A prognostic 4-gene expression signature for squamous cell lung carcinoma

Diagnostic Value of GDF10 for the Tumorigenesis and Immune Infiltration in Lung Squamous Cell Carcinoma

OBJECTIVE

Lung squamous cell carcinoma (LUSC) is associated with a low survival rate. Evidence suggests that bone morphogenetic proteins (BMPs) and their receptors (BMPRs) play crucial roles in tumorigenesis and progression. However, a comprehensive analysis of their role in LUSC is lacking. Our study aimed to explore the relationship between BMPs/BMPRs expression levels and the tumorigenesis and prognosis of LUSC.

METHODS

The "R/Limma" package was utilized to analyze the differential expression characteristics of BMPs/BMPRs in LUSC, using data from TCGA, GTEx, and GEO databases. Concurrently, the "survminer" packages were employed to investigate their prognostic value and correlation with clinical features in LUSC. The core gene associated with LUSC progression was further explored through weighted gene correlation network analysis (WGCNA). LASSO analysis was conducted to construct a prognostic risk model for LUSC. Clinical specimens were examined by immunohistochemical analysis to confirm the diagnostic value in LUSC. Furthermore, based on the tumor immune estimation resource database and tumor-immune system interaction database, the role of the core gene in the tumor microenvironment of LUSC was explored.

RESULTS

GDF10 had a significant correlation only with the pathological T stage of LUSC, and the protein expression level of GDF10 decreased with the tumorigenesis of LUSC. A prognostic risk model was constructed with GDF10 as the core gene and 5 hub genes (HRASLS, HIST1H2BH, FLRT3, CHEK2, and ALPL) for LUSC. GDF10 showed a significant positive correlation with immune cell infiltration and immune checkpoint expression.

CONCLUSION

GDF10 might serve as a diagnostic biomarker reflecting the tumorigenesis of LUSC and regulating the tumor immune microenvironment to guide more effective treatment for LUSC.

Intricate confrontation: Research progress and application potential of TRIM family proteins in tumor immune escape

BACKGROUND

Tripartite motif (TRIM) family proteins have more than 80 members and are widely found in various eukaryotic cells. Most TRIM family proteins participate in the ubiquitin-proteasome degradation system as E3-ubiquitin ligases; therefore, they play pivotal regulatory roles in the occurrence and development of tumors, including tumor immune escape. Due to the diversity of functional domains of TRIM family proteins, they can extensively participate in multiple signaling pathways of tumor immune escape through different substrates. In current research and clinical contexts, immune escape has become an urgent problem. The extensive participation of TRIM family proteins in curing tumors or preventing postoperative recurrence and metastasis makes them promising targets.

AIM OF REVIEW

The aim of the review is to make up for the gap in the current research on TRIM family proteins and tumor immune escape and propose future development directions according to the current progress and problems.

KEY SCIENTIFIC CONCEPTS OF REVIEW

This up-to-date review summarizes the characteristics and biological functions of TRIM family proteins, discusses the mechanisms of TRIM family proteins involved in tumor immune escape, and highlights the specific mechanism from the level of structure-function-molecule-pathway-phenotype, including mechanisms at the level of protein domains and functions, at the level of molecules and signaling pathways, and at the level of cells and microenvironments. We also discuss the application potential of TRIM family proteins in tumor immunotherapy, such as possible treatment strategies for combination targeting TRIM family protein drugs and checkpoint inhibitors for improving cancer treatment.

A Genome-Wide Association Study of Small Cell Lung Cancer

INTRODUCTION

Small cell lung cancer (SCLC) comprises 10-15% of all lung cancer cases and is the most aggressive histological type. Survival is poor and the molecular landscape of this disease is extraordinarily complex. The objective of this paper was to perform a Genome-Wide Association Study (GWAS) of this disease using a case-control study specifically designed for small cell lung cancer (SCLC).

METHODS

Incident cases were consecutively recruited from 8 hospitals from different regions of Spain. Controls were recruited from the same hospitals using a frequency sampling based on age and sex distribution of cases. Biological samples were obtained along with detailed information on cases and controls lifestyle, including tobacco and radon exposure.

RESULTS

We included 271 SCLC cases and 557 controls. We found evidence (p-values<10-5) of an association in the complete dataset for several loci, while MAP4 showed a significant association in the gene-based analysis. Pathway analysis suggested that ATR, ATRIP, MCM4, MCM5, ORC4, RPA3 and CDC25A genes have a role on the onset of SCLC.

CONCLUSION

This study provides biological evidence for pathways related to SCLC, offering novel loci for further research.

Development of a tumor microenvironment-related prognostic signature in glioma to predict immune landscape and potential therapeutic drugs

The involvement of the tumor microenvironment (TME) in the biology of gliomas has expanded, while it is yet uncertain its potential of supporting diagnosis and therapy choices. According to immunological characteristics and overall survival, cohorts of glioma patients from public databases were separated into two TME-relevant clusters in this analysis. Based on differentially expressed genes between TME clusters and correlative regression analysis, a 21-gene molecular classifier of TME-related prognostic signature (TPS) was constructed. Afterward, the prognostic efficacy and effectiveness of TPS were assessed in the training and validation groups. The outcome demonstrated that TPS might be utilized alone or in conjunction with other clinical criteria to act as a superior prognostic predictor for glioma. Also, high-risk glioma patients classified by TPS were considered to associate with enhanced immune infiltration, greater tumor mutation, and worse general prognosis. Finally, possible treatment medicines specialized for different risk subgroups of TPS were evaluated in drug databases.

Deciphering a cell death-associated signature for predicting prognosis and response to immunotherapy in lung squamous cell carcinoma

BACKGROUND

Lung squamous cell carcinoma (LUSC) is a subtype of non-small cell carcinoma, accounting for about 30% of all lung cancers. Yet, the evaluation of prognostic outcome and therapy response of patients with LUSC remains to be resolved. This study aimed to explore the prognostic value of cell death pathways and develop a cell death-associated signature for predicting prognosis and guiding treatment in LUSC.

METHODS

Transcriptome profiles and corresponding clinical information of LUSC patients were gathered from The Cancer Genome Atlas (TCGA-LUSC, n = 493) and Gene Expression Omnibus database (GSE74777, n = 107). The cell death-related genes including autophagy (n = 348), apoptosis (n = 163), and necrosis (n = 166) were retrieved from the Kyoto Encyclopedia of Genes and Genomes and Gene Ontology databases. In the training cohort (TCGA-LUSC), LASSO Cox regression was used to construct four prognostic signatures of respective autophagy, apoptosis, and necrosis pathway and genes of three pathways. After comparing the four signatures, the cell death index (CDI), the signature of combined genes, was further validated in the GSE74777 dataset. We also investigated the clinical significance of the CDI signature in predicting the immunotherapeutic response of LUSC patients.

RESULTS

The CDI signature was significantly associated with the overall survival of LUSC patients in the training cohort (HR, 2.13; 95% CI, 1.62‒2.82; P < 0.001) and in the validation cohort (HR, 1.94; 95% CI, 1.01‒3.72; P = 0.04). The differentially expressed genes between the high- and low-risk groups contained cell death-associated cytokines and were enriched in immune-associated pathways. We also found a higher infiltration of naive CD4⁺ T cells, monocytes, activated dendritic cells, neutrophils, and lower infiltration of plasma cells and resting memory CD4⁺ T cells in the high-risk group. Tumor stemness indices, mRNAsi and mDNAsi, were both negatively correlated with the risk score of the CDI. Moreover, LUSC patients in the low-risk group are more likely to respond to immunotherapy than those in the high-risk group (P = 0.002).

CONCLUSIONS

This study revealed a reliable cell death-associated signature (CDI) that closely correlated with prognosis and the tumor microenvironment in LUSC, which may assist in predicting the prognosis and response to immunotherapy for patients with LUSC.

