected lncRNA in cancer and adjacent tissues.

Results

In the GSE65858 training cohort, 124 probes significantly related to prognosis were identified, 11 significant lncRNAs were further selected by rbsurv dimensionality reduction analysis. Finally, 4-lncRNA signature was constructed by multivariate Cox analysis. This signature was associated with tumor-associated pathway and is an independent factor of the patient’s prognosis. 4-lncRNA signature has strong robustness and can exert stable prediction performance in different cohorts. A nomogram comprising the prognostic model to predict the overall survival was established. The 4-lncRNA signature was significantly upregulated in HNSCC samples.

Conclusion

The predictive model and nomogram will enable patients to be more accurately managed in trials and clinical practices and could be applied as a new prognostic model for predicting survival of HNSCC patients.

An RNA-sequencing-based transcriptome for a significantly prognostic novel driver signature identification in bladder urothelial carcinoma

Bladder cancer (BC) is the ninth most common malignancy worldwide. Bladder urothelial carcinoma (BLCA) constitutes more than 90% of bladder cancer (BC). The five-year survival rate is 5–70%, and patients with BLCA have a poor clinical outcome. The identification of novel clinical molecular markers in BLCA is still urgent to allow for predicting clinical outcomes. This study aimed to identify a novel signature integrating the three-dimension transcriptome of protein coding genes, long non-coding RNAs, microRNAs that is related to the overall survival of patients with BLCA, contributing to earlier prediction and effective treatment selection, as well as to the verification of the established model in the subtypes identified. Gene expression profiling and the clinical information of 400 patients diagnosed with BLCA were retrieved from The Cancer Genome Atlas (TCGA) database. A univariate Cox regression analysis, robust likelihood-based survival modelling analysis and random forests for survival regression and classification algorithms were used to identify the critical biomarkers. A multivariate Cox regression analysis was utilized to construct a risk score formula with a maximum area under the curve (AUC = 0.7669 in the training set). The significant signature could classify patients into high-risk and low-risk groups with significant differences in overall survival time. Similar results were confirmed in the test set (AUC = 0.645) and in the entire set (AUC = 0.710). The multivariate Cox regression analysis indicated that the five-RNA signature was an independent predictive factor for patients with BLCA. Non-negative matrix factorization and a similarity network fusion algorithm were applied for identifying three molecular subtypes. The signature could separate patients in every subtype into high- and low- groups with a distinct difference. Gene set variation analysis of protein-coding genes associated with the five prognostic RNAs demonstrated that the co-expressed protein-coding genes were involved in the pathways and biological process of tumourigenesis. The five-RNA signature could serve as to some degree a reliable independent signature for predicting outcome in patients with BLCA.

A transcriptional co-expression network-based approach to identify prognostic biomarkers in gastric carcinoma

Background

Gastric carcinoma is a very diverse disease. The progression of gastric carcinoma is influenced by complicated gene networks. This study aims to investigate the actual and potential prognostic biomarkers related to survival in gastric carcinoma patients to further our understanding of tumor biology.

Methods

A weighted gene co-expression network analysis was performed with a transcriptome dataset to identify networks and hub genes relevant to gastric carcinoma prognosis. Data was obtained from 300 primary gastric carcinomas (GSE62254). A validation dataset (GSE34942 and GSE15459) and TCGA dataset confirmed the results. Gene ontology, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and gene set enrichment analysis (GSEA) were performed to identify the clusters responsible for the biological processes and pathways of this disease.

Results

A brown transcriptional module enriched in the organizational process of the extracellular matrix was significantly correlated with overall survival (HR = 1.586, p = 0.005, 95% CI [1.149–2.189]) and disease-free survival (HR = 1.544, p = 0.008, 95% CI [1.119–2.131]). These observations were confirmed in the validation dataset (HR = 1.664, p = 0.006, 95% CI [1.155–2.398] in overall survival). Ten hub genes were identified and confirmed in the validation dataset from this brown module; five key biomarkers (COL8A1, FRMD6, TIMP2, CNRIP1 and GPR124 (ADGRA2)) were identified for further research in microsatellite instability (MSI) and epithelial-tomesenchymal transition (MSS/EMT) gastric carcinoma molecular subtypes. A high expression of these genes indicated a poor prognosis.

