A six-mRNA signature model for the prognosis of head and neck squamous cell carcinoma

PSMA2 promotes chemo- and radioresistance of oral squamous cell carcinoma by modulating mitophagy pathway

Oral cavity squamous cell carcinoma (OSCC) represents the most prevalent malignancy among head and neck squamous cell carcinomas (HNSCCs). Standard treatment modalities include surgical resection combined with radiation and chemotherapy. However, locoregional failure remains a critical issue affecting the prognosis of OSCC patients, largely due to tumor resistance against radiation or chemotherapy. In this study, we established a gene database related to OSCC recurrence and identified PSMA2 as a novel molecule influencing prognosis in OSCC patients. An independent Taiwanese cohort confirmed that elevated PSMA2 transcript levels were associated with poorer prognosis and contributed to the chemo- and radioresistance phenotype in OSCC. Furthermore, we confirmed that PSMA2 regulates cell cycle, mitochondrial dysfunction, and mitophagy, thereby contributing to carcinogenesis and resistance. Notably, mitophagy inducer exhibit antitumor effects in PSMA2-overexpressing OSCC xenograft mouse model. Collectively, our results provide a mechanistic understanding of the atypical function of PSMA2 in promoting OSCC recurrence.

The circRNA hsa-circ-0013561 regulates head and neck squamous cell carcinoma development via the miR-7-5p/PDK3 axis

BACKGROUND

Circular RNAs (circRNAs) belong to a class of covalently closed single stranded RNAs that have been implicated in cancer progression. Former investigations showed that hsa-circ-0013561 is abnormally expressed in head and neck squamous cell carcinoma (HNSCC). Nevertheless, the role of hsa-circ-0013561 during the progress of HNSCC still unclear.

METHODS

Present study applied FISH and qRT-PCR to examine hsa-circ-0013561 expression in HNSCC cells and tissue samples. Dual-luciferase reporter assay was employed to identify downstream targets of hsa-circ-0013561. Transwell migration, 5-ethynyl-2'-deoxyuridine incorporation, CCK8 and colony formation assays were utilized to test cell migration and proliferation. A mouse tumor xenograft model was utilized to determine the hsa-circ-0013561 roles in HNSCC progression and metastasis in vivo.

RESULTS

We found that hsa-circ-0013561 was upregulated in HNSCC tissue samples. hsa-circ-0013561 downregulation inhibited HNSCC cell proliferation and migration to promote apoptosis and G1 cell cycle arrest. The dual-luciferase reporter assay revealed that miR-7-5p and PDK3 are hsa-circ-0013561 downstream targets. PDK3 overexpression or miR-7-5p suppression reversed the hsa-circ-0013561-induced silencing effects on HNSCC cell proliferation and migration. PDK3 overexpression reversed miR-7-5p-induced effects on HNSCC cell proliferation and migration.

CONCLUSION

The findings suggest that hsa-circ-0013561 downregulation inhibits HNSCC metastasis and progression through PDK3 expression and miR-7-5p binding modulation.

Patients with Systemic Juvenile Idiopathic Arthritis (SJIA) Show Differences in Autoantibody Signatures Based on Disease Activity

Systemic juvenile idiopathic arthritis (SJIA) is a severe rheumatic disease in children. It is a subgroup of juvenile idiopathic arthritis (JIA; MIM #604302), which is the most common rheumatic disease in children. The diagnosis of SJIA often comes with a significant delay, and the classification between autoinflammatory and autoimmune disease is still discussed. In this study, we analyzed the immunological responses of patients with SJIA, using human proteome arrays presenting immobilized recombinantly expressed human proteins, to analyze the involvement of autoantibodies in SJIA. Results from group comparisons show several differentially reactive antigens involved in inflammatory processes. Intriguingly, many of the identified antigens had a high reactivity against proteins involved in the NF-κB pathway, and it is also notable that many of the detected DIRAGs are described as dysregulated in rheumatoid arthritis. Our data highlight novel proteins and pathways potentially dysregulated in SJIA and offer a unique approach to unraveling the underlying disease pathogenesis in this chronic arthropathy.