Aiding Cancer's "Sweet Tooth": Role of Hexokinases in Metabolic Reprogramming

Hexokinases (HKs) convert hexose sugars to hexose-6-phosphate, thus trapping them inside cells to meet the synthetic and energetic demands. HKs participate in various standard and altered physiological processes, including cancer, primarily through the reprogramming of cellular metabolism. Four canonical HKs have been identified with different expression patterns across tissues. HKs 1-3 play a role in glucose utilization, whereas HK 4 (glucokinase, GCK) also acts as a glucose sensor. Recently, a novel fifth HK, hexokinase domain containing 1 (HKDC1), has been identified, which plays a role in whole-body glucose utilization and insulin sensitivity. Beyond the metabolic functions, HKDC1 is differentially expressed in many forms of human cancer. This review focuses on the role of HKs, particularly HKDC1, in metabolic reprogramming and cancer progression.

DNA damage repair gene signature model for predicting prognosis and chemotherapy outcomes in lung squamous cell carcinoma

BACKGROUND

Lung squamous cell carcinoma (LUSC) is prone to metastasis and likely to develop resistance to chemotherapeutic drugs. DNA repair has been reported to be involved in the progression and chemoresistance of LUSC. However, the relationship between LUSC patient prognosis and DNA damage repair genes is still unclear.

METHODS

The clinical information of LUSC patients and tumour gene expression level data were downloaded from the TCGA database. Unsupervised clustering and Cox regression were performed to obtain molecular subtypes and prognosis-related significant genes based on a list including 150 DNA damage repair genes downloaded from the GSEA database. The coefficients determined by the multivariate Cox regression analysis and the expression level of prognosis-related DNA damage repair genes were employed to calculate the risk score, which divided LUSC patients into two groups: the high-risk group and the low-risk group. Immune viability, overall survival, and anticarcinogen sensitivity analyses of the two groups of LUSC patients were performed by Kaplan-Meier analysis with the log rank test, ssGSEA and the pRRophetic package in R software. A time-dependent ROC curve was applied to compare the survival prediction ability of the risk score, which was used to construct a survival prediction model by multivariate Cox regression. The prediction model was used to build a nomogram, the discriminative ability of which was confirmed by C-index assessment, and its calibration was validated by calibration curve analysis. Differentially expressed DNA damage repair genes in LUSC patient tissues were retrieved by the Wilcoxon test and validated by qRT-PCR and IHC.

RESULT

LUSC patients were separated into two clusters based on molecular subtypes, of which Cluster 2 was associated with worse overall survival. A prognostic prediction model for LUSC patients was constructed and validated, and a risk score calculated based on the expression levels of ten DNA damage repair genes was employed. The clinical utility was evaluated by drug sensitivity and immune filtration analyses. Thirteen-one genes were upregulated in LUSC patient samples, and we selected the top four genes that were validated by RT-PCR and IHC.

CONCLUSION

We established a novel prognostic model based on DNA damage repair gene expression that can be used to predict therapeutic efficacy in LUSC patients.

Establishment of a prognostic signature for lung adenocarcinoma by integration of 7 pyroptosis-related genes and cross-validation between the TCGA and GEO cohorts: A comprehensive bioinformatics analysis

Pyroptosis-related genes (PRGs) have been reported to be associated with prognosis of lung adenocarcinoma (LUAD). Until now, the relationship of PRGs to the prognosis of LUAD patients and its underlying mechanisms have been poorly elucidated. Using The Cancer Genome Atlas (TCGA) LUAD cohort, a prior bioinformatics analysis constructed a prognostic signature incorporating 5 PRGs (NLRP7, NLRP1, NLRP2, NOD1, and CASP6) for predicting prognosis of LUAD patients. However, it has not been validated by the Gene Expression Omnibus (GEO) LUAD cohort yet. We implemented a modified bioinformatics analysis to, respectively, construct one prognostic signature with the TCGA cohort and with the GEO cohort and attempted to perform cross-validations by the GEO cohort and the TCGA cohort alternately in turn. Univariate and multivariate Cox regression analysis screened PRGs and constructed 2 prognostic signatures with the TCGA and GEO cohorts. All LUAD samples were classified into high- and low-risk groups according to the median risk score that was generated by regression formula. Kaplan-Meier survival analysis compared the overall survival rate between the 2 risk groups, and receiver operating characteristic curve analysis evaluated predictive performance of the 2 signatures. Additionally, risk score, combined with clinicopathological features, was subjected to multivariate Cox regression analysis, to evaluate independent prognostic value of the 2 signatures. Finally, the 2 signatures received cross-validations by the GEO and TCGA cohorts, alternately. The TCGA cohort yielded a 3-gene signature (PYCARD, NLRP1, and NLRC4), whereas the GEO cohort built a 7-gene signature (SCAF11, NOD1, NLRP2, NLRP1, GPX4, CASP8, and AIM2) for predicting the prognosis of LUAD patients. Multivariate analysis proved independent prognostic value of risk score in the TCGA cohort (hazard ratio, = 1.939,; P = 8.43 × 10-4) and the GEO cohort (hazard ratio, = 2.291,; P = 4.34 × 10-9). Cross-validations confirmed prognostic value for the 7-gene signature from the GEO cohort by the TCGA cohort but not for the 3-gene signature from the TCGA cohort by the GEO cohort. We develop and validate a 7-gene prognostic signature (SCAF11, NOD1, NLRP2, NLRP1, GPX4, CASP8, and AIM2) with independent prognostic value for patients with LUAD.

LINC01426 aggravates the malignant progression of glioma through miR-661/Mdm2 axis

BACKGROUND

Long intergenic non-protein coding RNA 1426 (LINC01426) is up-regulated in glioma and functions as a tumor promoter. However, the role of LINC01426 in glioma required further exploration. Therefore, this article mainly studied the role and possible mechanism of LINC01426 in glioma.

METHODS

The area under the receiver operating characteristic curve was used to determine the diagnostic value of LINC01426. The effect of LINC01426 on tumor growth was analyzed by tumorigenesis assay and immunohistochemical analysis. Bioinformatics analysis, dual-luciferase assay, RNA pull-down, Pearson test, and real-time quantitative PCR (RT-qPCR) were applied to verify the relationship between target genes. The expressions and effects of LINC01426, miR-661 and MDM2 proto-oncogene (Mdm2) in glioma were examined by bioinformatics analysis combined with molecular and functional experiments (RT-qRCR, 3-(4,5)-dimethylthiahiazo (-z-y1)-3,5-di-phenytetrazoliumromide, clone formation, BrdU, flow cytometry). The expressions of proliferation and apoptosis-related proteins were determined by Western blot.

RESULTS

LINC01426, which was high-expressed in glioma and was related to poor prognosis, could be used as a diagnostic marker for glioma. SiLINC01426 inhibited the malignant phenotype of glioma cells in vitro and attenuated tumor growth and PCNA expression in vivo, while the effects of LINC01426 were the opposite. LINC01426 targeted and inversely correlated with miR-661, which was low-expressed in glioma. MiR-661 inhibitor evidently overturned the effect of siLINC01426 on biological functions, proliferation, and apoptosis-related proteins of glioma cells. Mdm2 bound to miR-661. Moreover, siMdm2 reversed the effects of miR-661 inhibitor on the biological characteristics and Mdm2/p53/p21 expression of glioma cells.

CONCLUSION

LINC01426 aggravated the malignant progression of glioma through miR-661/Mdm2 axis.

