Gene expression signature of non-involved lung tissue associated with survival in lung adenocarcinoma patients

TXNDC12 inhibits lipid peroxidation and ferroptosis

Ferroptosis is a type of regulated cell death characterized by lipid peroxidation and subsequent damage to the plasma membrane. Here, we report a ferroptosis resistance mechanism involving the upregulation of TXNDC12, a thioredoxin domain-containing protein located in the endoplasmic reticulum. The inducible expression of TXNDC12 during ferroptosis in leukemia cells is inhibited by the knockdown of the transcription factor ATF4, rather than NFE2L2. Mechanistically, TXNDC12 acts to inhibit lipid peroxidation without affecting iron accumulation during ferroptosis. When TXNDC12 is overexpressed, it restores the sensitivity of ATF4-knockdown cells to ferroptosis. Moreover, TXNDC12 plays a GPX4-independent role in inhibiting lipid peroxidation. The absence of TXNDC12 enhances the tumor-suppressive effects of ferroptosis induction in both cell culture and animal models. Collectively, these findings demonstrate an endoplasmic reticulum-based anti-ferroptosis pathway in cancer cells with potential translational applications.

TXNDC12 knockdown promotes ferroptosis by modulating SLC7A11 expression in glioma

Ferroptosis is an iron-dependent cell death process mainly triggered by reactive oxygen species (ROS) and lipid peroxidation. Thioredoxin domain protein 12 (TXNDC12) promotes the development of some tumors; however, its function in tumor ferroptosis remains unclear. In this study, we found that knockdown of TXNDC12 promoted erastin-induced increase in ROS, lipid peroxidation, and Fe2+ levels, and decreased glutathione content. TXNDC12 is involved in ferroptosis by regulating SLC7A11. Further studies showed that TXNDC12 knockdown promoted an erastin-induced decrease in glioma cell viability. Overall, TXNDC12 played a significant role in ferroptosis by modulating SLC7A11 expression. Thus, TXNDC12 and ferroptosis may provide new targets for the treatment of gliomas.

SNTB1 regulates colorectal cancer cell proliferation and metastasis through YAP1 and the WNT/β-catenin pathway

Colorectal cancer is a common type of digestive tract cancer with a significant morbidity and death rate across the world, partially attributing to the metastasis-associated problems. In this study, integrative bioinformatics analyses were performed to identify genes that might contribute to colorectal cancer metastasis, and 293 genes were dramatically increased and 369 genes were decreased within colon cancer samples. Among up-regulated genes, top five genes correlated with colorectal cancer patient's prognosis were verified for expression in clinical samples and syntrophin beta 1 (SNTB1) was the most up-regulated. In vitro, SNTB1 knockdown suppresses the malignant behaviors of colorectal cancer cells, including cell viability, colony formation capacity, as well as the abilities to migrate and invade. Furthermore, SNTB1 knockdown decreased the levels of Wnt1, C-Jun, C-Myc, TCF7, and cyclin D1, and inhibited EMT in both cell lines. In vivo, SNTB1 knockdown inhibited tumor growth and metastasis in nude mice models. SNTB1 positively regulated Yes1 associated transcriptional regulator (YAP1) expression; YAP1 partially reversed the effects of SNTB1 on colorectal cancer cell phenotypes and the Wnt/β-catenin/MYC signaling. In conclusion, SNTB1 knockdown inhibits colorectal cancer cell aggressiveness in vitro and tumor growth and metastasis in vivo through the Wnt/β-catenin/MYC signaling; YAP1 might mediate SNTB1 functions on colorectal cancer.