Conclusion

A transcriptional co-expression network-based approach was used to identify prognostic biomarkers in gastric carcinoma. This method may have potential for use in personalized therapies, however, large-scale randomized controlled clinical trials and replication experiments are needed before these key biomarkers can be applied clinically.

Application of Machine Learning in Developing a Novelty Five-Pseudogene Signature to Predict Prognosis of Head and Neck Squamous Cell Carcinoma: A New Aspect of "Junk Genes" in Biomedical Practice

Head and neck squamous cell carcinoma (HNSCC) is the sixth malignancy, which is characterized by poor prognosis or high mortality because of the lack of predicting markers. Aberrant cancer pseudogenes have been found predictive for prognosis. We aim to identify a pseudogene-based prognosis signature for HNSCC by machine learning. RNA-seq data were downloaded from The Cancer Genome Atlas, and 700 differentially-expressed pseudogenes were identified. The survival-related pseudogenes were screened through COX-regression analysis, which includes univariate regression, least absolute shrinkage and selection operator regression, and multivariate regression, and a five-pseudogene signature was constructed. The value of prediction for the signature was validated in multiple subgroups in terms of survival. Gene set enrichment analysis (GSEA) and coexpression analysis were used to determine the underlying biological functions. Seven hundred dysregulated pseudogenes were identified, and the five-pseudogene signature can distinguish the low-risk and high-risk patients for both training and testing sets and predicted prognosis with high sensitivity and specificity. Furthermore, the signature was applicable to patients of different genders, ages, stages, and grades. Coexpression analysis revealed that the five-pseudogene is associated with immune system. GSEA showed cancer-related biological process and pathways the five-pseudogene involved in. The five-pseudogene signature is not only a novel marker for prognosis but also a promising signature for monitoring therapeutic schedule. Therefore, our findings may have potential clinical significance.

miR-30e-5p represses angiogenesis and metastasis by directly targeting AEG-1 in squamous cell carcinoma of the head and neck

Metastasis is a critical determinant for the treatment strategy and prognosis in patients with squamous cell carcinoma of the head and neck (SCCHN). However, the mechanisms underlying SCCHN metastasis are poorly understood. Our study sought to determine the key microRNA and their functional mechanisms involved in SCCHN metastasis. For The Cancer Genome Atlas (TCGA) data analysis, quantitative PCR was used to quantify the level of miR-30e-5p in SCCHN and its clinical significance was further analyzed. A series of in vitro and in vivo experiments were applied to determine the effects of miR-30e-5p and its target AEG-1 on SCCHN metastasis. A mechanism investigation further revealed that AEG-1 was implicated in the angiogenesis and metastasis mediated by miR-30e-5p. Overall, our study confirms that miR-30e-5p is a valuable predictive biomarker and potential therapeutic target in SCCHN metastasis.

Transcriptomics and Epigenomics in head and neck cancer: available repositories and molecular signatures

For many years, head and neck squamous cell carcinoma (HNSCC) has been considered as a single entity. However, in the last decades HNSCC complexity and heterogeneity have been recognized. In parallel, high-throughput omics techniques had allowed picturing a larger spectrum of the behavior and characteristics of molecules in cancer and a large set of omics web-based tools and informative repository databases have been developed. The objective of the present review is to provide an overview on biological, prognostic and predictive molecular signatures in HNSCC. To contextualize the selected data, our literature survey includes a short summary of the main characteristics of omics data repositories and web-tools for data analyses. The timeframe of our analysis was fixed, encompassing papers published between January 2015 and January 2019. From more than 1000 papers evaluated, 61 omics studies were selected: 33 investigating mRNA signatures, 11 and 13 related to miRNA and other non-coding-RNA signatures and 4 analyzing DNA methylation signatures. More than half of identified signatures (36) had a prognostic value but only in 10 studies selection of a specific anatomical sub-site (8 oral cavity, 1 oropharynx and 1 both oral cavity and oropharynx) was performed. Noteworthy, although the sample size included in many studies was limited, about one-half of the retrieved studies reported an external validation on independent dataset(s), strengthening the relevance of the obtained data. Finally, we highlighted the development and exploitation of three gene-expression signatures, whose clinical impact on prognosis/prediction of treatment response could be high. Based on this overview on omics-related literature in HNSCC, we identified some limits and strengths. The major limits are represented by the low number of signatures associated to DNA methylation and to non-coding RNA (miRNA, lncRNA and piRNAs) and the availability of a single dataset with multiple omics on more than 500 HNSCC (i.e. TCGA). The major strengths rely on the integration of multiple datasets through meta-analysis approaches and on the growing integration among omics data obtained on the same cohort of patients. Moreover, new approaches based on artificial intelligence and informatic analyses are expected to be available in the next future.

