Seven LncRNA-mRNA based risk score predicts the survival of head and neck squamous cell carcinoma

John M, Fuentes-Bayne H, Patel SP, Mardiros A, Xu H, Ng EW, Go WY, Wong DJ, Sunwoo JB, Welch JS. Genomic Immune Evasion: Diagnostic and Therapeutic Opportunities in Head and Neck Squamous Cell Carcinomas

Head and neck squamous cell cancers (HNSCCs) represent a diverse group of tumors emerging within different mucosal surfaces of the oral cavity, nasopharynx, oropharynx, larynx, and hypopharynx. HNSCCs share common clinical risk factors and genomic features, including smoking, alcohol, age, male sex, aneuploidy, and TP53 mutations. Viral initiating and contributing events are increasingly recognized in HNSCCs. While both Epstein-Barr Virus (EBV) and human papilloma virus (HPV) are observed, EBV is more frequently associated with nasopharyngeal cancers whereas HPV is associated with oropharyngeal cancers. HNSCCs are associated with high tumor mutational burden and loss of tumor suppressor gene function, especially in TP53 and X-linked genes. Multiple lines of evidence suggest that HNSCCs are subject to immunologic surveillance and immune-induced evolutionary pressure that correlate with negative clinical outcomes. This review will discuss genomic mechanisms related to immune-mediated pressures and propose prognostic and therapeutic implications of detectable immune escape mechanisms that drive tumorigenesis and disease progression.

An ion-channel-gene-based prediction model for head and neck squamous cell carcinoma: Prognostic assessment and treatment guidance

PURPOSE

Head and neck squamous cell carcinoma (HNSCC) is a very diverse malignancy with a poor prognosis. The purpose of this study was to develop a new signature based on 12 ion channel genes to predict the outcome and immune status of HNSCC patients.

METHODS

Clinicopathological information and gene sequencing data of HNSCC patients were generated from the Cancer Genome Atlas and Gene Expression Omnibus databases. A set of 323 ion channel genes was obtained from the HUGO Gene Nomenclature Committee database and literature review. Using univariate Cox regression analysis, the ion channel genes related to HNSCC prognosis were identified. A prognostic signature and nomogram were then created using machine learning methods. Kaplan-Meier analysis was used to explore the relevance of the risk scores and overall survival (OS). We also investigated the association between risk scores, tumor immune infiltration, and gene mutational status. Finally, we detected the expression levels of the signature genes by quantitative real-time polymerase chain reaction, western blotting, and immunohistochemistry.

RESULTS

We separated the patients into high- and low-risk groups according to the risk scores computed based on these 12 ion channel genes, and the OS of the low-risk group was significantly longer (p<0.001). The area under the curve for predicting 3-year survival was 0.729. Univariate and multivariate analyses showed that the 12-ion-channel-gene risk model was an independent prognostic factor. We also developed a nomogram model based on risk scores and clinicopathological variables to forecast outcomes. Furthermore, immune cell infiltration, gene mutation status, immunotherapy response, and chemotherapeutic treatment sensitivity were all linked to risk scores. Moreover, high expression levels of ANO1, AQP9, and BEST2 were detected in HNSCC tissues, whereas AQP5, SCNN1G, and SCN4A expression was low in HNSCC tissues, as determined by experiments.

CONCLUSION

The 12-ion-channel-gene prognostic signatures have been demonstrated to be highly efficient in predicting the prognosis, immune microenvironment, gene mutation status, immunotherapy response, and chemotherapeutic sensitivity of HNSCC patients.

Identification and Validation of a Novel Prognostic Gene Model for Colorectal Cancer

Aims

Colon cancer (CRC), with high morbidity and mortality, is a common and highly malignant cancer, which always has a bad prognosis. So it is urgent to employ a reasonable manner to assess the prognosis of patients. We developed and validated a gene model for predicting CRC risk.

Methods

The Gene Expression Omnibus (GEO) database was used to extract the gene expression profiles of CRC patients (N = 181) from GEO to identify genes that were differentially expressed between CRC patients and controls and then stable signature genes by firstly using both robust likelihood-based modeling with 1000 iterations and random survival forest variable hunting algorithms. Cluster analysis using the longest distance method was drawn out, and Kaplan–Meier (KM) survival analysis was used to compare the clusters. Meanwhile, the risk score was evaluated in three independent datasets including the GEO and Illumina HiSeq sequencing platforms. The corresponding risk index was calculated, and samples were clustered into high- and low-risk groups according to the median. And survival ROC analysis was used to evaluate the prognostic model. Finally, the Gene Set Enrichment Analysis (GSEA) was performed for further functional enrichment analyses.