RNA-Based Liquid Biopsy in Head and Neck Cancer

Head and neck cancer (HNC) is a prevalent and diverse group of malignancies with substantial morbidity and mortality rates. Early detection and monitoring of HNC are crucial for improving patient outcomes. Liquid biopsy, a non-invasive diagnostic approach, has emerged as a promising tool for cancer detection and monitoring. In this article, we review the application of RNA-based liquid biopsy in HNC. Various types of RNA, including messenger RNA (mRNA), microRNA (miRNA), long non-coding RNA (lncRNA), small nuclear RNA (snRNA), small nucleolar RNA (snoRNA), circular RNA (circRNA) and PIWI-interacting RNA (piRNA), are explored as potential biomarkers in HNC liquid-based diagnostics. The roles of RNAs in HNC diagnosis, metastasis, tumor resistance to radio and chemotherapy, and overall prognosis are discussed. RNA-based liquid biopsy holds great promise for the early detection, prognosis, and personalized treatment of HNC. Further research and validation are necessary to translate these findings into clinical practice and improve patient outcomes.

MRI-based radiomic prognostic signature for locally advanced oral cavity squamous cell carcinoma: development, testing and comparison with genomic prognostic signatures

BACKGROUND

. At present, the prognostic prediction in advanced oral cavity squamous cell carcinoma (OCSCC) is based on the tumor-node-metastasis (TNM) staging system, and the most used imaging modality in these patients is magnetic resonance image (MRI). With the aim to improve the prediction, we developed an MRI-based radiomic signature as a prognostic marker for overall survival (OS) in OCSCC patients and compared it with published gene expression signatures for prognosis of OS in head and neck cancer patients, replicated herein on our OCSCC dataset.

METHODS

For each patient, 1072 radiomic features were extracted from T1 and T2-weighted MRI (T1w and T2w). Features selection was performed, and an optimal set of five of them was used to fit a Cox proportional hazard regression model for OS. The radiomic signature was developed on a multi-centric locally advanced OCSCC retrospective dataset (n = 123) and validated on a prospective cohort (n = 108).

RESULTS

The performance of the signature was evaluated in terms of C-index (0.68 (IQR 0.66-0.70)), hazard ratio (HR 2.64 (95% CI 1.62-4.31)), and high/low risk group stratification (log-rank p < 0.001, Kaplan-Meier curves). When tested on a multi-centric prospective cohort (n = 108), the signature had a C-index of 0.62 (IQR 0.58-0.64) and outperformed the clinical and pathologic TNM stage and six out of seven gene expression prognostic signatures. In addition, the significant difference of the radiomic signature between stages III and IVa/b in patients receiving surgery suggests a potential association of MRI features with the pathologic stage.

CONCLUSIONS

Overall, the present study suggests that MRI signatures, containing non-invasive and cost-effective remarkable information, could be exploited as prognostic tools.

circ_0000045 promotes proliferation, migration, and invasion of head and neck squamous cell carcinomas via regulating HSP70 and MAPK pathway

Objective

Head and neck squamous cell carcinoma (HNSCC) is one severe malignancy driven by complex cellular and signaling mechanisms. However, the roles of circular RNAs (circRNAs) in HNSCC’s development remains poorly understood. Therefore, this study investigated the functions of differentially expressed circRNAs in regulating HNSCC cell functions.

Methods

Differentially expressed circRNAs were characterized through RNA sequencing in HNSCC tissues. CircRNA’s identity was then confirmed using RT-PCR and Sanger’s sequencing. Next, expression levels of circRNA and mRNA were detected by qRT-PCR, after which protein abundances were measured by Western blotting. Subsequently, the proliferation, migration, and invasion of HNSCC cells was assessed by MTS, wound healing, and Transwell system, respectively, followed by identification of circRNA-binding proteins in HNSCC cells by circRNA pull-down, coupled with mass spectrometry.