FRMD8 targets both CDK4 activation and RB degradation to suppress colon cancer growth

Cyclin-dependent kinase 4 (CDK4) and retinoblastoma protein (RB) are both important cell-cycle regulators that function in different scenarios. Here, we report that FERM domain-containing 8 (FRMD8) inhibits CDK4 activation and stabilizes RB, thereby causing cell-cycle arrest and inhibiting colorectal cancer (CRC) cell growth. FRMD8 interacts separately with CDK7 and CDK4, and it disrupts the interaction of CDK7 with CDK4, subsequently inhibiting CDK4 activation. FRMD8 competes with MDM2 to bind RB and attenuates MDM2-mediated RB degradation. Frmd8 deficiency in mice accelerates azoxymethane/dextran-sodium-sulfate-induced colorectal adenoma formation. The FRMD8 promoter is hypermethylated, and low expression of FRMD8 predicts poor prognosis in CRC patients. Further, we identify an LKCHE-containing FRMD8 peptide that blocks MDM2 binding to RB and stabilizes RB. Combined application of the CDK4 inhibitor and FRMD8 peptide leads to marked suppression of CRC cell growth. Therefore, using an LKCHE-containing peptide to interfere with the MDM2-RB interaction may have therapeutic value in CDK4/6 inhibitor-resistant patients.

Harnessing function of EMT in hepatocellular carcinoma: From biological view to nanotechnological standpoint

Management of cancer metastasis has been associated with remarkable reduction in progression of cancer cells and improving survival rate of patients. Since 90% of mortality are due to cancer metastasis, its suppression can improve ability in cancer fighting. The EMT has been an underlying cause in increasing cancer migration and it is followed by mesenchymal transformation of epithelial cells. HCC is the predominant kind of liver tumor threatening life of many people around the world with poor prognosis. Increasing patient prognosis can be obtained via inhibiting tumor metastasis. HCC metastasis modulation by EMT and HCC therapy by nanoparticles are discussed here. First of all, EMT happens during progression and advanced stages of HCC and therefore, its inhibition can reduce tumor malignancy. Moreover, anti-cancer compounds including all-trans retinoic acid and plumbaging, among others, have been considered as inhibitors of EMT. The EMT association with chemoresistance has been evaluated. Moreover, ZEB1/2, TGF-β, Snail and Twist are EMT modulators in HCC and enhancing cancer invasion. Therefore, EMT mechanism and related molecular mechanisms in HCC are evaluated. The treatment of HCC has not been only emphasized on targeting molecular pathways with pharmacological compounds and since drugs have low bioavailability, their targeted delivery by nanoparticles promotes HCC elimination. Moreover, nanoparticle-mediated phototherapy impairs tumorigenesis in HCC by triggering cell death. Metastasis of HCC and even EMT mechanism can be suppressed by cargo-loaded nanoparticles.

Lung Adenocarcinoma Diagnosed at a Younger Age Is Associated with Advanced Stage, Female Sex, and Ever-Smoker Status, in Patients Treated with Lung Resection

To date, the factors which affect the age at diagnosis of lung adenocarcinoma are not fully understood. In our study, we examined the relationships of age at diagnosis with smoking, pathological stage, sex, and year of diagnosis in a discovery (n = 1694) and validation (n = 1384) series of lung adenocarcinoma patients who had undergone pulmonary resection at hospitals in the Milan area and at Thoraxklinik (Heidelberg), respectively. In the discovery series, younger age at diagnosis was associated with ever-smoker status (OR = 1.5, p = 0.0035) and advanced stage (taking stage I as reference: stage III OR = 1.4, p = 0.0067; stage IV OR = 1.7, p = 0.0080), whereas older age at diagnosis was associated with male sex (OR = 0.57, p < 0.001). Analysis in the validation series confirmed the ever versus never smokers' association (OR = 2.9, p < 0.001), the association with highest stages (stage III versus stage I OR = 1.4, p = 0.0066; stage IV versus stage I OR = 2.0, p = 0.0022), and the male versus female sex association (OR = 0.78, p = 0.032). These data suggest the role of smoking in affecting the natural history of the disease. Moreover, aggressive tumours seem to have shorter latency from initiation to clinical detection. Finally, younger age at diagnosis is associated with the female sex, suggesting that hormonal status of young women confers risk to develop lung adenocarcinoma. Overall, this study provided novel findings on the mechanisms underlying age at diagnosis of lung adenocarcinoma.