Results

A 10-gene model was obtained, including 7 negative impact factors (SLC39A14, AACS, ERP29, LAMP3, TMEM106C, TMED2, and SLC25A3) and 3 positive ones (CNPY2, GRB10, and PBK), which related with several important oncogenic pathways (KRAS signaling, TNF-α signaling pathway, and WNT signaling pathway) and several cancer-related cellular processes (epithelial mesenchymal transition and cellular apoptosis). By using colon cancer datasets from The Cancer Genome Atlas (TCGA), the model was validated in KM survival analysis (P ≤ 0.001) and significant analysis with recurrence time (P = 0.0018).

Conclusions

This study firstly developed a stable and effective 10-gene model by using novel combined methods, and CRC patients might be able to use it as a prognostic marker for predicting their survival and monitoring their long-term treatment.

Identification of a Seven-Differentially Expressed Gene-Based Recurrence-Free Survival Model for Melanoma Patients

Melanoma is a malignant tumor that originates in melanocytes of the skin or mucous membrane, which has a high mortality rate and worse prognosis. Therefore, perspective prognosis evaluation seems more important for patients' treatment. Gene expression profiles of melanoma were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, respectively. 130 consistent differentially expressed genes (DEGs) were identified between melanoma and nevus tissues from two GEO cohorts. Prognostic genes were identified by univariate analysis, and 20 of them were regarded to be associated with the recurrence-free survival (RFS) of melanoma patients. Then, the LASSO Cox regression analysis chose seven of them to establish a seven-DEG-based RFS predicting signature. We demonstrated that this model was more powerful to predict RFS risk than other individual clinical features and was able to independently predict the RFS outcomes in different subsets of patients. We attempted to search for the underlying mechanisms by analyzing the coexpression genes of the seven candidates, and the pathway enrichment analyses indicated that immune response-related pathways might play a critical role in melanoma progression. Finally, we establish a robust seven-DEG-based RFS predicting signature, which will facilitate the personalized treatment of melanoma 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.

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.

Identifying critical protein-coding genes and long non-coding RNAs in non-functioning pituitary adenoma recurrence

Non-functioning pituitary adenoma (NFPA) is a very common type of intracranial tumor. Monitoring and predicting the postoperative recurrence of NFPAs is difficult, as these adenomas do not present with serum hormone hypersecretion. Long non-coding RNAs (lncRNAs) and protein-coding genes (PCGs) play critical roles in the development and progression of numerous tumors. However, the complex network of RNA interactions related to the mechanisms underlying the postoperative recurrence of NFPA is still unclear. In the present study, 73 patients with NFPA were investigated using high-throughput sequencing and follow-up investigations. In total, 6 of these patients with recurrence within 1 year after surgery were selected as the fast recurrence group, and 6 patients with recurrence 5 years after surgery were selected as the slow recurrence group. By performing differential expression analysis of the fast recurrence and slow recurrence groups, a set of differentially expressed PCGs and lncRNAs were obtained (t-test, P<0.05). Next, protein-protein interaction coregulatory networks and lncRNA-mRNA coexpression networks were identified. In addition, the hub lncRNA-mRNA modules related to NFPA recurrence were further screened and transcriptome expression markers for NFPA regression were identified (log-rank test, P<0.05). Finally, the ability of the hub and module genes to predict recurrence and progression-free survival in patients with NFPA was evaluated. To confirm the credibility of the bioinformatic analyses, nucleolar protein 6 and LL21NC02-21A1.1 were randomly selected from among the genes with prognostic significance for validation by reverse transcription-quantitative PCR in another set of NFPA samples (n=9). These results may be helpful for evaluating the slow and rapid recurrence of NFPA after surgery and exploring the mechanisms underlying NFPA recurrence. Future effective biomarkers and therapeutic targets may also be revealed.