Results

Great alterations in circRNA profiles were detected in HNSCC tissues, including the elevated expression of circ_0000045. As observed, silencing of circ_0000045 effectively repressed the proliferation, migration, and invasion of HNSCC cell lines (FaDu and SCC-9). Contrarily, circ_0000045’s overexpression promoted the proliferation, migration, and invasion in FaDu and SCC-9 cells. Results also showed that circ_0000045 was associated with multiple RNA-binding proteins in HNSCC cells, such as HSP70. Moreover, circ_0000045 knockdown enhanced HSP70 expression and inhibited JNK2 and P38’s expression in HNSCC cells, which were oppositely regulated by circ_0000045’s overexpression.

Conclusion

The high expression of circ_0000045; therefore, promoted cell proliferation, migration, and invasion during HNSCC’s development through regulating HSP70 protein and mitogen-activated protein kinase signaling.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09880-y.

The Prediction of a 3-Protein-Based Model on the Prognosis of Head and Neck Squamous Cell Carcinoma

Background

Head and neck squamous cell carcinoma (HNSCC) is one of the commonest malignant tumors. Using high-throughput genomic methods, RNA-based diagnostic and prognostic models for HNSCC with potential clinical value have been developed. However, the clinical utility and reproducibility of these models are uncertain. Because the complex regulatory processes occurring after mRNA is transcribed, the abundance of proteins in a cell can never be fully predicted or explained by their corresponding mRNA expression. We aimed to assume and verify a novel protein signature for checking the HNSCC patients' prognosis.

Methods

The functional proteomic data of 332 HNSCC cases were collected from The Cancer Proteome Atlas (TCPA), and the related follow-up and clinical data were acquired from The Cancer Genome Atlas (TCGA). This study adopted multivariate and univariate Cox regression analysis, Akaike Information Criterion, receiver operating characteristic (ROC) analysis, and Kaplan-Meier method.

Results

Patients' clinical features in both sets were comparable (all, P > 0.05). The area under the ROC curve (AUC) for the 3-protein signature (X4EBP1_pT37T46, HER3_pY1289, and NF2) in the test set was 0.655 and in the combined cohort (all 332 patients combined) was 0.699. In addition, the 3-protein signature exhibited better predictive value for the survival of HNSCC patients as in comparison with conventional clinical factors like age, gender, tumor stage, and smoking history (TNM stage).

Conclusion

The 3-protein signature developed in this study exhibits good performance in predicting the overall survival of with HNSCC patients. The 3-protein signature exhibited better predictive value for survival than conventional clinical factors just like gender, TNM stage, smoking history, and age.

The Protein L-Isoaspartyl (D-Aspartyl) Methyltransferase Regulates Glial-to-Mesenchymal Transition and Migration Induced by TGF-β1 in Human U-87 MG Glioma Cells

The enzyme PIMT methylates abnormal aspartyl residues in proteins. U-87 MG cells are commonly used to study the most frequent brain tumor, glioblastoma. Previously, we reported that PIMT isoform I possessed oncogenic features when overexpressed in U-87 MG and U-251 MG glioma cells. Higher levels of wild-type PIMT stimulated migration and invasion in both glioma cell lines. Conversely, PIMT silencing reduced these migratory abilities of both cell lines. These results indicate that PIMT could play a critical role in glioblastoma growth. Here, we investigated for the first time, molecular mechanisms involving PIMT in the regulation of epithelial to mesenchymal transition (EMT) upon TGF-β1 treatments. Gene array analyses indicated that EMT genes but not PIMT gene were regulated in U-87 MG cells treated with TGF-β1. Importantly, PIMT silencing by siRNA inhibited in vitro migration in U-87 MG cells induced by TGF-β1. In contrast, overexpressed wild-type PIMT and TGF-β1 had additive effects on cell migration. When PIMT was inhibited by siRNA, this prevented Slug induction by TGF-β1, while Snail stimulation by TGF-β1 was increased. Indeed, overexpression of wild-type PIMT led to the opposite effects on Slug and Snail expression dependent on TGF-β1. These data highlighted the importance of PIMT in the EMT response dependent on TGF-β1 in U-87 MG glioma cells by an antagonist regulation in the expression of transcription factors Slug and Snail, which are critical players in EMT.