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.

Integrative Analysis of Methylation and Transcriptome Identified Epigenetically Regulated lncRNAs With Prognostic Relevance for Thyroid Cancer

Emerging evidence has shown that epigenetic changes in DNA methylation, an important regulator of long non-coding RNA (lncRNA) expression, can disturb the expression patterns of lncRNAs and contribute to carcinogenesis. However, knowledge about crosstalk effects between DNA methylation and lncRNA regulation in thyroid cancer (THCA) remain largely unknown. In this study, we performed an integrated analysis of methylation and the transcriptome and identified 483 epigenetically regulated lncRNAs (EpilncRNAs) associated with the development and progression of THCA. These EpilncRNAs can be divided into two categories based on their methylation and expression patterns: 228 HyperLncRNAs and 255 HypoLncRNAs. Then, we identified a methylation-driven 5-lncRNA-based signature (EpiLncPM) to improve prognosis prediction using the random survival forest and multivariate Cox analysis, which were then validated using the training dataset [Hazard ratio (HR) = 50.097, 95% confidence interval (CI): 10.231-245.312, p < 0.001] and testing dataset (HR = 4.395, 95% CI: 0.981-19.686, p = 0.053). Multivariate analysis suggested that the EpiLncPM is an independent prognostic factor. By performing a functional enrichment analysis of GO and KEGG for mRNAs co-expressed with the EpiLncPM, we found that the EpiLncPM was involved in immune and inflammatory-related biological processes. Finally, in situ hybridization analysis in 119 papillary thyroid carcinoma (PTC) tissues and paired adjacent normal tissues revealed that selected candidate lncRNA AC110011 has significantly higher expression of PTC compared to adjacent non-neoplastic tissues, and was closely related to the tumor size, lymph node metastasis, and extrathyroidal extension. In summary, our study characterized the crosstalk between DNA methylation and lncRNA, and provided novel biomarkers for the prognosis of THCA.

OTUD6B-AS1 Inhibits Viability, Migration, and Invasion of Thyroid Carcinoma by Targeting miR-183-5p and miR-21

Background: The long noncoding RNA (lncRNA) functions as a regulator of initiation, progression, and metastasis of thyroid carcinomas. lncRNA OTUD6B antisense RNA 1 (OTUD6B-AS1) is a tumor-suppressive noncoding RNA in clear cell renal cell carcinoma. The role of OTUD6B-AS1 in thyroid carcinomas has not been reported yet. We aim to investigate the expression and biological functions of OTUD6B-AS1 in thyroid carcinomas. Methods: The expression level of OTUD6B-AS1 was measured in 60 paired human thyroid carcinoma tissues and corresponding adjacent normal thyroid tissues. The correlations between the OTUD6B-AS1 expression levels and clinicopathological features were evaluated using the Mann-Whitney test. The effects of OTUD6B-AS1 on thyroid carcinoma cells were determined via the MTT and transwell assays. The potential targets of OTUD6B-AS1 were screened using the online programs OncomiR and StarBase 3.0, and the LncBase Predicted v.2. Luciferase reporter assay was used to confirm the interactions between OTUD6B-AS1 and its potential targets. Results: OTUD6B-AS1 was downregulated in thyroid carcinoma tissue samples. The expression of OTUD6B-AS1 correlated with tumor size, clinical stage, and lymphatic metastasis of thyroid carcinoma. Overexpression of OTUD6B-AS1 significantly decreased the viability, migration, and invasion of thyroid carcinoma cells. Online programs predicted miR-183-5p and miR-21 as potential targets of OTUD6B-AS1. Luciferase reporter assays showed miR-183-5p and miR-21 bound to OTUD6B-AS1. Moreover, overexpression of miR-183-5p and miR-21 compromised the inhibitory effects of OTUD6B-AS1 on viability, migration, and invasion of thyroid carcinoma cells. Conclusions: Taken together, our findings present in vitro evidence of lncRNA OTUD6B-AS1 as a tumor suppressor in thyroid carcinomas. OTUD6B-AS1 inhibits viability, migration, and invasion of thyroid carcinoma by targeting miR-183-5p and miR-21.