Fifteen mRNA-lncRNA expression-based signature predicted the survival of late-staged head and neck squamous cell carcinoma

Background: Gene expression is necessary for regulation in almost all biological processes, at the same time, it is related to the prognosis for head and neck squamous cell carcinoma (HNSCC). The prognosis of late-staged HNSCC is important because of its guiding significance on the therapy strategies.

Methods: In this work, we analyzed the relationship between gene expression and HNSCC in The Cancer Genome Atlas (TCGA) cohort, and optimized the panel with random forest survival analysis. Subsequently, a Cox multivariate regression-based model was developed to predict the clinical outcome of HNSCC. The performance of the model was assayed in the training cohort and validated in another three independent cohorts (GSE41614, E-TABM-302, E-MTAB-1328). The underlying pathways significantly associated with the model were identified. According to the results, patients of low-score group (median survival months: 27.4, 95% CI: 18.2–43) had a significant poor survival than those of high-score group (median survival months: 69.4, 95% CI: 58.7–72.1, P=2.7e-5), and the observation was repeatable in the other validation cohorts. Further analysis revealed that the model performed better than the other clinical indicators and is independent of these indicators.

Results: Comparison revealed that the model performed better than existing models for late HNSCC prognosis. Gene set enrichment analysis (GSEA) elucidated that the model was significantly associated with various cell processes and pathways.

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.

Comprehensive profiling identifies a novel signature with robust predictive value and reveals the potential drug resistance mechanism in glioma

Background

Gliomas are the most common and malignant brain tumors. The standard therapy is surgery combined with radiotherapy, chemotherapy, and/or other comprehensive methods. However, the emergence of chemoresistance is the main obstacle in treatment and its mechanism is still unclear.

Methods

We firstly developed a multi-gene signature by integrated analysis of cancer stem cell and drug resistance related genes. The Chinese Glioma Genome Atlas (CGGA, 325 samples) and The Cancer Genome Atlas (TCGA, 699 samples) datasets were then employed to verify the efficacy of the risk signature and investigate its significance in glioma prognosis. GraphPad Prism, SPSS and R language were used for statistical analysis and graphical work.

Results

This signature could distinguish the prognosis of patients, and patients with high risk score exhibited short survival time. The Cox regression and Nomogram model indicated the independent prognostic performance and high prognostic accuracy of the signature for survival. Combined with a well-known chemotherapy impact factor-MGMT promoter methylation status, this risk signature could further subdivide patients with distinct survival. Functional analysis of associated genes revealed signature-related biological process of cell proliferation, immune response and cell stemness. These mechanisms were confirmed in patient samples.

Conclusions

The signature was an independent and powerful prognostic biomarker in glioma, which would improve risk stratification and provide a more accurate assessment of personalized treatment.

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.

The clinical significance of collagen family gene expression in esophageal squamous cell carcinoma

Background

Esophageal squamous cell carcinoma (ESCC) is a subtype of esophageal cancer with high incidence and mortality. Due to the poor 5-year survival rates of patients with ESCC, exploring novel diagnostic markers for early ESCC is emergent. Collagen, the abundant constituent of extracellular matrix, plays a critical role in tumor growth and epithelial-mesenchymal transition. However, the clinical significance of collagen genes in ESCC has been rarely studied. In this work, we systematically analyzed the gene expression of whole collagen family in ESCC, aiming to search for ideal biomarkers.

Methods

Clinical data and gene expression profiles of ESCC patients were collected from The Cancer Genome Atlas and the gene expression omnibus databases. Bioinformatics methods, including differential expression analysis, survival analysis, gene sets enrichment analysis (GSEA) and co-expression network analysis, were performed to investigate the correlation between the expression patterns of 44 collagen family genes and the development of ESCC.

Results

A total of 22 genes of collagen family were identified as differentially expressed genes in both the two datasets. Among them, COL1A1, COL10A1 and COL11A1 were particularly up-regulated in ESCC tissues compared to normal controls, while COL4A4, COL6A5 and COL14A1 were notably down-regulated. Besides, patients with low COL6A5 expression or high COL18A1 expression showed poor survival. In addition, a 7-gene prediction model was established based on collagen gene expression to predict patient survival, which had better predictive accuracy than the tumor-node-metastasis staging based model. Finally, GSEA results suggested that collagen genes might be tightly associated with PI3K/Akt/mTOR pathway, p53 pathway, apoptosis, cell cycle, etc.