General Features and Novel Gene Signatures That Identify Epstein-Barr Virus-Associated Epithelial Cancers

Epstein-Barr virus (EBV) is associated with various types of human malignancies, including nasopharyngeal carcinoma (NPC), EBV-associated gastric carcinoma (EBVaGC), and oral squamous cell carcinoma (OSCC). The present study aimed to identify gene signatures and common signaling pathways that can be used to predict the prognosis of EBV-associated epithelial cancers (EBVaCAs) by performing an integrated bioinformatics analysis of cell lines and tumor tissues. We identified 12 differentially expressed genes (DEGs) in the EBVaCA cell lines. Among them, only four DEGs, including BAMBI, SLC26A9, SGPP2, and TMC8, were significantly upregulated. However, SLC26A9 and TMC8, but not BAMBI and SGPP2, were significantly upregulated in EBV-positive tumor tissues compared to EBV-negative tumor tissues. Next, we identified IL6/JAK/STAT3 and TNF-α/NF-κB signaling pathways as common hallmarks of EBVaCAs. The expression of key genes related to the two hallmarks was upregulated in both EBV-infected cell lines and EBV-positive tumor tissues. These results suggest that SLC26A9 and TMC8 might be gene signatures that can effectively predict the prognosis of EBVaCAs and provide new insights into the molecular mechanisms of EBV-driven epithelial cancers.

Low Levels of TRIM28-Interacting KRAB-ZNF Genes Associate with Cancer Stemness and Predict Poor Prognosis of Kidney Renal Clear Cell Carcinoma Patients

Simple Summary

This is the first report investigating the involvement of TRIM28-interacting KRAB-ZNFs in kidney cancer progression. We demonstrate a significant negative association between KRAB-ZNFs and cancer stemness followed by an attenuated immune-suppressive response and reveal the prognostic role for several KRAB-ZNFs. Our findings may help better understand the molecular basis of kidney cancer and ultimately pave the way to more appropriate prognostic tools and novel therapeutic strategies directly eradicating the dedifferentiated compartment of the tumor.

Abstract

Krüppel-associated box zinc finger (KRAB-ZNF) proteins are known to regulate diverse biological processes, such as embryonic development, tissue-specific gene expression, and cancer progression. However, their involvement in the regulation of cancer stemness-like phenotype acquisition and maintenance is scarcely explored across solid tumor types, and to date, there are no data for kidney renal clear cell cancer (KIRC). We have harnessed The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database transcriptomic data and used several bioinformatic tools (i.e., GEPIA2, GSCALite, TISIDB, GSEA, CIBERSORT) to verify the relation between the expression and genomic alterations in KRAB-ZNFs and kidney cancer, focusing primarily on tumor dedifferentiation status and antitumor immune response. Our results demonstrate a significant negative correlation between KRAB-ZNFs and kidney cancer dedifferentiation status followed by an attenuated immune-suppressive response. The transcriptomic profiles of high KRAB-ZNF-expressing kidney tumors are significantly enriched with stem cell markers and show a depletion of several inflammatory pathways known for favoring cancer stemness. Moreover, we show for the first time the prognostic role for several KRAB-ZNFs in kidney cancer. Our results provide new insight into the role of selected KRAB-ZNF proteins in kidney cancer development. We believe that our findings may help better understand the molecular basis of KIRC.

Development of a Novel Prognostic Signature Based on Antigen Processing and Presentation in Patients with Breast Cancer