Cancer subtyping with heterogeneous multi-omics data via hierarchical multi-kernel learning

Differentiating cancer subtypes is crucial to guide personalized treatment and improve the prognosis for patients. Integrating multi-omics data can offer a comprehensive landscape of cancer biological process and provide promising ways for cancer diagnosis and treatment. Taking the heterogeneity of different omics data types into account, we propose a hierarchical multi-kernel learning (hMKL) approach, a novel cancer molecular subtyping method to identify cancer subtypes by adopting a two-stage kernel learning strategy. In stage 1, we obtain a composite kernel borrowing the cancer integration via multi-kernel learning (CIMLR) idea by optimizing the kernel parameters for individual omics data type. In stage 2, we obtain a final fused kernel through a weighted linear combination of individual kernels learned from stage 1 using an unsupervised multiple kernel learning method. Based on the final fusion kernel, k-means clustering is applied to identify cancer subtypes. Simulation studies show that hMKL outperforms the one-stage CIMLR method when there is data heterogeneity. hMKL can estimate the number of clusters correctly, which is the key challenge in subtyping. Application to two real data sets shows that hMKL identified meaningful subtypes and key cancer-associated biomarkers. The proposed method provides a novel toolkit for heterogeneous multi-omics data integration and cancer subtypes identification.

New Insights into the Identity of the DFNA58 Gene

Hearing loss is the most common sensory deficit, affecting 466 million people worldwide. The vast and diverse genes involved reflect the complexity of auditory physiology, which requires the use of animal models in order to gain a fuller understanding. Among the loci with a yet-to-be validated gene is the DFNA58, in which ~200 Kb genomic duplication, including three protein-coding genes (PLEK, CNRIP1, and PPP3R1's exon1), was found to segregate with autosomal dominant hearing loss. Through whole genome sequencing, the duplication was found to be in tandem and inserted in an intergenic region, without the disruption of the topological domains. Reanalysis of transcriptomes data studies (zebrafish and mouse), and RT-qPCR analysis of adult zebrafish target organs, in order to access their orthologues expression, highlighted promising results with Cnrip1a, corroborated by zebrafish in situ hybridization and immunofluorescence. Mouse data also suggested Cnrip1 as the best candidate for a relevant role in auditory physiology, and its importance in hearing seems to have remained conserved but the cell type exerting its function might have changed, from hair cells to spiral ganglion neurons.

Genetic predisposition to lung adenocarcinoma outcome is a feature already present in patients' noninvolved lung tissue

Emerging evidence suggests that the prognosis of patients with lung adenocarcinoma can be determined from germline variants and transcript levels in nontumoral lung tissue. Gene expression data from noninvolved lung tissue of 483 lung adenocarcinoma patients were tested for correlation with overall survival using multivariable Cox proportional hazard and multivariate machine learning models. For genes whose transcript levels are associated with survival, we used genotype data from 414 patients to identify germline variants acting as cis-expression quantitative trait loci (eQTLs). Associations of eQTL variant genotypes with gene expression and survival were tested. Levels of four transcripts were inversely associated with survival by Cox analysis (CLCF1, hazard ratio [HR] = 1.53; CNTNAP1, HR = 2.17; DUSP14, HR = 1.78; and MT1F: HR = 1.40). Machine learning analysis identified a signature of transcripts associated with lung adenocarcinoma outcome that was largely overlapping with the transcripts identified by Cox analysis, including the three most significant genes (CLCF1, CNTNAP1, and DUSP14). Pathway analysis indicated that the signature is enriched for ECM components. We identified 32 cis-eQTLs for CNTNAP1, including 6 with an inverse correlation and 26 with a direct correlation between the number of minor alleles and transcript levels. Of these, all but one were prognostic: the six with an inverse correlation were associated with better prognosis (HR < 1) while the others were associated with worse prognosis. Our findings provide supportive evidence that genetic predisposition to lung adenocarcinoma outcome is a feature already present in patients' noninvolved lung tissue.