Conclusion

Several collagen genes could be potential diagnostic and prognostic biomarkers for ESCC. Moreover, a novel 7-gene prediction model is probably useful for predicting survival outcomes of ESCC patients. These findings may facilitate early detection of ESCC and help improves prognosis of the patients.

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.

Long non-coding RNA HOTAIR/microRNA-206 sponge regulates STC2 and further influences cell biological functions in head and neck squamous cell carcinoma

Objective

It is essential to characterize underlying molecular mechanism associated with head and neck squamous cell carcinoma (HNSCC) and identify promising therapeutic targets. Herein, we explored role of homeobox transcript antisense RNA (HOTAIR) in HNSCC to regulate stanniocalcin‐2 (STC2) by sponging microRNA‐206 (miR‐206).

Methods

HNSCC‐related differentially expressed genes and regulation network amongst HOTAIR, miR‐206 and STC2 were identified. Next, effect of HOTAIR on cell biological functions of HNSCC was identified after transfection of cells with HOTAIR overexpressed plasmids or siRNA against HOTAIR. PI3K/AKT signalling pathway‐related gene expression was measured after miR‐206 and STC2 were suppressed. Cell invasion, migration and proliferation were assessed. Finally, tumour growth was assessed to determine the effects of HOTAIR/miR‐206/STC2 axis in vivo.

Results

HOTAIR specifically bound to miR‐206 and miR‐206 targeted STC2. Downregulated HOTAIR or upregulated miR‐206 suppressed HNSCC cell proliferation, invasion and migration. miR‐206 inhibited PI3K/AKT signalling pathway by down‐regulating STC2. Besides, silenced HOTAIR or overexpressed miR‐206 repressed the tumour growth of nude mice with HNSCC.

Conclusion

HOTAIR regulated HNSCC cell biological functions by binding to miR‐206 through STC2.

Identification and validation of potential long non-coding RNA biomarkers in predicting survival of patients with head and neck squamous cell carcinoma

Long non-coding RNAs (lncRNAs) are frequently dysregulated in cancer and their aberrant expression has been associated with cancer diagnosis and prognosis, which suggests that they may be promising molecular biomarkers. However, understanding of the expression pattern of lncRNAs and their prognostic roles in head and neck squamous cell carcinoma (HNSCC) is relatively limited. In the current study, the prognostic value of lncRNA expression profiles in predicting the OS of patients with HNSCC was investigated by integrating clinical and profiling data from The Cancer Genome Atlas. A total of ten lncRNAs closely associated with the prognosis of patients with HNSCC were identified and may serve as novel biomarkers. This 10-lncRNA signature was used to classify patients into 2 groups with significantly different overall survival (OS) times (median OS time, 1.65 vs. 13.04 years; P<0.0001). This lncRNA signature was validated in an independent testing cohort. The results of multivariable Cox regression and stratification analyses revealed that the prognostic value of the 10-lncRNA signature was independent of other clinical and pathological factors for the survival of patients with HNSCC. Functional analysis demonstrated that lncRNA expression-based risk scoring may reflect the basic status of the immune response in the tumor microenvironment. The presented study demonstrated the value of a lncRNA signature as a potential biomarker to improve the clinical prognosis of patients with HNSCC.

The discovery of a novel eight-mRNA-lncRNA signature predicting survival of hepatocellular carcinoma patients

Increasing evidence indicates that the expressions of messenger RNAs (mRNAs) and long non-coding RNAs (lncRNAs) undergo a frequent and aberrant change in carcinogenesis and cancer development. But some research was carried out on mRNA-lncRNA signatures for prediction of hepatocellular carcinoma (HCC) prognosis. We aimed to establish an mRNA-lncRNA signature to improve the ability to predict HCC patients' survival. The subjects from the cancer genome atlas (TCGA) data set were randomly divided into two parts: training data set (n = 246) and testing data set (n = 124). Using computational methods, we selected eight gene signatures (five mRNAs and three lncRNAs) to generate the risk score model, which were significantly correlated with overall survival of patients with HCC in both training and testing data set. The signature had the ability to classify the patients in training data set into a high-risk group and low-risk group with significantly different overall survival (hazard ratio = 4.157, 95% confidence interval = 2.648-6.526, P < 0.001). The prognostic value was further validated in testing data set and the entire data set. Further analysis revealed that this signature was independent of tumor stage. In addition, Gene Set Enrichment Analysis suggested that high risk score group was associated with cell proliferation and division related pathways. Finally, we developed a well-performed nomogram integrating the prognostic signature and other clinical information to predict 3- and 5-year overall survival. In conclusion, the prognostic mRNAs and lncRNAs identified in our study indicate their potential role in HCC biogenesis. The risk score model based on the mRNA-lncRNA may be an efficient classification tool to evaluate the prognosis of patients' with HCC.