Identification and Validation of an Immune-Related RNA Signature to Predict Survival of Patients With Head and Neck Squamous Cell Carcinoma

Head and neck squamous cell carcinoma (HNSCC) is a heterogeneous disease characterized by different molecular subgroups and clinical features. Therefore, it is important to uncover reliable molecular biomarkers for distinguishing different risk patient subgroup. Here, we conducted a multi-omics analysis to examine the joint predictive power of a multi-type RNA signature in the prognosis of HNSCC patients through integration analysis of mRNA, miRNA, and lncRNA expression profiles and clinical data in a large number of HNSCC patients. A multi-type RNA signature (15SigRS) was constructed which can classify patients into the high-risk group and low-risk group with the significantly different outcome [hazard ratio (HR) = 2.718, 95% confidence interval (CI), 2.258–3.272, p < 0.001] in the discovery data set, and subsequently validated in the Cancer Genome Atlas (TCGA) testing data set (HR = 1.299, 95% CI, 1.170–1.442, p < 0.001) and another independent GSE65858 data set (HR = 1.077, 95% CI, 1.016–1.143, p = 0.013). Further multivariate Cox regression analysis and stratification analysis demonstrated the independence of predictive performance of the 15SigRS relative to conventional clinicopathological factors. Furthermore, the 15SigRS has a prior performance in prognostic prediction than other single RNA type-based signatures. Functional analysis suggested that the 15SigRS are involved in immune- or metabolism-related KEGG pathways. In summary, our study demonstrated the potential application of mixed RNA types as molecular markers for predicting the outcome of cancer patients.

An integrated nomogram combining lncRNAs classifier and clinicopathologic factors to predict the recurrence of head and neck squamous cell carcinoma

Long non-coding RNAs (lncRNAs) which have little or no protein-coding capacity, due to their potential roles in the cancer disease, caught a particular interest. Our study aims to develop an lncRNAs-based classifier and a nomogram incorporating the lncRNAs classifier and clinicopathologic factors to help to improve the accuracy of recurrence prediction for head and neck squamous cell carcinoma (HNSCC) patients. The HNSCC lncRNAs profiling data and the corresponding clinicopathologic information were downloaded from TANRIC database and cBioPortal. Using univariable Cox regression and Least absolute shrinkage and selection operator (LASSO) analysis, we developed 15-lncRNAs-based classifier related to recurrence. On the basis of multivariable Cox regression analysis results, a nomogram integrating the genomic and clinicopathologic predictors was built. The predictive accuracy and discriminative ability of the inclusive nomogram were confirmed by calibration curve and a concordance index (C-index), and compared with TNM stage system by C-index, receiver operating characteristic (ROC) analysis. Decision curve analysis (DCA) was conducted to evaluate clinical value of our nomogram. Consequently, fifteen recurrence-free survival (RFS) -related lncRNAs were identified, and the classifier consisting of the established 15 lncRNAs could effectively divide patients into high-risk and low-risk subgroup. The prediction ability of the 15-lncRNAs-based classifier for predicting 3- year and 5-year RFS were 0.833 and 0.771. Independent factors derived from multivariable analysis to predict recurrence were number of positive LNs, margin status, mutation count and lncRNAs classifier, which were all embedded into the nomogram. The calibration curve for the recurrence probability showed that the predictions based on the nomogram were in good coincide with practical observations. The C-index of the nomogram was 0.76 (0.72–0.79), and the area under curve (AUC) of nomogram in predicting RFS was 0.809, which were significantly higher than traditional TNM stage and 15-lncRNAs-based classifier. Decision curve analysis further demonstrated that our nomogram had larger net benefit than TNM stage and 15-lncRNAs-based classifier. The results were confirmed externally. In summary, a visually inclusive nomogram for patients with HNSCC, comprising genomic and clinicopathologic variables, generates more accurate prediction of the recurrence probability when compared TNM stage alone, but more additional data remains needed before being used in clinical practice.