Long Non-coding RNA LINC01426 Contributes to the Malignant Behaviors of NSCLC Via Acting As a Sponge for miR-143-3p

Recently, long non-coding RNA (lncRNA) is proved to play critical roles in non-small cell lung cancer (NSCLC) progression. However, the detailed effects of LINC01426 in NSCLC and its functional mechanism remain unknown. The expression of LINC01426, microRNA-143-3p (miR-143-3p), and Ubiquitin-specific peptidase 28 (USP28) was assessed by quantitative real-time polymerase chain reaction (RT-qPCR). The colony-forming ability was determined by colony-forming assay. 5-ethynyl-2'-deoxyuridine (EdU) staining assay was performed to evaluate cell proliferation. The migrated and invaded abilities of cells were measured by transwell assays. Flow cytometry was used to examine cell apoptosis. The protein expression was analyzed by Western blot analysis. The glycolysis ability was analyzed by commercial kits. Dual-luciferase reporter assay, RNA immunoprecipitation (RIP) assay, and RNA pull-down assay were used to confirm relationship among LINC01426, miR-143-3p, and USP28. A xenograft experiment was conducted to explore the effects of LINC01426 inhibition in vivo. Our results confirmed that LINC01426 and USP28 expression were increased, while miR-143-3p expression was decreased in NSCLC tissues and cells. Further functional experiments demonstrated that LINC01426 inhibition markedly impaired cell proliferation, migration, invasion, autophagy, and glycolysis while induced apoptosis in NSCLC cells, and LINC01426 derived malignant behaviors of NSCLC cells by sponging miR-143-3p. Additionally, LINC01426 regulated USP28 expression by sponging miR-143-3p. USP28 overexpression partly overturned the inhibitory effect of miR-143-3p on NSCLC progression. Consistently, silencing of LINC01426 significantly inhibited the growth of NSCLC tumor in vivo. LINC01426 accelerated the malignant progression of NSCLC. Mechanistically, LINC01426 acted as a competing endogenous RNA (ceRNA) for miR-143-3p to upregulate USP28 expression.

Potential Impact of Cancer Susceptibility Genes on Lung Cancer Metastasis

Background. Studies of prognosis-related molecular markers are an important tool to uncover the mechanism of tumour metastasis. Cancer susceptibility gene testing is an important tool for genetic counselling of cancer risk. However, the impact of lung cancer susceptibility genes (LCSGs) on lung cancer metastasis and prognosis has not been well studied. Methods. The list of lung cancer susceptibility genes was retrospectively analysed and updated. After expression profiling and functional analysis, LCSG-based signatures for prognosis were identified by Cox regression and LASSO regression analyses. For translational purposes, nomograms integrating LCSGs and clinical characteristics were constructed. Results. A total of 301 LCSGs were employed for modelling. For lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), 10-gene and 7-gene signatures were created and independently validated. The LCSG-based risk score could stratify LUAD survival (univariate: hazard ratio $\left(\text{HR}\right)=1.076$ , 95% confidence interval $\left(\text{CI}\right)=1.049$ –1.103, $P<0.001$ ; multivariate: $\text{HR}=1.066$ , 95% $\text{CI}=1.037$ –1.095, $P<0.001$ ) and LUSC survival (univariate: $\text{HR}=1.149$ , 95% $\text{CI}=1.066$ −1.239, $P<0.001$ ; multivariate: $\text{HR}=1.129$ , 95% $\text{CI}=1.038$ −1.228, $P=0.005$ ). One of the processes affected by differentially expressed genes in both LUAD and LUSC was the negative regulation of epithelial cell differentiation. Conclusions. Overall, novel LCSG-based gene signatures for LUAD and LUSC were constructed. These findings could expand the understanding of the impact of LCSG expression on cancer metastasis and prognosis.

Long noncoding RNA LINC01426 promotes the progression of lung adenocarcinoma via regulating miRNA-125a-5p/ casein kinase 2 alpha 1 axis

Although long noncoding RNAs (lncRNAs) in lung adenocarcinoma (LUAD) have been increasingly studied, LINC01426 has not been fully investigated in LUAD. The GEPIA database revealed that LINC01426 was upregulated in LUAD tissues. In our study, we further verified the significantly high expression of LINC01426 in LUAD tissues and cell lines. We also analyzed the LINC01426 expression level and LUAD clinical features and found that high LINC01426 expression was associated with tumor diameter; tumor, node, and metastases (TNM) staging; lymph node metastasis (LNM); and overall survival (OS) rate of LUAD patients. In vitro experiments revealed that suppression of LINC01426 could repress the proliferation, migration and invasion of LUAD cells. Then, the bioinformatic analysis revealed that there were binding domains between miR-125a-5p and the 3′-UTR of LINC01426. As revealed by dual-luciferase reporter gene experiment and RNA Binding Protein Immunoprecipitation (RIP) assay, miR-125a-5p could bind to LINC01426. Additionally, the results of qRT-PCR and Pearson’s analysis respectively revealed that miR-125a-5p was slightly expressed in LUAD and its expression was negatively correlated with LINC01426. Moreover, casein kinase 2 alpha 1 (CSNK2A1) was predicted to bind to miR-125a-5p. CSNK2A1 expression was remarkably high in LUAD tissues, negatively associated with miR-125a-5p, and positively correlated with LINC01426. Subsequently, our results showed that CSNK2A1 enhanced the malignant progression of LUAD cells. Overall, our study revealed that LINC01426 might regulate the malignant phenotype of LUAD via the miR-125a-5p/CSNK2A1 axis.

Hexokinase domain-containing protein-1 in metabolic diseases and beyond

Glucose phosphorylation by hexokinases (HKs) traps glucose in cells and facilitates its usage in metabolic processes dependent on cellular needs. HK domain-containing protein-1 (HKDC1) is a recently discovered protein with wide expression containing HK activity, first noted through a genome-wide association study (GWAS) to be linked with gestational glucose homeostasis during pregnancy. Since then, HKDC1 has been observed to be expressed in many human tissues. Moreover, studies have shown that HKDC1 plays a role in glucose homeostasis by which it may affect the progression of many pathophysiological conditions such as gestational diabetes mellitus (GDM), nonalcoholic steatohepatitis (NASH), and cancer. Here, we review the key studies contributing to our current understanding of the roles of HKDC1 in human pathophysiological conditions and potential therapeutic interventions.

Alterations in the Ca2+ toolkit in oesophageal adenocarcinoma

Aim:

To investigate alterations in transcription of genes, encoding Ca²⁺ toolkit proteins, in oesophageal adenocarcinoma (OAC) and to assess associations between gene expression, tumor grade, nodal-metastatic stage, and patient survival.

Methods:

The expression of 275 transcripts, encoding components of the Ca²⁺ toolkit, was analyzed in two OAC datasets: the Cancer Genome Atlas [via the University of Alabama Cancer (UALCAN) portal] and the oesophageal-cancer, clinical, and molecular stratification [Oesophageal Cancer Clinical and Molecular Stratification (OCCAMS)] dataset. Effects of differential expression of these genes on patient survival were determined using Kaplan-Meier log-rank tests. OAC grade- and metastatic-stage status was investigated for a subset of genes. Adjustment for the multiplicity of testing was made throughout.

Results:

Of the 275 Ca²⁺-toolkit genes analyzed, 75 displayed consistent changes in expression between OAC and normal tissue in both datasets. The channel-encoding genes, N-methyl-D-aspartate receptor 2D (GRIN2D), transient receptor potential (TRP) ion channel classical or canonical 4 (TRPC4), and TRP ion channel melastatin 2 (TRPM2) demonstrated the greatest increase in expression in OAC in both datasets. Nine genes were consistently upregulated in both datasets and were also associated with improved survival outcomes. The 6 top-ranking genes for the weighted significance of altered expression and survival outcomes were selected for further analysis: voltage-gated Ca²⁺ channel subunit α 1D (CACNA1D), voltage-gated Ca²⁺ channel auxiliary subunit α2 δ4 (CACNA2D4), junctophilin 1 (JPH1), acid-sensing ion channel 4 (ACCN4), TRPM5, and secretory pathway Ca²⁺ ATPase 2 (ATP2C2). CACNA1D, JPH1, and ATP2C2 were also upregulated in advanced OAC tumor grades and nodal-metastatic stages in both datasets.