RELB: A novel prognostic marker for glioblastoma as identified by population-based analysis

Glioblastoma multiforme (GBM) is the most common and malignant type of glioma, with a poor prognosis for patients. The survival time of patients varies greatly due to the complexity of the human genome, which harbors diverse oncogenic drivers. In order to identify the specific driving factors, 325 glioma samples from the Chinese Glioma Genome Atlas (CGGA) database were analyzed in the present study. The level of RELB proto-oncogene, NF-κβ subunit (RELB) expression increased with the pathological grade progression of the gliomas, and higher expression levels were present in the mesenchymal subtype and isocitrate dehydrogenase 1 (IDH1) wild-type gliomas. This RELB expression pattern was identified in the CGGA database and observed in three large independent databases. In patients with GBM from the CGGA database, a higher RELB expression level was associated with a shorter survival time, a mesenchymal subtype and IDH1 wild-type gliomas. Kaplan-Meier survival analysis, survival nomograms and Cox analysis demonstrated an independent prognostic value for RELB expression. Moreover, biological function analysis indicated the association of RELB with the ‘immune response’, ‘cell activation’ and the ‘apoptotic process’. In addition, RELB expression levels exhibited a negative correlation with the levels of microRNA (miR)-139-5p and miR-139-3p. The present study identified the pathological and biological roles of RELB in glioma and revealed its independent prognostic effect. These results suggested that RELB may be used as a prognostic biomarker and potential therapeutic target in glioma.

Personalized prognostication in head and neck cancer: A systematic review of nomograms according to the AJCC precision medicine core (PMC) criteria

BACKGROUND

The American Joint Committee on Cancer (AJCC) Precision Medicine Core (PMC) has recognized the need for more personalized probabilistic predictions above the "TNM" staging system and has recently released a checklist of inclusion and exclusion criteria for evaluating prognostic models.

METHODS

A systematic review of articles in which nomograms were created for head and neck cancer (HNC) was carried out according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The AJCC PMC criteria were used to score the individual studies.

RESULTS

Forty-four studies were included in the final qualitative analysis. The mean number of inclusion criteria met was 9.3 out of 13, and the mean number of exclusion criteria met was 2.1 out of 3. Studies were generally of high quality, but no single study fulfilled all of the AJCC PMC criteria.

CONCLUSION

This is the first study to utilize the AJCC checklist to comprehensively evaluate the published prognostic nomograms in HNC. Future studies should attempt to adhere to the AJCC PMC criteria. Recommendations for future research are given.

SUMMARY

The AJCC recently released a set of criteria to grade the quality of prognostic cancer models. In this study, we grade all published nomograms for head and neck cancer according to the new guidelines.

Transcriptomic model-based lncRNAs and mRNAs serve as independent prognostic indicators in head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSC) is one of most common types of cancer worldwide, and mRNAs and long non-coding RNAs (lncRNAs) have been identified as prognostic biomarkers in HNSC. In the present study, using gene expression datasets from multiple platforms, survival-associated genes in HNSC were identified. Subsequently, a combination of 17 genes (14 mRNAs and 3 lncRNA) was optimized using random forest variable hunting and a risk score model for HNSC prognosis was developed using a cohort from The Cancer Genome Atlas. Patients with high-risk scores tend to have earlier disease recurrence and lower survival rates, compared with those with low-risk scores. This observation was further validated in three independent datasets (GSE41613, GSE10300 and E-MTAB-302). Association analysis revealed that the risk score is independent of other clinicopathological observations. On the basis of the results depicted in the nomogram, the risk score performs better in 3-year survival rate prediction than other clinical observations. In summary, the lncRNA-mRNA signature-based risk score successfully predicts the survival of HNSC and serves as an indicator of prognosis.