A Genomic-Clinicopathologic Nomogram Predicts Survival for Patients with Laryngeal Squamous Cell Carcinoma

Background

Long noncoding RNAs (lncRNAs), which have little or no ability to encode proteins, have attracted special attention due to their potential role in cancer disease. We aimed to establish a lncRNA signature and a nomogram incorporating the genomic and clinicopathologic factors to improve the accuracy of survival prediction for laryngeal squamous cell carcinoma (LSCC).

Methods

A LSCC RNA-sequencing (RNA-seq) dataset and the matched clinicopathologic information were downloaded from The Cancer Genome Atlas (TCGA). Using univariable Cox regression and least absolute shrinkage and selection operator (LASSO) analysis, we developed a thirteen-lncRNA signature related to prognosis. On the basis of multivariable Cox regression analysis results, a nomogram integrating the genomic and clinicopathologic predictors was built. The predictive accuracy and discriminative ability of the inclusive nomogram were confirmed by calibration curve and a concordance index (C-index), and compared with the TNM staging system by C-index and receiver operating characteristic (ROC) analysis. Decision curve analysis (DCA) was conducted to evaluate the clinical value of our nomogram.

Results

Thirteen overall survival- (OS-) related lncRNAs were identified, and the signature consisting of the selected thirteen lncRNAs could effectively divide patients into high-risk and low-risk subgroups, with area under curves (AUC) of 0.89 (3-year OS) and 0.885 (5-year OS). Independent factors derived from multivariable analysis to predict survival were margin status, tumor status, and lncRNA signature, which were all assembled into the nomogram. The calibration curve for the survival probability showed that the predictions based on the nomogram coincided well with actual observations. The C-index of the nomogram was 0.82 (0.77-0.87), and the area under curve (AUC) of the nomogram in predicting overall survival (OS) was 0.938, both of which were significantly higher than the traditional TNM stage. Decision curve analysis further demonstrated that our nomogram had larger net benefit than TNM stage.

Conclusion

An inclusive nomogram for patients with LSCC, comprising genomic and clinicopathologic variables, generates more accurate estimations of the survival probability when compared with TNM stage alone, but more data are needed before the nomogram is used in clinical practice.

Genome-Wide Identification of a Novel Eight-lncRNA Signature to Improve Prognostic Prediction in Head and Neck Squamous Cell Carcinoma

Objectives: LncRNAs are essential survival prognostic indicators with important biological functions in tumorigenesis and tumor progression. This study aimed to establish a long non-coding RNA (lncRNA) signature that can effectively predict the prognosis of patients with head and neck squamous cell carcinoma (HNSCC) and explore the potential functions of these lncRNAs.

Materials and Methods: We re-annotated RNA sequencing and obtained exhaustive RNA-seq data of 269 patients with comprehensive clinical information from the GEO database. Then an 8-lncRNA signature capable of predicting the survival prognosis of HNSCC patients and a nomogram containing this signature were established. Weighted Co-expression Network Construction (WGCNA), Gene Set Enrichment Analysis (GSEA), and Gene Ontology (GO) enrichment were then applied to predict the possible biological functions of the signature and each individual lncRNA.

Results: Eight lncRNAs associated with survival in HNSCC patients, including AC010624.1, AC130456.4, LINC00608, LINC01300, MIR99AHG, AC008655.1, AC055758.2, and AC118553.1, were obtained by univariate regression, cox LASSO regression, and multivariate regression. Functionally, patients with high signature scores had abnormal immune functions via GSEA. AC010624.1 and AC130456.4 may participate in epidermal cell differentiation and skin development, and MIR99AHG in the formation of cellular structures. Other lncRNAs in the signature may also participate in important biological processes.

Conclusions: Therefore, we established an 8-lncRNA signature that can effectively guide clinical prediction of the prognosis of patients with HNSCC, and individuals with high signature scores may have abnormal immune function.