Background: Complex antigen processing and presentation processes are involved in the development and progression of breast cancer (BC). A single biomarker is unlikely to adequately reflect the complex interplay between immune cells and cancer; however, there have been few attempts to find a robust antigen processing and presentation-related signature to predict the survival outcome of BC patients with respect to tumor immunology. Therefore, we aimed to develop an accurate gene signature based on immune-related genes for prognosis prediction of BC. Methods: Information on BC patients was obtained from The Cancer Genome Atlas. Gene set enrichment analysis was used to confirm the gene set related to antigen processing and presentation that contributed to BC. Cox proportional regression, multivariate Cox regression, and stratified analysis were used to identify the prognostic power of the gene signature. Differentially expressed mRNAs between high- and low-risk groups were determined by KEGG analysis. Results: A three-gene signature comprising HSPA5 (heat shock protein family A member 5), PSME2 (proteasome activator subunit 2), and HLA-F (major histocompatibility complex, class I, F) was significantly associated with OS. HSPA5 and PSME2 were protective (hazard ratio (HR) < 1), and HLA-F was risky (HR > 1). Risk score, estrogen receptor (ER), progesterone receptor (PR) and PD-L1 were independent prognostic indicators. KIT and ACACB may have important roles in the mechanism by which the gene signature regulates prognosis of BC. Conclusion: The proposed three-gene signature is a promising biomarker for estimating survival outcomes in BC patients.

Identification of a Novel Epithelial-Mesenchymal Transition Gene Signature Predicting Survival in Patients With HNSCC

Head and neck squamous cell cancer (HNSCC) is one of the most common types of cancer worldwide. There have been many reports suggesting that biomarkers explored via database mining plays a critical role in predicting HNSCC prognosis. However, a single biomarker for prognostic analysis is not adequate. Additionally, there is growing evidence indicating that gene signature could be a better choice for HNSCC prognosis. We performed a comprehensive analysis of mRNA expression profiles using clinical information of HNSCC patients from The Cancer Genome Atlas (TCGA). Gene Set Enrichment Analysis (GSEA) was performed, and we found that a set of genes involved in epithelial mesenchymal transition (EMT) contributed to HNSCC. Cox proportional regression model was used to identify a four-gene (WIPF1, PPIB, BASP1, PLOD2) signature that were significantly associated with overall survival (OS), and all the four genes were significantly upregulated in tumor tissues. We successfully classified the patients with HNSCC into high-risk and low-risk groups, where in high-risk indicated poorer patient prognosis, indicating that this gene signature might be a novel potential biomarker for the prognosis of HNSCC. The prognostic ability of the gene signature was further validated in an independent cohort from the Gene Expression Omnibus (GEO) database. In conclusion, we identified a four-EMT-based gene signature which provides the potentiality to serve as novel independent biomarkers for predicting survival in HNSCC patients, as well as a new possibility for individualized treatment of HNSCC.

Identification of Prognosis Associated microRNAs in HNSCC Subtypes Based on TCGA Dataset

Background and Objectives: Head and Neck Squamous Cell Carcinoma (HNSCC) includes cancers from the oral cavity, larynx, and oropharynx and is the sixth-most common cancer worldwide. MicroRNAs are small non-coding RNAs for which altered expression has been demonstrated in pathological processes, such as cancer. The objective of our study was to evaluate the different expression profile in HNSCC subtypes and the prognostic value that one or several miRNAs may have. Materials and Methods: Data from The Cancer Genome Atlas Program-Head and Neck Squamous Cell Carcinoma (TCGA-HNSCC) patients were collected. Differential expression analysis was conducted by edge R-powered TCGAbiolinks R package specific function. Enrichment analysis was developed with Diana Tool miRPath 3.0. Kaplan-Meier survival estimators were used, followed by log-rank tests to compute significance. Results: A total of 127 miRNAs were identified with differential expression level in HNSCC; 48 of them were site-specific and, surprisingly, only miR-383 showed a similar deregulation in all locations studied (tonsil, mouth, floor of mouth, cheek mucosa, lip, tongue, and base of tongue). The most probable affected pathways based on miRNAs interaction levels were protein processing in endoplasmic reticulum, proteoglycans in cancer (p < 0.01), Hippo signaling pathway (p < 0.01), and Transforming growth factor-beta (TGF-beta) signaling pathway (p < 0.01). The survival analysis highlighted 38 differentially expressed miRNAs as prognostic biomarkers. The miRNAs with a greater association between poor prognosis and altered expression (p < 0.001) were miR-137, miR-125b-2, miR-26c, and miR-1304. Conclusions: In this study we have determined miR-137, miR-125b-2, miR-26c, and miR-1304 as novel powerful prognosis biomarkers. Furthermore, we have depicted the miRNAs expression patterns in tumor patients compared with normal subjects using the TCGA-HNSCC cohort.