Identification of 4-lncRNA prognostic signature in head and neck squamous cell carcinoma

Deregulated long noncoding RNAs (lncRNA) have been critically implicated in tumorigenesis and serve as novel diagnostic and prognostic biomarkers. Here we sought to develop a prognostic lncRNA signature in patients with head and neck squamous cell carcinoma (HNSCC). Original RNA-seq data of 499 HNSCC samples were retrieved from The Cancer Genome Atlas database, which was randomly divided into training and testing set. Univariate Cox regression survival analysis, robust likelihood-based survival model and random sampling iterations were applied to identify prognostic lncRNA candidates in the training cohort. A prognostic risk score was developed based on the Cox coefficient of four individual lncRNA imputed as follows: (0.14546 × expression level of RP11-366H4.1) + (0.27106 × expression level of LINC01123) + (0.54316 × expression level of RP11-110I1.14) + (-0.48794 × expression level of CTD-2506J14.1). Kaplan-Meier analysis revealed that patients with high-risk score had significantly reduced overall survival as compared with those with low-risk score when patients in training, testing, and validation cohorts were stratified into high- or low-risk subgroups. Multivariate survival analysis further revealed that this 4-lncRNA signature was a novel and important prognostic factor independent of multiple clinicopathological parameters. Importantly, ROC analyses indicated that predictive accuracy and sensitivity of this 4-lncRNA signature outperformed those previously well-established prognostic factors. Noticeably, prognostic score based on quantification of these 4-lncRNA via qRT-PCR in another independent HNSCC cohort robustly stratified patients into subgroups with high or low survival. Taken together, we developed a robust 4-lncRNA prognostic signature for HNSCC that might provide a novel powerful prognostic biomarker for precision oncology.

A three-lncRNA expression signature predicts survival in head and neck squamous cell carcinoma (HNSCC)

Increasing evidence has shown that long non-coding RNAs (lncRNAs) have important biological functions and can be used as a prognostic biomarker in human cancers. However, investigation of the prognostic value of lncRNAs in head and neck squamous cell carcinoma (HNSCC) is in infancy. In the present study, we analyzed the lncRNA expression data in a large number of HNSCC patients (n=425) derived from The Cancer Genome Atlas (TCGA) to identify an lncRNA expression signature for improving the prognosis of HNSCC. Three lncRNAs are identified to be significantly associated with survival in the training dataset using Cox regression analysis. Three lncRNAs were integrated to construct an lncRNA expression signature that could stratify patients of training dataset into the high-risk group and low-risk group with significantly different survival time (median survival 1.85 years vs. 5.48 years; P=0.0018, log-rank test). The prognostic value of this three-lncRNA signature was confirmed in the testing and entire datasets, respectively. Further analysis revealed that the prognostic power of three-lncRNA signature was independent of clinical features by multivariate Cox regression and stratified analysis. These three lncRNAs were significantly associated with known genetic and epigenetic events by means of functional enrichment analysis. Therefore, our results indicated that the three-lncRNA expression signature can predict HNSCC patients’ survival.

Seven Genes Based Novel Signature Predicts Clinical Outcome and Platinum Sensitivity of High Grade IIIc Serous Ovarian Carcinoma

Background: As a major subtype of ovarian cancer, high grade FIGO stage IIIc serous ovarian carcinoma (HG3cSOC), has various prognosis due to genetic heterogeneity.

Methods: The transcriptome of 401 primary FIGO IIIc serous ovarian samples was screened, seven genes based prognostic model was developed. The prognostic valueof risk score in four different cohorts (TCGA-cohort, Poland-cohort, Japan-cohort and USA-cohort) was validated. The relationship between risk score and other clinical indicators were analyzed. The guide value of risk score for platinum-taxol chemotherapy was also assayed. Tissue microenvironment difference among samples with different risk scores was investigated.