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

Head and neck squamous cell carcinoma (HNSCC) is a common malignant disease with high mortality rates. Recently, long non-coding RNAs (lncRNAs) have been demonstrated to participate in a number of important biological functions and could serve as prognostic biomarkers in the field of oncology. Therefore, the present study aimed to identify an lncRNA-based model that was associated with prognosis. RNA-sequencing data was downloaded from The Cancer Genome Atlas and R software was used to analyze the data. Univariate analyses, robust likelihood analyses and multivariate analyses were performed to screen out key lncRNA candidates associated with prognosis and construct a risk model. A Kaplan-Meier plot was constructed for survival analysis. LncBase and Starbase were used to identify the miRNA and protein targets. Gene set enrichment analysis was used for functional analysis. As a result, a 4-lncRNA (ALMS1-IT1, RP11-359J14.2, CTB-178M22.2 and RP11-347C18.5) based risk model was identified and patients in the high-risk group were revealed to have a lower survival rate than patients in the low-risk group. A nomogram that could predict the survival of patients was plotted. A total of 79 target miRNAs and 61 target proteins were identified. The gene set enrichment analysis results revealed that nutrient metabolism pathways were enriched in the high-risk group and immune regulation pathways were enriched in the low-risk group. In summary, a 4-lncRNA based risk model was identified that was associated with prognosis, which may serve as a prognosis prediction biomarker for HNSCC.

Systematic Profile Analysis of Prognostic Alternative Messenger RNA Splicing Signatures and Splicing Factors in Head and Neck Squamous Cell Carcinoma

Head and neck squamous cell carcinoma (HNSC) is a common malignancy with high mortality and poor prognosis. Alternative splicing (AS) is a transcriptional regulation mechanism that generates multiple transcripts from same genes, and aberrant AS signatures of cancers can be predictive for prognosis. We identified the survival-related AS events and splicing factors (SFs) from the RNA sequencing data and the corresponding clinical information of an HNSC cohort downloaded from The Cancer Genome Atlas (TCGA) and SpliceSeq. The independent prognostic predictors were assessed by Cox proportional regression analysis, and the regulatory network of SFs and AS events was analyzed by Spearman's test and constructed. A total of 4626 survival-related AS events in 3280 genes were identified, and most were protective factors. Among the different types of splicing events, exon skip was the most frequent. The prognostic models were constructed for each type of AS, and the area under the curve of the receiver operating characteristic curve of the combined prognostic model was 0.765, indicating good predictive performance. Finally, a correlation network between SF and AS events was constructed. We identified prognostic predictors based on AS events that stratified HNSC patients into the high- and low-risk groups, and revealed splicing networks that provide insights into the underlying mechanisms.

lncRNA Expression after Irradiation and Chemoexposure of HNSCC Cell Lines

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cause of cancer mortality in the world. To improve the quality of diagnostics and patients' treatment, new and effective biomarkers are needed. Recent studies have shown that the expression level of different types of long non-coding RNAs (lncRNAs) is dysregulated in HNSCC and correlates with many biological processes. In this study, the response of lncRNAs in HNSCC cell lines after exposure to irradiation and cytotoxic drugs was examined. The SCC-040, SCC-25, FaDu, and Cal27 cell lines were treated with different radiation doses as well as exposed to cisplatin and doxorubicin. The expression changes of lncRNAs after exposure to these agents were checked by quantitative reverse transcription-polymerase chain reaction (qRT-PCR). Target prediction was performed using available online tools and classified into specific biological processes and cellular pathways. The results indicated that the irradiation, as well as chemoexposure, causes changes in lncRNA expression and the effect depends on the cell line, type of agents as well as their dose. After irradiation using the dose of 5 Gy significant dysregulation of 4 lncRNAs, 10 Gy-5 lncRNAs, and 20 Gy-3 lncRNAs, respectively, were observed in all cell lines. Only lncRNAs Zfhx2as was down-regulated in all cell lines independently of the dose used. After cisplatin exposure, 14 lncRNAs showed lower and only two higher expressions. Doxorubicin resulted in lower expressions of eight and increased four of lncRNAs. Common effects of cytotoxic drugs were observed in the case of antiPEG11, BACE1AS, PCGEM1, and ST7OT. Analysis of the predicted targets for dysregulated lncRNAs indicated that they are involved in important biological processes, regulating cellular pathways connected with direct response to irradiation or chemoexposure, cellular phenotype, cancer initiating cells, and angiogenesis. Both irradiation and chemoexposure caused specific changes in lncRNAs expression. However, the common effect is potentially important for cellular response to the stress and survival. Further study will show if lncRNAs are useful tools in patients' treatment monitoring.