Potential five-mRNA signature model for the prediction of prognosis in patients with papillary thyroid carcinoma

Although the mortality rate of papillary thyroid carcinoma (PTC) is relatively low, the recurrence rates of PTC remain high. The high recurrence rates are related to the difficulties in treatment. Gene expression profiles has provided novel insights into potential therapeutic targets and molecular biomarkers of PTC. The aim of the present study was to identify mRNA signatures which may categorize PTCs into high-and low-risk subgroups and aid with the predictions for prognoses. The mRNA expression profiles of PTC and normal thyroid tissue samples were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed mRNAs were identified using the ‘EdgeR’ software package. Gene signatures associated with the overall survival of PTC were selected, and enrichment analysis was performed to explore the biological pathways and functions of the prognostic mRNAs using the Database for Visualization, Annotation and Integration Discovery. A signature model was established to investigate a specific and robust risk stratification for PTC. A total of 1,085 differentially expressed mRNAs were identified between the PTC and normal thyroid tissue samples. Among them, 361 mRNAs were associated with overall survival (P<0.05). A 5-mRNA prognostic signature for PTC (ADRA1B, RIPPLY3, PCOLCE, TEKT1 and SALL3) was identified to classify the patients into high-and low-risk subgroups. These prognostic mRNAs were enriched in Gene Ontology terms such as ‘calcium ion binding’, ‘enzyme inhibitor activity’, ‘carbohydrate binding’, ‘transcriptional activator activity’, ‘RNA polymerase II core promoter proximal region sequence-specific binding’ and ‘glutathione transferase activity’, and Kyoto Encyclopedia of Genes and Genomes signaling pathways such as ‘pertussis’, ‘ascorbate and aldarate metabolism’, ‘systemic lupus erythematosus’, ‘drug metabolism-cytochrome P450 and ‘complement and coagulation cascades’. The 5-mRNA signature model may be useful during consultations with patients with PTC to improve the prediction of their prognosis. In addition, the prognostic signature identified in the present study may reveal novel therapeutic targets for patients with PTC.

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.

Glycolysis gene expression profilings screen for prognostic risk signature of hepatocellular carcinoma

Metabolic changes are the markers of cancer and have attracted wide attention in recent years. One of the main metabolic features of tumor cells is the high level of glycolysis, even if there is oxygen. The transformation and preference of metabolic pathways is usually regulated by specific gene expression. The aim of this study is to develop a glycolysis-related risk signature as a biomarker via four common cancer types. Only hepatocellular carcinoma was shown the strong relationship with glycolysis. The mRNA sequencing and chip data of hepatocellular carcinoma, breast invasive carcinoma, renal clear cell carcinoma, colorectal adenocarcinoma were included in the study. Gene set enrichment analysis was performed, profiling three glycolysis-related gene sets, it revealed genes associated with the biological process. Univariate and multivariate Cox proportional regression models were used to screen out prognostic-related gene signature. We identified six mRNAs (DPYSL4, HOMER1, ABCB6, CENPA, CDK1, STMN1) significantly associated with overall survival in the Cox proportional regression model for hepatocellular carcinoma. Based on this gene signature, we were able to divide patients into high-risk and low-risk subgroups. Multivariate Cox regression analysis showed that prognostic power of this six gene signature is independent of clinical variables. Further, we validated this data in our own 55 paired hepatocellular carcinoma and adjacent tissues. The results showed that these proteins were highly expressed in hepatocellular carcinoma tissues compared with adjacent tissue. The survival time of high-risk group was significantly shorter than that of low-risk group, indicating that high-risk group had poor prognosis. We calculated the correlation coefficients between six proteins and found that these six proteins were independent of each other. In conclusions, we developed a glycolysis-related gene signature that could predict survival in hepatocellular carcinoma patients. Our findings provide novel insight to the mechanisms of glycolysis and it is useful for identifying patients with hepatocellular carcinoma with poor prognoses.