Transcription Factors Leading to High Expression of Neuropeptide L1CAM in Brain Metastases from Lung Adenocarcinoma and Clinical Prognostic Analysis

Background

There is a lack of understanding of the development of metastasis in lung adenocarcinoma (LUAD). This study is aimed at exploring the upstream regulatory transcription factors of L1 cell adhesion molecule (L1CAM) and to construct a prognostic model to predict the risk of brain metastasis in LUAD.

Methods

Differences in gene expression between LUAD and brain metastatic LUAD were analyzed using the Wilcoxon rank-sum test. The GRNdb (http://www.grndb.com) was used to reveal the upstream regulatory transcription factors of L1CAM in LUAD. Single-cell expression profile data (GSE131907) were obtained from the transcriptome data of 10 metastatic brain tissue samples. LUAD prognostic nomogram prediction models were constructed based on the identified significant transcription factors and L1CAM.

Results

Survival analysis suggested that high L1CAM expression was negatively significantly associated with overall survival, disease-specific survival, and prognosis in the progression-free interval (p < 0.05). The box plot indicates that high expression of L1CAM was associated with distant metastases in LUAD, while ROC curves suggested that high expression of L1CAM was associated with poor prognosis. FOSL2, HOXA9, IRF4, IKZF1, STAT1, FLI1, ETS1, E2F7, and ADARB1 are potential upstream transcriptional regulators of L1CAM. Single-cell data analysis revealed that the expression of L1CAM was found significantly and positively correlated with the expression of ETS1, FOSL2, and STAT1 in brain metastases. L1CAM, ETS1, FOSL2, and STAT1 were used to construct the LUAD prognostic nomogram prediction model, and the ROC curves suggest that the constructed nomogram possesses good predictive power.

Conclusion

By bioinformatics methods, ETS1, FOSL2, and STAT1 were identified as potential transcriptional regulators of L1CAM in this study. This will help to facilitate the early identification of patients at high risk of metastasis.

Upregulation of SNTB1 correlates with poor prognosis and promotes cell growth by negative regulating PKN2 in colorectal cancer

Background

Colorectal cancer (CRC) is one of the most highly malignant tumors and has a complicated pathogenesis. A preliminary study identified syntrophin beta 1 (SNTB1) as a potential oncogene in CRC. However, the clinical significance, biological function, and underlying mechanisms of SNTB1 in CRC remain largely unknown. Thus, the present study aimed to investigate the role of SNTB1 in CRC.

Methods

The expression profile of SNTB1 in CRC samples was evaluated by database analysis, cDNA array, tissue microarray, quantitative real-time PCR (qPCR), and immunohistochemistry. SNTB1 expression in human CRC cells was silenced using short hairpin RNAs (shRNA)/small interfering RNAs (siRNA) and its mRNA and protein levels were assessed by qPCR and/or western blotting. Cell viability, survival, cell cycle, and apoptosis were determined by the CCK-8 assay, colony formation, and flow cytometry assays, respectively. A xenograft nude mouse model of CRC was established to validate the roles of SNTB1 in vivo. Immunohistochemistry and TUNEL staining were used to determine the expression of SNTB1, PCNA, and cell apoptosis in tissue samples. Isobaric tag for relative and absolute quantification (iTRAQ) was used to analyze the differentially expressed proteins after knockdown of SNTB1 in CRC cells. Silence of protein kinase N2 (PKN2) using si-PNK2 was performed for rescue experiments.