Results: High-risk group (N=200, median survival months: 39.6, 95% CI: 35.9-46.3 months) had a significantly worse prognosis than low-risk group (N=201, median survival months: 52.6, 95% CI: 45.2-64.9 months;). The risk score's performance was validated in Japan-cohort (N=90, Poland-cohort (N=48) and USA-cohort (N=84). The risk score is independent from age, primary tumor size, grade and treatment methods and the performance of risk score is uniform in subgroups. Furthermore, the risk score predicted the response of HG3cSOC to platinum-based regimen after surgery, and this finding was further validated in newly collected China-cohort (N=102). Gene Set Enrichment Analysis (GSEA) and tumor infiltration analysis revealed that risk score reflected the immune infiltration and cell-cell interaction status, and the migration function of candidate genes were also verified.

Conclusions: The optimized seven genes-based model is a valuable and robust model in predicting the survival of HG3cSOC, and served as a valuable marker for the response to platinum-based chemotherapy.

Tissue Expression Difference between mRNAs and lncRNAs

Messenger RNA (mRNA) and long noncoding RNA (lncRNA) are two main subgroups of RNAs participating in transcription regulation. With the development of next generation sequencing, increasing lncRNAs are identified. Many hidden functions of lncRNAs are also revealed. However, the differences in lncRNAs and mRNAs are still unclear. For example, we need to determine whether lncRNAs have stronger tissue specificity than mRNAs and which tissues have more lncRNAs expressed. To investigate such tissue expression difference between mRNAs and lncRNAs, we encoded 9339 lncRNAs and 14,294 mRNAs with 71 expression features, including 69 maximum expression features for 69 types of cells, one feature for the maximum expression in all cells, and one expression specificity feature that was measured as Chao-Shen-corrected Shannon's entropy. With advanced feature selection methods, such as maximum relevance minimum redundancy, incremental feature selection methods, and random forest algorithm, 13 features presented the dissimilarity of lncRNAs and mRNAs. The 11 cell subtype features indicated which cell types of the lncRNAs and mRNAs had the largest expression difference. Such cell subtypes may be the potential cell models for lncRNA identification and function investigation. The expression specificity feature suggested that the cell types to express mRNAs and lncRNAs were different. The maximum expression feature suggested that the maximum expression levels of mRNAs and lncRNAs were different. In addition, the rule learning algorithm, repeated incremental pruning to produce error reduction algorithm, was also employed to produce effective classification rules for classifying lncRNAs and mRNAs, which gave competitive results compared with random forest and could give a clearer picture of different expression patterns between lncRNAs and mRNAs. Results not only revealed the heterogeneous expression pattern of lncRNA and mRNA, but also gave rise to the development of a new tool to identify the potential biological functions of such RNA subgroups.

Identification of genes that predict the biochemical recurrence of prostate cancer

Prostate cancer (PCa) is one of the most prevalent cancer types in men. Biochemical recurrence continues to occur in a large proportion of patients after radical prostatectomy. Thus, prognostic biomarkers are required to determine which treatment is suitable. In the present study, RNA-sequencing gene expression data from The Cancer Genome Atlas was used in order to develop a risk-score staging system based on the expression of eight genes. Cox multivariate regression was used to predict the outcome of patients with PCa. The biomedical recurrence-free survival of patients with low-risk scores was significantly longer compared with patients with high-risk scores (P=5×10⁻⁷). This result was further validated using another dataset, GSE70769, from the National Center for Biotechnology Information. The prognostic values of other clinical information and risk scores were evaluated for 5-year biochemical recurrence. The prognostic value of the risk score was determined using an area under curve value of 0.819, predicting the 5-year biochemical recurrence of patients with PCa. The risk score was identified to be significantly associated with primary tumor stage (P<0.01), Gleason score (P<0.01), and lymph node invasion (P<0.05), but was independent of age. Cox multivariate regression revealed that the risk score was an indicator for prediction of biochemical recurrence. Thus, the risk score is a valuable and robust indicator for predicting the biochemical recurrence of patients with PCa.

A novel gene signature based on five glioblastoma stem-like cell relevant genes predicts the survival of primary glioblastoma

PURPOSE

Primary glioblastoma (pGBM) is the most common and lethal type of neoplasms in the central nervous system, while the existing biomarkers, lacking consideration on the stemness changes of GBM cells, are not specific enough to predict the complex prognosis respectively. We aimed to build a high-efficiency prediction gene signature related to GBM cell stemness and investigate its prognostic value in primary glioblastoma.