Identification of novel cell glycolysis related gene signature predicting survival in patients with endometrial cancer

BACKGROUND

Endometrial cancer (EC) is one of the three major gynecological malignancies. Numerous biomarkers that may be associated with survival and prognosis have been identified through database mining in previous studies. However, the predictive ability of single-gene biomarkers is not sufficiently specific. Genetic signatures may be an improved option for prediction. This study aimed to explore data from The Cancer Genome Atlas (TCGA) to identify a new genetic signature for predicting the prognosis of EC.

METHODS

mRNA expression profiling was performed in a group of patients with EC (n = 548) from TCGA. Gene set enrichment analysis was performed to identify gene sets that were significantly different between EC tissues and normal tissues. Cox proportional hazards regression models were used to identify genes significantly associated with overall survival. Quantitative real-time-PCR was used to verify the reliability of the expression of selected mRNAs. Subsequent multivariate Cox regression analysis was used to establish a prognostic risk parameter formula. Kaplan-Meier survival estimates and the log-rank test were used to validate the significance of risk parameters for prognosis prediction.

RESULT

Nine genes associated with glycolysis (CLDN9, B4GALT1, GMPPB, B4GALT4, AK4, CHST6, PC, GPC1, and SRD5A3) were found to be significantly related to overall survival. The results of mRNA expression analysis by PCR were consistent with those of bioinformatics analysis. Based on the nine-gene signature, the 548 patients with EC were divided into high/low-risk subgroups. The prognostic ability of the nine-gene signature was not affected by other factors.

CONCLUSION

A nine-gene signature associated with cellular glycolysis for predicting the survival of patients with EC was developed. The findings provide insight into the mechanisms of cellular glycolysis and identification of patients with poor prognosis in EC.

Comprehensive Genomic Review of TCGA Head and Neck Squamous Cell Carcinomas (HNSCC)

The aim of this present study was to comprehensively describe somatic DNA alterations and transcriptional alterations in the last extension of the HNSCC subsets in TCGA, encompassing a total of 528 tumours. In order to achieve this goal, transcriptional analysis, functional enrichment assays, survival analysis, somatic copy number alteration analysis and somatic alteration analysis were carried out. A total of 3491 deregulated genes were found in HNSCC patients, and the functional analysis carried out determined that tissue development and cell differentiation were the most relevant signalling pathways in upregulated and downregulated genes, respectively. Somatic copy number alteration analysis showed a “top five” altered HNSCC genes: CDKN2A (deleted in 32.03% of patients), CDKN2B (deleted in 28.34% of patients), PPFIA1 (amplified in 26.02% of patients), FADD (amplified in 25.63% of patients) and ANO1 (amplified in 25.44% of patients). Somatic mutations analysis revealed TP53 mutation in 72% of the tumour samples followed by TTN (39%), FAT1 (23%) and MUC16 (19%). Another interesting result is the mutual exclusivity pattern that was discovered between the TP53 and PIK3CA mutations, and the co-occurrence of CDKN2A with the TP53 and FAT1 alterations. On analysis to relate differential expression genes and somatic copy number alterations, some genes were overexpressed and amplified, for example, FOXL2, but other deleted genes also showed overexpression, such as CDKN2A. Survival analysis revealed that overexpression of some oncogenes, such as EGFR, CDK6 or CDK4 were associated with poorer prognosis tumours. These new findings help us to develop new therapies and programs for the prevention of HNSCC.

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.

A Six-Gene-Based Prognostic Model Predicts Survival in Head and Neck Squamous Cell Carcinoma Patients

Background and Objective

Head and neck cancer is a malignant tumor that begins in the head and neck region, and has the sixth highest incidence worldwide. Previous studies have indicated several prognostic markers for head and neck squamous cell carcinoma (HNSCC), but due to poor accuracy and sensitivity of these clinical characteristic markers attention has been gradually switched to molecular biomarkers. This study aimed to sort out the mRNAs correlated with patient survival time to establish an mRNA combination prognostic biomarker model for HNSCC patient risk stratification, providing optimal therapeutic regimens and improving patient prognosis.