Conclusions:

This study has unveiled alterations of the Ca²⁺ toolkit in OAC, compared to normal tissue. Such Ca²⁺ signalling findings are consistent with those from studies on other cancers. Genes that were consistently upregulated in both datasets might represent useful markers for patient diagnosis. Genes that were consistently upregulated, and which were associated with improved survival, might be useful markers for patient outcome. These survival-associated genes may also represent targets for the development of novel chemotherapeutic agents.

Prognostic Gene Signature for Squamous Cell Carcinoma with a Higher Risk for Treatment Failure and Accelerated MEK-ERK Pathway Activity

Simple Summary

Squamous cell carcinoma (SCC) is the most prevalent type of human cancer worldwide and represents the majority of head and neck tumors. As SCC from aerodigestive or genitourinary tracts share not only common etiology and histological features but also molecular patterns, the major objectives of this study were the establishment of a pan-SCC-related prognostic gene signature by an integrative analysis of multi-omics data and the elucidation of underlying oncogenic pathway activities as potential vulnerabilities for a more efficient and less toxic therapy. Our approach delivers a reliable molecular classifier to identify HNSCC and other SCC patients at higher risk for treatment failure with tumors characterized by a more prominent MAPK activity, who might benefit from a targeted treatment with MEK inhibitors.

Abstract

Squamous cell carcinoma (SCC) is the most prevalent histological type of human cancer, including head and neck squamous cell carcinoma (HNSCC). However, reliable prognostic gene signatures for SCC and underlying genetic and/or epigenetic principles are still unclear. We identified 37 prognostic candidate genes by best cutoff computation based on survival in a pan-SCC cohort (n = 1334) of The Cancer Genome Atlas (TCGA), whose expression stratified not only the pan-SCC cohort but also independent HNSCC validation cohorts into three distinct prognostic subgroups. The most relevant prognostic genes were prioritized by a Least Absolute Shrinkage and Selection Operator Cox regression model and were used to identify subgroups with high or low risks for unfavorable survival. An integrative analysis of multi-omics data identified FN1, SEMA3A, CDH2, FBN1, COL5A1, and ADAM12 as key nodes in a regulatory network related to the prognostic phenotype. An in-silico drug screen predicted two MEK inhibitors (Trametinib and Selumetinib) as effective compounds for high-risk SCC based on the Cancer Cell Line Encyclopedia, which is supported by a higher p-MEK1/2 immunohistochemical staining of high-risk HNSCC. In conclusion, our data identified a molecular classifier for high-risk HNSCC as well as other SCC patients, who might benefit from treatment with MEK inhibitors.

The Corticospinal Excitability Can Be Predicted by Spontaneous Electroencephalography Oscillations

Transcranial magnetic stimulation (TMS) has a wide range of clinical applications, and there is growing interest in neural oscillations and corticospinal excitability determined by TMS. Previous studies have shown that corticospinal excitability is influenced by fluctuations of brain oscillations in the sensorimotor region, but it is unclear whether brain network activity modulates corticospinal excitability. Here, we addressed this question by recording electroencephalography (EEG) and TMS measurements in 32 healthy individuals. The resting motor threshold (RMT) and active motor threshold (AMT) were determined as markers of corticospinal excitability. The least absolute shrinkage and selection operator (LASSO) was used to identify significant EEG metrics and then correlation analysis was performed. The analysis revealed that alpha2 power in the sensorimotor region was inversely correlated with RMT and AMT. Innovatively, graph theory was used to construct a brain network, and the relationship between the brain network and corticospinal excitability was explored. It was found that the global efficiency in the theta band was positively correlated with RMT. Additionally, the global efficiency in the alpha2 band was negatively correlated with RMT and AMT. These findings indicated that corticospinal excitability can be modulated by the power spectrum in sensorimotor regions and the global efficiency of functional networks. EEG network analysis can provide a useful supplement for studying the association between EEG oscillations and corticospinal excitability.

The effect and mechanism of miR-607/CANT1 axis in lung squamous carcinoma

Lung squamous carcinoma (LUSC) is the second most frequent subtype of non-small cell lung cancer. Rarely gene alterations are identified in LUSC. Therefore, identifying LUSC-related genes to explain the relevant molecular mechanism is urgently needed. A potential biomarker, calcium-activated nucleotidase 1 (CANT1), was elevated in tissues of LUSC patients relative to normal cases based on the TCGA and/or GTEx database. CCK-8 and transwell tests were then implemented to measure the proliferative, invasive and migratory capacities, and showed that knockdown of CANT1 blocked LUSC cells proliferation. miR-607, predicted as an upstream factor for CANT1, was declined in LUSC using TargetScan analysis and luciferase activity test. Low miR-607 expression was related with unfavorable outcomes of LUSC patients. Moreover, miR-607 downregulation elevated cell viability, invasion and migration in LUSC cells, which was antagonized by si-CANT1. GEPIA website was accessed to estimate the relevance between CANT1 and epithelial-mesenchymal transition (EMT)-related positive factors. The protein levels of Fibronectin, Vimentin, Snail and β-catenin were altered due to the abnormal CANT1 and miR-607 expression. Together, these data unveiled that miR-607/CANT1 pair may exert a vital role in the progression of LUSC through mediating EMT process, which would furnish an available therapeutic therapy for LUSC.

The Role of Hexokinase Domain Containing Protein-1 in Glucose Regulation During Pregnancy

PURPOSE OF REVIEW

Gestational diabetes mellitus (GDM) is a common pregnancy complication conferring an increased risk to the individual of developing type 2 diabetes. As such, a thorough understanding of the pathophysiology of GDM is warranted. Hexokinase domain containing protein-1 (HKDC1) is a recently discovered protein containing hexokinase activity which has been shown to be associated with glucose metabolism during pregnancy. Here, we discuss recent evidence suggesting roles for the novel HKDC1 in gestational glucose homeostasis and the development of GDM and overt diabetes.

RECENT FINDINGS

Genome-wide association studies identified variants of the HKDC1 gene associated with maternal glucose metabolism. Studies modulating HKDC1 protein expression in pregnant mice demonstrate that HKDC1 has roles in whole-body glucose utilization and nutrient balance, with liver-specific HKDC1 influencing insulin sensitivity, glucose tolerance, gluconeogenesis, and ketone production. HKDC1 has important roles in maintaining maternal glucose homeostasis extending beyond traditional hexokinase functions and may serve as a potential therapeutic target.

Recurrent HBV Integration Targets as Potential Drivers in Hepatocellular Carcinoma

Chronic hepatitis B virus (HBV) infection is the major etiology of hepatocellular carcinoma (HCC), frequently with HBV integrating into the host genome. HBV integration, found in 85% of HBV-associated HCC (HBV–HCC) tissue samples, has been suggested to be oncogenic. Here, we investigated the potential of HBV–HCC driver identification via the characterization of recurrently targeted genes (RTGs). A total of 18,596 HBV integration sites from our in-house study and others were analyzed. RTGs were identified by applying three criteria: at least two HCC subjects, reported by at least two studies, and the number of reporting studies. A total of 396 RTGs were identified. Among the 28 most frequent RTGs, defined as affected in at least 10 HCC patients, 23 (82%) were associated with carcinogenesis and 5 (18%) had no known function. Available breakpoint positions from the three most frequent RTGs, TERT, MLL4/KMT2B, and PLEKHG4B, were analyzed. Mutual exclusivity of TERT promoter mutation and HBV integration into TERT was observed. We present an RTG consensus through comprehensive analysis to enable the potential identification and discovery of HCC drivers for drug development and disease management.

Identification of a glycolysis-related gene signature associated with clinical outcome for patients with lung squamous cell carcinoma

Background

Lung squamous cell carcinoma (LUSC), one of the main types of lung cancer, has caused a huge social burden. There has been no significant progress in its therapy in recent years, Resulting in a poor prognosis. This study aims to develop a glycolysis‐related gene signature to predict patients’ survival with LUSC and explore new therapeutic targets.

Methods

We obtained the mRNA expression and clinical information of 550 patients with LUSC from the Cancer Genome Atlas (TCGA) database. Glycolysis genes were identified by Gene Set Enrichment Analysis (GSEA). The glycolysis‐related gene signature was established using the Cox regression analysis.