Results

SNTB1 expression was increased in CRC tissues compared with adjacent noncancerous tissues and the increased SNTB1 expression was associated with shorter overall survival of CRC patients. Silencing of SNTB1 suppressed cell viability and survival, induced cell cycle arrest and apoptosis in vitro, and inhibited the growth of CRC cells in vivo. Further elucidation of the regulation of STNB1 on CRC growth by iTRAQ analysis identified 210 up-regulated and 55 down-regulated proteins in CRC cells after SNTB knockdown. A PPI network analysis identified PKN2 as a hub protein and was up-regulated in CRC cells after SNTB1 knockdown. Western-blot analysis further confirmed that SNTB1 knockdown significantly up-regulated PKN2 protein expression in CRC cells and decreased the phosphorylation of both ERK1/2 and AKT. Moreover, rescue experiments indicated that PKN2 knockdown significantly rescued SNTB1 knockdown-mediated decrease in cell viability, survival, and increase of cell cycle arrest at G0/G1 phase and apoptosis of CRC cells.

Conclusions

These findings indicate that SNTB1 is overexpressed in CRC. Elevated SNTB1 levels are correlated with shorter patient survival. Importantly, SNTB1 promotes tumor growth and progression of CRC, possibly by reducing the expression of PKN2 and activating the ERK and AKT signaling pathway. Our study highlights the potential of SNTB1 as a new prognostic factor and therapeutic target for CRC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02246-7.

A rare genomic duplication in 2p14 underlies autosomal dominant hearing loss DFNA58

Here we define a ~200 Kb genomic duplication in 2p14 as the genetic signature that segregates with postlingual progressive sensorineural autosomal dominant hearing loss (HL) in 20 affected individuals from the DFNA58 family, first reported in 2009. The duplication includes two entire genes, PLEK and CNRIP1, and the first exon of PPP3R1 (protein coding), in addition to four uncharacterized long non-coding (lnc) RNA genes and part of a novel protein-coding gene. Quantitative analysis of mRNA expression in blood samples revealed selective overexpression of CNRIP1 and of two lncRNA genes (LOC107985892 and LOC102724389) in all affected members tested, but not in unaffected ones. Qualitative analysis of mRNA expression identified also fusion transcripts involving parts of PPP3R1, CNRIP1 and an intergenic region between PLEK and CNRIP1, in the blood of all carriers of the duplication, but were heterogeneous in nature. By in situ hybridization and immunofluorescence, we showed that Cnrip1, Plek and Ppp3r1 genes are all expressed in the adult mouse cochlea including the spiral ganglion neurons, suggesting changes in expression levels of these genes in the hearing organ could underlie the DFNA58 form of deafness. Our study highlights the value of studying rare genomic events leading to HL, such as copy number variations. Further studies will be required to determine which of these genes, either coding proteins or non-coding RNAs, is or are responsible for DFNA58 HL.

Read-through transcripts in lung: germline genetic regulation and correlation with the expression of other genes

Transcripts originating from the transcriptional read through of two adjacent, similarly oriented genes have been identified in normal and neoplastic tissues, but their functional role and the mechanisms that regulate their expression are mostly unknown. Here, we investigated whether the expression of read-through transcripts previously identified in the non-involved lung tissue of lung adenocarcinoma patients was genetically regulated. Data on genome-wide single nucleotide variant genotypes and expression levels of 10 read-through transcripts in 201 samples of lung tissue were combined to identify expression quantitative trait loci (eQTLs). Then, to identify genes whose expression levels correlated with the 10 read-through transcripts, we used whole transcriptome profiles available for 154 patients. For 8 read-though transcripts, we identified 60 eQTLs (false discovery rate <0.05), including 17 cis-eQTLs and 43 trans-eQTLs. These eQTLs did not maintain their behavior on the 'parental' genes involved in the read-through transcriptional event. The expression levels of 7 read-through transcripts were found to correlate with the expression of other genes: CHIA-PIFO and CTSC-RAB38 correlated with CHIA and RAB38, respectively, while 5 other read-through transcripts correlated with 43 unique non-parental transcripts; thus offering indications about the molecular processes in which these chimeric transcripts may be involved. We confirmed 9 eQTLs (for 4 transcripts) in the non-involved lung tissue from an independent series of 188 lung adenocarcinoma patients. Therefore, this study indicates that the expression of four read-through transcripts in normal lung tissue is under germline genetic regulation, and that this regulation is independent of that of the genes involved in the read-through event.