METHODS

Differentially expressed genes were screened in GSE23806 database. The selected genes were then verified by univariate Cox regression in 591 patients from four enormous independent databases, including the Chinese Glioma Genome Atlas (CGGA), TCGA, REMBRANDT and GSE16011. Finally, the intersected genes were included to build the gene signature. GO analysis and GSEA were carried out to explore the bioinformatic implication.

RESULTS

The novel five-gene signature was used to identify high- and low-risk groups in the four databases, and the high-risk group showed notably poorer prognosis (P < 0.05). Gene ontology (GO) terms including "immune response", "apoptotic process", and "angiogenesis" were picked out by GO analysis and GSEA, which revealed that the gene signature was highly possibly related to the stemness of GSCs and predicting the prognosis of GBM effectively.

CONCLUSION

We built a gene signature with five glioblastoma stem-like cell (GSC) relevant genes, and predicted the survival in four independent databases effectively, which is possibly related to the stemness of GSCs in pGBM. Several GO terms were investigated to be correlated to the signature. The signature can predict the prognosis of glioblastoma efficiently.

A gene expression-based risk model reveals prognosis of gastric cancer

Background

The prognosis of gastric cancer is difficult to determine, although clinical indicators provide valuable evidence.

Methods

In this study, using screened biomarkers of gastric cancer in combination with random forest variable hunting and multivariable Cox regression, a risk score model was developed to predict the survival of gastric cancer. Survival difference between high/low-risk groups were compared. The relationship between risk score and other clinicopathological indicators was evaluated. Gene set enrichment analysis (GSEA) was used to identify pathways associated with risk scores.

Results

The patients with high risk scores (median overall survival: 20.2 months, 95% CI [16.9–26.0] months) tend to exhibit early events compared with those with low risk scores (median survival: 70.0 months, 95% CI [46.9–101] months, p = 1.80e–5). Further validation was implemented in another three independent datasets (GSE15459, GSE26253, GSE62254). Correlation analyses between clinical observations and risk scores were performed, and the results indicated that the risk score was not significantly associated with gender, age and primary tumor size but was significantly associated with grade and tumor stage. In addition, the risk score was also not influenced by radiation therapy. Cox multivariate regression and three-year survival nomogram suggest that the risk score is an important indicator of gastric cancer prognosis. GSEA was used to identified KEGG pathways significantly associated with risk score, and signaling pathways involved in focal adhesion and the TGF-beta signaling pathway were identified.

Conclusion

The risk score model successfully predicted the survival of 1,294 gastric cancer samples from four independent datasets and is among the most important indicators in clinical clinicopathological information for the prognosis of gastric cancer. To our knowledge, it is the first report to predict the survival of gastric cancer using optimized expression panel.

microRNAs and Acute Myeloid Leukemia Chemoresistance: A Mechanistic Overview

Up until the early 2000s, a functional role for microRNAs (miRNAs) was yet to be elucidated. With the advent of increasingly high-throughput and precise RNA-sequencing techniques within the last two decades, it has become well established that miRNAs can regulate almost all cellular processes through their ability to post-transcriptionally regulate a majority of protein-coding genes and countless other non-coding genes. In cancer, miRNAs have been demonstrated to play critical roles by modifying or controlling all major hallmarks including cell division, self-renewal, invasion, and DNA damage among others. Before the introduction of anthracyclines and cytarabine in the 1960s, acute myeloid leukemia (AML) was considered a fatal disease. In decades since, prognosis has improved substantially; however, long-term survival with AML remains poor. Resistance to chemotherapy, whether it is present at diagnosis or induced during treatment is a major therapeutic challenge in the treatment of this disease. Certain mechanisms such as DNA damage response and drug targeting, cell cycling, cell death, and drug trafficking pathways have been shown to be further dysregulated in treatment resistant cancers. miRNAs playing key roles in the emergence of these drug resistance phenotypes have recently emerged and replacement or inhibition of these miRNAs may be a viable treatment option. Herein, we describe the roles miRNAs can play in drug resistant AML and we describe miRNA-transcript interactions found within other cancer states which may be present within drug resistant AML. We describe the mechanisms of action of these miRNAs and how they can contribute to a poor overall survival and outcome as well. With the precision of miRNA mimic- or antagomir-based therapies, miRNAs provide an avenue for exquisite targeting in the therapy of drug resistant cancers.