Results

We developed five glycolysis‐related genes signature (HKDC1, AGL, ALDH7A1, SLC16A3, and MIOX) to calculate each patient's risk score. According to the risk score, patients were divided into high‐ and low‐risk groups and exhibited significant differences in overall survival (OS) between the two groups. The ROC curves showed that the AUC was 0.707 for the training cohort and 0.651 for the validation cohort. Additionally, the risk score was confirmed as an independent risk factor for LUSC patients by Cox regression analysis.

Conclusion

We built a gene signature to clarify the connection between glycolysis and LUSC. This model performs well in evaluating patients’ survival with LUSC and provides new biomarkers for targeted therapy.

A Glycolysis-Related Five-Gene Signature Predicts Biochemical Recurrence-Free Survival in Patients With Prostate Adenocarcinoma

Prostate cancer (PCa) is one of the most frequently diagnosed cancers in males worldwide. Approximately 25% of all patients experience biochemical recurrence (BCR) after radical prostatectomy (RP) and BCR indicates increased risk for metastasis and castration resistance. PCa patients with highly glycolytic tumors have a worse prognosis. Thus, this study aimed to explore glycolysis-based predictive biomarkers for BCR. Expression data and clinical information of PCa samples were retrieved from three publicly available datasets. One from The Cancer Genome Atlas (TCGA) dataset was used as the training cohort, and two from the Gene Expression Omnibus (GEO) dataset (GSE54460 and GSE70769) were used as validation cohorts. Using the training cohort, univariate Cox regression survival analysis, robust likelihood-based survival model, and stepwise multiply Cox analysis were sequentially applied to explore predictive glycolysis-related candidates. A five-gene risk score was then constructed based on the Cox coefficient as the following: (−0.8367*GYS2) + (0.3448*STMN1) + (0.3595*PPFIA4) + (−0.1940*KDELR3) + (0.4779*ABCB6). Receiver operating characteristic curve (ROC) analysis was used to identify the optimal cut-off point, and patients were divided into low risk and high risk groups. Kaplan–Meier analysis revealed that high risk group had significantly shorter BCR free survival time as compared with that in low risk group in training and validation cohorts. In conclusion, our data support the glycolysis-based five-gene signature as a novel and robust signature for predicting BCR of PCa patients.

Comprehensive Profiling of Genomic and Transcriptomic Differences between Risk Groups of Lung Adenocarcinoma and Lung Squamous Cell Carcinoma

Lung cancer is the second most frequently diagnosed cancer type and responsible for the highest number of cancer deaths worldwide. Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) are subtypes of non-small-cell lung cancer which has the highest frequency of lung cancer cases. We aimed to analyze genomic and transcriptomic variations including simple nucleotide variations (SNVs), copy number variations (CNVs) and differential expressed genes (DEGs) in order to find key genes and pathways for diagnostic and prognostic prediction for lung adenocarcinoma and lung squamous cell carcinoma. We performed a univariate Cox model and then lasso-regularized Cox model with leave-one-out cross-validation using The Cancer Genome Atlas (TCGA) gene expression data in tumor samples. We generated 35- and 33-gene signatures for prognostic risk prediction based on the overall survival time of the patients with LUAD and LUSC, respectively. When we clustered patients into high- and low-risk groups, the survival analysis showed highly significant results with high prediction power for both training and test datasets. Then, we characterized the differences including significant SNVs, CNVs, DEGs, active subnetworks, and the pathways. We described the results for the risk groups and cancer subtypes separately to identify specific genomic alterations between both high-risk groups and cancer subtypes. Both LUAD and LUSC high-risk groups have more downregulated immune pathways and upregulated metabolic pathways. On the other hand, low-risk groups have both up- and downregulated genes on cancer-related pathways. Both LUAD and LUSC have important gene alterations such as CDKN2A and CDKN2B deletions with different frequencies. SOX2 amplification occurs in LUSC and PSMD4 amplification in LUAD. EGFR and KRAS mutations are mutually exclusive in LUAD samples. EGFR, MGA, SMARCA4, ATM, RBM10, and KDM5C genes are mutated only in LUAD but not in LUSC. CDKN2A, PTEN, and HRAS genes are mutated only in LUSC samples. The low-risk groups of both LUAD and LUSC tend to have a higher number of SNVs, CNVs, and DEGs. The signature genes and altered genes have the potential to be used as diagnostic and prognostic biomarkers for personalized oncology.

Upregulation of LINC01426 promotes the progression and stemness in lung adenocarcinoma by enhancing the level of SHH protein to activate the hedgehog pathway

Long noncoding RNAs (lncRNAs) play crucial roles in regulating a variety of biological processes in lung adenocarcinoma (LUAD). In our study, we mainly explored the functional roles of a novel lncRNA long intergenic non-protein coding RNA 1426 (LINC01426) in LUAD. We applied bioinformatics analysis to find the expression of LINC01426 was upregulated in LUAD tissue. Functionally, silencing of LINC01426 obviously suppressed the proliferation, migration, epithelial-mesenchymal transition (EMT), and stemness of LUAD cells. Then, we observed that LINC01426 functioned through the hedgehog pathway in LUAD. The effect of LINC01426 knockdown could be fully reversed by adding hedgehog pathway activator SAG. In addition, we proved that LINC01426 could not affect SHH transcription and its mRNA level. Pull-down sliver staining and RIP assay revealed that LINC01426 could interact with USP22. Ubiquitination assays manifested that LINC01426 and USP22 modulated SHH ubiquitination levels. Rescue assays verified that SHH overexpression rescued the cell growth, migration, and stemness suppressed by LINC01426 silencing. In conclusion, LINC01426 promotes LUAD progression by recruiting USP22 to stabilize SHH protein and thus activate the hedgehog pathway.

A Prognostic 14-Gene Expression Signature for Lung Adenocarcinoma: A Study Based on TCGA Data Mining

Background

Lung adenocarcinoma (LUAD), a major and fatal subtype of lung cancer, caused lots of mortalities and showed different outcomes in prognosis. This study was to assess key genes and to develop a prognostic signature for the patient therapy with LUAD.

Method

RNA expression profile and clinical data from 522 LUAD patients were accessed and downloaded from the Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were extracted and analyzed between normal tissues and LUAD samples. Then, a 14-DEG signature was developed and identified for the survival prediction in LUAD patients by means of univariate and multivariate Cox regression analyses. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed to predict the potential biological functions and pathways of these DEGs.

Results

Twenty-two out of 5924 DEGs in the TCGA dataset were screened and associated with the overall survival (OS) of LUAD patients. 14CID="C008" value=" "DEGs were finally selected and included in our development and validation model by risk score analysis. The ROC analysis indicated that the specificity and sensitivity of this profile signature were high. Further functional enrichment analyses indicated that these DEGs might regulate genes that affect the function of release of sequestered calcium ion into cytosol and pathways that associated with vibrio cholerae infection.

Conclusion

Our study developed a novel 14-DEG signature providing more efficient and persuasive prognostic information beyond conventional clinicopathological factors for survival prediction of LUAD patients.