Methods

Clinical data and transcriptome sequencing data of HNSCC were retrieved from TCGA database and were allocated into training and validation datasets. The prognostic model was established using the mRNAs, which were sorted out from training dataset by a significant correlation with survival time. Eventually, the prediction property of the model was evaluated by Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curve.

Results

An optimal prognostic model by the combination of six mRNAs was established. Kaplan-Meier survival analysis revealed effective risk stratification by this model for patients in the two datasets. The area under ROC curve (AUC) was > 0.65 for training and validation datasets, indicating good sensitivity and specificity of this model. Moreover, prominent superiority of this model to investigate prognostic biomarkers was demonstrated.

Conclusion

Our model provided effective prognostication in terms of death risk stratification and evaluation in HNSCC patients. Combination of this prognostic model with current treatment measures is expected to greatly improve the patients' prognosis.

A six-mRNA prognostic model to predict survival in head and neck squamous cell carcinoma

BACKGROUND

Transcriptional dysregulation is one of the most important features of cancer genesis and progression. Applying gene expression dysregulation information to predict the development of cancers is useful for cancer diagnosis. However, previous studies mainly focused on the relationship between a single gene and cancer. Prognostic prediction using combined gene models remains limited.

MATERIALS AND METHODS

Gene expression profiles were downloaded from The Cancer Genome Atlas and the data sets were randomly divided into training data sets and test data sets. A six-gene signature associated with head and neck squamous cell carcinoma (HNSCC) and overall survival (OS) was identified according to a training cohort by using weighted gene correlation network analysis and least absolute shrinkage and selection operator Cox regression. The test data set and gene expression omnibus (GEO) data set were used to validate this signature.

RESULTS

We identified six candidate genes, namely, FOXL2NB, PCOLCE2, SPINK6, ULBP2, KCNJ18, and RFPL1, and, using a six-gene model, predicted the risk of death of head and neck squamous cell carcinoma in The Cancer Genome Atlas. At a selected cutoff, patients were clustered into low- and high-risk groups. The OS curves of the two groups of patients had significant differences, and the time-dependent receiver operating characteristics of OS, disease-specific survival (DSS), and progression-free survival (PFS) were as high as 0.766, 0.731, and 0.623, respectively. Then, the test data set and the GEO data set were used to evaluate our model, and we found that the OS time in the high-risk group was significantly shorter than in the low-risk group in both data sets, and the receiver operating characteristics of test data set were 0.669, 0.675, and 0.614, respectively. Furthermore, univariate and multivariate Cox regression analyses showed that the risk score was independent of clinicopathological features.

CONCLUSION

The six-gene model could predict the OS of HNSCC patients and improve therapeutic decision-making.

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.

Identification of a novel cell cycle-related gene signature predicting survival in patients with gastric cancer

Gastric cancer (GC) is one of the most fatal cancers in the world. Thousands of biomarkers have been explored that might be related to survival and prognosis via database mining. However, the prediction effect of single gene biomarkers is not specific enough. Increasing evidence suggests that gene signatures are emerging as a possible better alternative. We aimed to develop a novel gene signature to improve the prognosis prediction of GC. Using the messenger RNA (mRNA)-mining approach, we performed mRNA expression profiling in a large GC cohort (n = 375) from The Cancer Genome Atlas (TCGA) database. Gene Set Enrichment Analysis (GSEA) was performed, and we recovered genes related to the G2/M checkpoint, which we identified with a Cox proportional regression model. We identified a set of five genes (MARCKS, CCNF, MAPK14, INCENP, and CHAF1A), which were significantly associated with overall survival (OS) in the test series. Based on this five-gene signature, the test series patients could be classified into high-risk or low-risk subgroups. Multivariate Cox regression analysis indicated that the prognostic power of this five-gene signature was independent of clinical features. In conclusion, we developed a five-gene signature related to the cell cycle that can predict survival for GC. Our findings provide novel insight that is useful for understanding cell cycle mechanisms and for identifying patients with GC with poor prognoses.