Identification of a Prognostic Model Based on Immune-Related Genes of Lung Squamous Cell Carcinoma

Immune-related genes (IRGs) play considerable roles in tumor immune microenvironment (IME). This research aimed to discover the differentially expressed immune-related genes (DEIRGs) based on the Cox predictive model to predict survival for lung squamous cell carcinoma (LUSC) through bioinformatics analysis. First of all, the differentially expressed genes (DEGs) were acquired based on The Cancer Genome Atlas (TCGA) using the limma R package, the DEIRGs were obtained from the ImmPort database, whereas the differentially expressed transcription factors (DETFs) were acquired from the Cistrome database. Thereafter, a TFs-mediated IRGs network was constructed to identify the candidate mechanisms for those DEIRGs in LUSC at molecular level. Moreover, Gene Ontology (GO), together with Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, was conducted for exploring those functional enrichments for DEIRGs. Besides, univariate as well as multivariate Cox regression analysis was conducted for establishing a prediction model for DEIRGs biomarkers. In addition, the relationship between the prognostic model and immunocytes was further explored through immunocyte correlation analysis. In total, 3,599 DEGs, 223 DEIRGs, and 46 DETFs were obtained from LUSC tissues and adjacent non-carcinoma tissues. According to multivariate Cox regression analysis, 10 DEIRGs (including CALCB, GCGR, HTR3A, AMH, VGF, SEMA3B, NRTN, ENG, ACVRL1, and NR4A1) were retrieved to establish a prognostic model for LUSC. Immunocyte infiltration analysis showed that dendritic cells and neutrophils were positively correlated with IRGs, which possibly exerted an important part within the IME of LUSC. Our study identifies a prognostic model based on IRGs, which is then used to predict LUSC prognosis and analyze immunocyte infiltration. This may provide a novel insight for exploring the potential IRGs in the IME of LUSC.

Development of an Immune-Related Risk Signature for Predicting Prognosis in Lung Squamous Cell Carcinoma

Lung squamous cell carcinoma (LSCC) is the most common subtype of non-small cell lung cancer. Immunotherapy has become an effective treatment in recent years, while patients showed different responses to the current treatment. It is vital to identify the potential immunogenomic signatures to predict patient' prognosis. The expression profiles of LSCC patients with the clinical information were downloaded from TCGA database. Differentially expressed immune-related genes (IRGs) were extracted using edgeR algorithm, and functional enrichment analysis showed that these IRGs were primarily enriched in inflammatory- and immune-related processes. "Cytokine-cytokine receptor interaction" and "PI3K-AKT signaling pathway" were the most enriched KEGG pathways. 27 differentially expressed IRGs were significantly correlated with the overall survival (OS) of patients using univariate Cox regression analysis. A prognostic risk signature that comprises seven IRGs (GCCR, FGF8, CLEC4M, PTH, SLC10A2, NPPC, and FGF4) was developed with effective predictive performance by multivariable Cox stepwise regression analysis. Most importantly, the signature could be an independent prognostic predictor after adjusting for clinicopathological parameters, and also validated in two independent LSCC cohorts (GSE4573 and GSE17710). Potential molecular mechanisms and tumor immune landscape of these IRGs were investigated through computational biology. Analysis of tumor infiltrating lymphocytes and immune checkpoint molecules revealed distinct immune landscape in high- and low-risk group. The study was the first time to construct IRG-based immune signature in the recognition of disease progression and prognosis of LSCC patients.

Identification of 17 mRNAs and a miRNA as an integrated prognostic signature for lung squamous cell carcinoma

BACKGROUND

Gene signatures for predicting the outcome of lung squamous cell carcinoma (LUSC) have been employed for many years. However, various signatures have been applied in clinical practice. Therefore, in the present study, we aimed to filter out an effective LUSC prognostic gene signature by simultaneously integrating mRNA and microRNA (miRNA).

METHODS

First, based on data from the Cancer Genome Atlas (TCGA) (https://www.cancer.gov/tcga), mRNAs and miRNAs that were related to overall survival of LUSC were obtained by the least absolute shrinkage and selection operator method. Subsequently, the predicting effect was tested by time-dependent receiver operating characteristic curve analysis and Kaplan-Meier survival analysis. Next, related clinical indices were added to evaluate the efficiency of the selected gene signatures. Finally, validation and comparison using three independent gene signatures were performed using data from the Gene Expression Omnibus database (https://www.ncbi.nlm.nih.gov/geo).

RESULTS

Our data showed that the prognostic index (PI) contained 17 mRNAs and one miRNA. According to the best normalized cut-off of PI (0.0247), the hazard ratio of the PI was 3.40 (95% confidence interval = 2.33-4.96). Moreover, when clinical factors were introduced, the PI was still the most significant index. In addition, only two Gene Ontology terms with p < 0.05 were reported. Furthermore, validation implied that, using our 18-gene signature, only hazard ratio = 1.36 (95% confidence interval = 1.01-1.83) was significant compared to the other three groups of gene biomarkers.

CONCLUSIONS

The 18-gene signature selected based on data from the TCGA database had an effective prognostic value for LUSC patients.

Identification of 5 Gene Signatures in Survival Prediction for Patients with Lung Squamous Cell Carcinoma Based on Integrated Multiomics Data Analysis

Background

Lung squamous cell carcinoma (LSCC) is a frequently diagnosed cancer worldwide, and it has a poor prognosis. The current study is aimed at developing the prediction of LSCC prognosis by integrating multiomics data including transcriptome, copy number variation data, and mutation data analysis, so as to predict patients' survival and discover new therapeutic targets.

Methods

RNASeq, SNP, CNV data, and LSCC patients' clinical follow-up information were downloaded from The Cancer Genome Atlas (TCGA), and the samples were randomly divided into two groups, namely, the training set and the validation set. In the training set, the genes related to prognosis and those with different copy numbers or with different SNPs were integrated to extract features using random forests, and finally, robust biomarkers were screened. In addition, a gene-related prognostic model was established and further verified in the test set and GEO validation set.

Results

We obtained a total of 804 prognostic-related genes and 535 copy amplification genes, 621 copy deletions genes, and 388 significantly mutated genes in genomic variants; noticeably, these genomic variant genes were found closely related to tumor development. A total of 51 candidate genes were obtained by integrating genomic variants and prognostic genes, and 5 characteristic genes (HIST1H2BH, SERPIND1, COL22A1, LCE3C, and ADAMTS17) were screened through random forest feature selection; we found that many of those genes had been reported to be related to LSCC progression. Cox regression analysis was performed to establish 5-gene signature that could serve as an independent prognostic factor for LSCC patients and can stratify risk samples in training set, test set, and external validation set (p < 0.01), and the 5-year survival areas under the curve (AUC) of both training set and validation set were > 0.67.

Conclusion

In the current study, 5 gene signatures were constructed as novel prognostic markers to predict the survival of LSCC patients. The present findings provide new diagnostic and prognostic biomarkers and therapeutic targets for LSCC treatment.

Identification of prognostic signature of non-small cell lung cancer based on TCGA methylation data

Non–small lung cancer (NSCLC) is a common malignant disease with very poor outcome. Accurate prediction of prognosis can better guide patient risk stratification and treatment decision making, and could optimize the outcome. Utilizing clinical and methylation/expression data in The Cancer Genome Atlas (TCGA), we conducted comprehensive evaluation of early-stage NSCLC to identify a methylation signature for survival prediction. 349 qualified cases of NSCLC with curative surgery were included and further grouped into the training and validation cohorts. We identified 4000 methylation loci with prognostic influence on univariate and multivariate regression analysis in the training cohort. KEGG pathway analysis was conducted to identify the key pathway. Hierarchical clustering and WGCNA co-expression analysis was performed to classify the sample phenotype and molecular subtypes. Hub 5′-C-phosphate-G-3′ (CpG) loci were identified by network analysis and then further applied for the construction of the prognostic signature. The predictive power of the prognostic model was further validated in the validation cohort. Based on clustering analysis, we identified 6 clinical molecular subtypes, which were associated with different clinical characteristics and overall survival; clusters 4 and 6 demonstrated the best and worst outcomes. We identified 17 hub CpG loci, and their weighted combination was used for the establishment of a prognostic model (RiskScore). The RiskScore significantly correlated with post-surgical outcome; patients with a higher RiskScore have worse overall survival in both the training and validation cohorts (P < 0.01). We developed a novel methylation signature that can reliably predict prognosis for patients with NSCLC.

A nine-long non-coding RNA signature for prognosis prediction of patients with lung squamous cell carcinoma

BACKGROUND

Lung squamous cell carcinoma (LUSC) is malignant disease with poor therapeutic response and unfavourable prognosis.

OBJECTIVE

This study aims to develop a long non-coding RNA (lncRNA) signature for survival prediction in patients with LUSC.

METHODS

We obtained lncRNA expression profiles of 493 LUSC cases from The Cancer Genome Atlas, and randomly divided the samples into a training set (n= 296) and a testing set (n= 197). Univariate Cox regression and random survival forest algorithm were performed to select optimum survival-related lncRNAs.

RESULTS

A lncRNA-focused risk score model was then constructed for prognosis prediction in the training set and further validated in the testing set and the entire set. Finally, bioinformatics analysis was carried out to explore the potential signaling pathways associated with the prognostic lncRNAs. A set of 9 lncRNAs were found to be strongly correlated with overall survival of LUSC patients. These 9 lncRNAs were integrated into a prognostic signature, which could separate patients into high- and low-risk groups with significantly different survival times in the training set (median: 30.5 vs. 80.5 months, log-rank P< 0.001). This signature was also confirmed in the testing set and the entire set. Besides, the prognostic value of the 9-lncRNA signature was independent of clinical features and maintained stable in stratified analyses. Functional enrichment study suggested that the 9 lncRNAs may be mainly involved in metabolism-related pathways, phosphatidylinositol signaling system, p53 signaling pathway, and notch signaling pathway.

CONCLUSIONS

Our study demonstrated the potential clinical implication of the 9-lncRNA signature for survival prediction of LUSC patients.

Identification of lncRNA biomarkers in lung squamous cell carcinoma using comprehensive analysis of lncRNA mediated ceRNA network

Long non-coding RNAs (lncRNAs) act as a member of competing endogenous RNAs (ceRNAs) and plays a significant role in tumorigenesis. The aim of this study was to identify potential lncRNA biomarkers for predicting the prognosis of lung squamous cell carcinoma (LUSC) using a comprehensive analysis of lncRNA mediated ceRNA network. Differentially expressed RNAs datasets were obtained using edge R package in 502 LUSC tissues and 49 adjacent non-LUSC tissues from the Cancer Genome Atlas (TCGA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed to identify functional enrichment implication of lncRNA related differentially expressed mRNAs. Survival analysis was used Kaplan-Meier curve method. Univariate and multivariate Cox regression analysis were performed to construct a predictive model with lncRNA biomarkers. A total of 2185 lncRNAs, 170 miRNAs and 2053 mRNAs were differentially expressed between LUSC tissues and adjacent non-LUSC tissues. The novel constructed ceRNA network incorporated 184 LUSC-specific lncRNAs, 18 miRNAs, and 49 mRNAs. About 11 of 184 differentially expressed lncRNAs and 1 of 18 differentially expressed miRNAs and 5 of 49 differentially expressed mRNAs were conspicuously related to overall survival (p < .05). Univariate and multivariate cox regression analysis showed that 6 lncRNAs were retrieved to construct a predictive model to predict the overall survival in LUSC patients. In conclusion, CeRNAs contributed to the progression of LUSC and a model with 6 lncRNAs might be potential biomarker for predicting the prognosis of LUSC.

Systematic analysis identifies three-lncRNA signature as a potentially prognostic biomarker for lung squamous cell carcinoma using bioinformatics strategy

Background

Lung squamous cell carcinoma (LUSC) is the second most common histological subtype of lung cancer (LC), and the prognoses of most LUSC patients are so far still very poor. The present study aimed at integrating lncRNA, miRNA and mRNA expression data to identify lncRNA signature in competitive endogenous RNA (ceRNA) network as a potentially prognostic biomarker for LUSC patients.

Methods

Gene expression data and clinical characteristics of LUSC patients were retrieved from The Cancer Genome Atlas (TCGA) database, and were integratedly analyzed using bioinformatics methods including Differentially Expressed Gene Analysis (DEGA), Weighted Gene Co-expression Network Analysis (WGCNA), Protein and Protein Interaction (PPI) network analysis and ceRNA network construction. Subsequently, univariate and multivariate Cox regression analyses of differentially expressed lncRNAs (DElncRNAs) in ceRNA network were performed to predict the overall survival (OS) in LUSC patients. Receiver operating characteristic (ROC) analysis was used to evaluate the performance of multivariate Cox regression model. Gene expression profiling interactive analysis (GEPIA) was used to validate key genes.

Results

WGCNA showed that turquoise module including 1,694 DElncRNAs, 2,654 DEmRNAs as well as 113 DEmiRNAs was identified as the most significant modules (cor=0.99, P<1e-200), and differentially expressed RNAs in the module were used to subsequently analyze. PPI network analysis identified FPR2, GNG11 and ADCY4 as critical genes in LUSC, and survival analysis revealed that low mRNA expression of FPR2 and GNG11 resulted in a higher OS rate of LUSC patients. A lncRNA-miRNA-mRNA ceRNA network including 121 DElncRNAs, 18 DEmiRNAs and 3 DEmRNAs was established, and univariate and multivariate Cox regression analysis of those 121 DElncRNAs showed a group of 3 DElncRNAs (TTTY16, POU6F2-AS2 and CACNA2D3-AS1) had significantly prognostic value in OS of LUSC patients. ROC analysis showed that the area under the curve (AUC) of the 3-lncRNA signature associated with 3-year survival was 0.629.

Conclusions

The current study provides novel insights into the lncRNA-related regulatory mechanisms underlying LUSC, and identifying 3-lncRNA signature may serve as a potentially prognostic biomarker in predicting the OS of LUSC patients.

Longitudinal analysis of epigenome-wide DNA methylation reveals novel smoking-related loci in African Americans

Changes in DNA methylation may be a potential mechanism that mediates the effects of smoking on physiological function and subsequent disease risk. Given the dynamic nature of the epigenome, longitudinal studies are indispensable for investigating smoking-induced methylation changes over time. Using blood samples collected approximately five years apart in 380 African Americans (mean age 60.7 years) from the Genetic Epidemiology Network of Arteriopathy (GENOA) study, we measured DNA methylation levels using Illumina HumanMethylation BeadChips. We evaluated the association between Phase 1 smoking status and rate of methylation change, using generalized estimating equation models. Among the 6958 CpG sites examined, smoking status was associated with methylation change for 22 CpGs (false discovery rate q < 0.1), with the majority (91%) becoming less methylated over time. Methylation change was greater in ever smokers than never smokers, and the absolute differences in rates of change ranged from 0.18 to 0.77 per decade in M value, equivalent to a β value change of 0.013 to 0.047 per decade. Significant enrichment was observed for CpG islands, enhancers, and DNAse hypersensitivity sites (p < 0.05). Although biological pathway analyses were not significant, most of the 22 CpGs were within genes known to be associated with cardiovascular disease, cancers, and aging. In conclusion, we identified epigenetic signatures for cigarette smoking that may have been missed in cross-sectional analyses, providing insight into the epigenetic effect of smoking and highlighting the importance of longitudinal analysis in understanding the dynamic human epigenome.

5 protein-based signature for resectable lung squamous cell carcinoma improves the prognostic performance of the TNM staging

INTRODUCTION

Prognostic biomarkers have been very elusive in the lung squamous cell carcinoma (SCC) and none is currently being used in the clinical setting. We aimed to identify and validate the clinical utility of a protein-based prognostic signature to stratify patients with early lung SCC according to their risk of recurrence or death.

METHODS

Patients were staged following the new International Association for the Study of Lung Cancer (IASLC) staging criteria (eighth edition, 2018). Three independent retrospective cohorts of 117, 96 and 105 patients with lung SCC were analysed to develop and validate a prognostic signature based on immunohistochemistry for five proteins.

RESULTS

We identified a five protein-based signature whose prognostic index (PI) was an independent and significant predictor of disease-free survival (DFS) (p<0.001; HR=4.06, 95% CI 2.18 to 7.56) and overall survival (OS) (p=0.004; HR=2.38, 95% CI 1.32 to 4.31). The prognostic capability of PI was confirmed in an external multi-institutional cohort for DFS (p=0.042; HR=2.01, 95% CI 1.03 to 3.94) and for OS (p=0.031; HR=2.29, 95% CI 1.08 to 4.86). Moreover, PI added complementary information to the newly established IASLC TNM 8th edition staging system. A combined prognostic model including both molecular and anatomical (TNM) criteria improved the risk stratification in both cohorts (p<0.05).

CONCLUSION

We have identified and validated a clinically feasible protein-based prognostic model that complements the updated TNM system allowing more accurate risk stratification. This signature may be used as an advantageous tool to improve the clinical management of the patients, allowing the reduction of lung SCC mortality through a more accurate knowledge of the patient's potential outcome.

miR-15a-5p suppresses inflammation and fibrosis of peritoneal mesothelial cells induced by peritoneal dialysis via targeting VEGFA

Long-term peritoneal dialysis (PD) often ends up with ultrafiltration failure (UFF) which is partially caused by persistent inflammation and fibrosis of peritoneal tissues. However, the mechanism is still unclear. In the current study, the peritoneum from UFF patients demonstrated inflammation and fibrosis which were positively related to the expression of vascular endothelial growth factor A (VEGFA). The in vitro model using human peritoneal mesothelial cells (HPMCs) stimulated by high glucose or advanced glycation end (AGE) product showed consistent changes of inflammation, fibrosis, and VEGFA. What's more, we showed that VEGFA was an instigator of inflammation and fibrosis. Several microRNAs (miRNAs) have been reported to regulate expression of VEGFA elsewhere. Five of them were selected to test the expression in the peritoneum of patients with PD. Results suggested that miR-15a-5p was the most significantly downregulated one. Also, in high glucose or AGE product-stimulated HPMCs, miR-15a-5p decreased. When miRNA mimic was used to restore the expression of miR-15a-5p, high glucose-induced VEGFA was repressed. The predicted binding site between these two molecules was confirmed by the dual-luciferase assay. Restoration of miR-15a-5p restrained inflammation and fibrosis of HPMCs. TGF-β1/Smad2 was shown to be the downstream signaling pathway and their activity was regulated by miR-15a-5p/VEGFA. In conclusion, our current study demonstrates that miR-15a-5p acts as a regulator of VEGFA mRNA and the following inflammation and fibrosis in peritoneal mesothelial cells. The miR-15a-5p/VEGFA pathway may be a potential target for preventing ultrafiltration failure in patients with PD.

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

Head and neck squamous cell carcinoma (HNSCC) is a common malignant cancer that accounts for 5-10% of all cancers. This study aimed to identify essential genes associated with the prognosis of HNSCC and construct a powerful prognostic model for the risk assessment of HNSCC. RNAseq expression profile data for the patients with HNSCC were obtained from the TCGA database (GEO). A total of 500 samples with full clinical following-up were randomly divided into a training set and a validation set. The training set was used to screen for differentially expressed lncRNAs. Single-factor survival analysis was performed to obtain lncRNAs that associated with prognosis. A robust likelihood-based survival model was constructed to identify the lncRNAs that are essential for the prognosis of HNSCC. A co-expression network between genes and lncRNAs was also constructed to identify lncRNAs co-expressed with genes to serve as the final signature lncRNAs for prognosis. Finally, the prognostic effect of the signature lncRNAs was tested by multi-factor survival analysis and a scoring model for the prognosis of HNSCC was constructed. Moreover, the results of the validation set and the relative expression levels of the signature lncRNAs in the tumour and the adjacent tissue were consistent with the results of the training set. The 5 lncRNAs were distributed among 3 expression modules. Further KEGG pathway enrichment analysis showed that these 3 co-expressed modules participate in different pathways, and many of these pathways are associated with the development and progression of disease. Therefore, we proposed that the 5 validated lncRNAs can be used to predict the prognosis of HNSCC patients and can be applied in postoperative treatment and follow-up.

Copy Number Profiling of MammaPrint™ Genes Reveals Association with the Prognosis of Breast Cancer Patients

Purpose

The MammaPrint™ gene signature, currently used in clinical practice, provides prognostic information regarding the recurrence and potential metastasis in breast cancer patients. However, the prognostic information of the 70 genes included can only be estimated at the RNA expression level. In this study, we investigated whether copy number information of MammaPrint™ genes at the DNA level can be used as a prognostic tool for breast cancer, as copy number variations (CNVs) are major contributors to cancer progression.

Methods

We performed CNV profiling of MammaPrint™ genes in 59 breast cancer cell lines and 650 breast cancer patients, using publicly available data in The Cancer Genome Atlas (TCGA) database. Statistical analyses including Fisher exact test, chi-square test, and Kaplan-Meier survival analyses were performed.

Results

All MammaPrint™ genes showed recurrent CNVs, particularly in TCGA cohort. CNVs of 32 and 36 genes showed significant associations with progesterone receptor and estrogen rector, respectively. No genes showed a significant association with human epidermal growth factor receptor 2 status and lymph node status. In addition, only six genes were associated with tumor stages. RFC4, HRASLS, NMU, GPR126, SCUBE2, C20orf46, and EBF4 were associated with reduced survival and RASSF7 and ESM1 were associated with reduced disease-free survival.

Conclusion

Based on these findings, a concordance of CNV-based genomic rearrangement with expression profiling of these genes and their putative roles in disease tumorigenesis was established. The results suggested that the CNV profiles of the MammaPrint™ genes can be used to predict the prognosis of breast cancer patients. In addition, this approach may lead to the development of new cancer biomarkers at the DNA level.

Proteomics analysis to reveal biological pathways and predictive proteins in the survival of high-grade serous ovarian cancer

High-grade serous ovarian cancer (HGSC) is an aggressive cancer with a worse clinical outcome. Therefore, studies about the prognosis of HGSC may provide therapeutic avenues to improve patient outcomes. Since genome alteration are manifested at the protein level, we integrated protein and mRNA data of ovarian cancer from The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) and utilized the sparse overlapping group lasso (SOGL) method, a new mechanism-driven variable selection method, to select dysregulated pathways and crucial proteins related to the survival of HGSC. We found that biosynthesis of amino acids was the main biological pathway with the best predictive performance (AUC = 0.900). A panel of three proteins, namely EIF2B1, PRPS1L1 and MAPK13 were selected as potential predictive proteins and the risk score consisting of these three proteins has predictive performance for overall survival (OS) and progression free survival (PFS), with AUC of 0.976 and 0.932, respectively. Our study provides additional information for further mechanism and therapeutic avenues to improve patient outcomes in clinical practice.

Pioglitazone-mediated reversal of elevated glucose metabolism in the airway epithelium of mouse lung adenocarcinomas

Airway epithelial cells are prone to the damage caused by lung cancer risk factors, such as cigarette smoking. Little is known about surrogate biomarkers in the bronchial airway epithelium that can be used to assess the effect of potential chemoprevention drugs on lung adenocarcinoma formation/progression. Pioglitazone has been suggested as a chemoprevention drug for lung cancer. To study the mechanisms underlying the role of pioglitazone in lung cancer prevention, we performed transcriptome sequencing (RNA-Seq) and found that Kras signaling was repressed by pioglitazone treatment in the airway epithelial cells of mice with lung adenocarcinoma (FDR q = 9.8E-04). It was also found that glucose metabolic pathways were elevated in the airway epithelium of mice with lung adenocarcinomas and inhibited by pioglitazone treatment (FDR q = 0.01). Downregulation of glucose metabolism genes was also observed in lung tumors of mice treated with pioglitazone. The high-risk expression signature of elevated glucose metabolism was associated with poor survival outcome in multiple lung adenocarcinoma patient populations (P values ranging from 1.0E-9 to 5.5E-5). Our results suggest that the role of pioglitazone in preventing lung adenocarcinoma may depend on inhibiting Kras signaling and glucose metabolism, which may serve as biomarkers of agent action in the airway epithelium.