Cannabinoid Receptor Interacting Protein 1a (CRIP1a) in Health and Disease

Endocannabinoid signaling depends upon the CB₁ and CB₂ cannabinoid receptors, their endogenous ligands anandamide and 2-arachidonoylglycerol, and intracellular proteins that mediate responses via the C-terminal and other intracellular receptor domains. The CB₁ receptor regulates and is regulated by associated G proteins predominantly of the Gi/o subtypes, β-arrestins 1 and 2, and the cannabinoid receptor-interacting protein 1a (CRIP1a). Evidence for a physiological role for CRIP1a is emerging as data regarding the cellular localization and function of CRIP1a are generated. Here we summarize the neuronal distribution and role of CRIP1a in endocannabinoid signaling, as well as discuss investigations linking CRIP1a to development, vision and hearing sensory systems, hippocampus and seizure regulation, and psychiatric disorders including schizophrenia. We also examine the genetic and epigenetic association of CRIP1a within a variety of cancer subtypes. This review provides evidence upon which to base future investigations on the function of CRIP1a in health and disease.

The molecular structure and function of sorting nexin 10 in skeletal disorders, cancers, and other pathological conditions

SNX10 is a member of the phox homology domain-containing family of phosphoinositide-binding proteins. Intracellularly, SNX10 localizes to endosomes where it mediates intracellular trafficking, endosome organization, and protein localization to the centrosome and cilium. It is highly expressed in bone and the gut where it participates in bone mineral and calcium homeostasis through the regulation of osteoclastic bone resorption and gastric acid secretion, respectively. Not surprisingly, patients harboring mutations in SNX10 mutation manifest a phenotype of autosomal recessive osteopetrosis or malignant infantile osteopetrosis, which is clinically characterized by dense bones with increased cortical bone into the medullary space with bone marrow occlusion or depletion, bone marrow failure, and anemia. Accordingly, SNX10 mutant osteoclasts exhibit impaired bone resorptive capacity. Beyond the skeleton, there is emerging evidence implicating SNX10 in cancer development, metabolic disorders, inflammation, and chaperone-mediated autophagy. Understanding the structural basis through which SNX10 exerts its diverse biological functions in both cell and tissue-specific manners may therefore inform new therapeutic opportunities toward the treatment and management of SNX10-related diseases.

Identification of a Sixteen-gene Prognostic Biomarker for Lung Adenocarcinoma Using a Machine Learning Method

Objectives: Lung adenocarcinoma (LUAD) accounts for a majority of cancer-related deaths worldwide annually. The identification of prognostic biomarkers and prediction of prognosis for LUAD patients is necessary. Materials and Methods: In this study, LUAD RNA-Seq data and clinical data from the Cancer Genome Atlas (TCGA) were divided into TCGA cohort I (n = 338) and II (n = 168). The cohort I was used for model construction, and the cohort II and data from Gene Expression Omnibus (GSE72094 cohort, n = 393; GSE11969 cohort, n = 149) were utilized for validation. First, the survival-related seed genes were selected from the cohort I using the machine learning model (random survival forest, RSF), and then in order to improve prediction accuracy, the forward selection model was utilized to identify the prognosis-related key genes among the seed genes using the clinically-integrated RNA-Seq data. Second, the survival risk score system was constructed by using these key genes in the cohort II, the GSE72094 cohort and the GSE11969 cohort, and the evaluation metrics such as HR, p value and C-index were calculated to validate the proposed method. Third, the developed approach was compared with the previous five prediction models. Finally, bioinformatics analyses (pathway, heatmap, protein-gene interaction network) have been applied to the identified seed genes and key genes. Results and Conclusion: Based on the RSF model and clinically-integrated RNA-Seq data, we identified sixteen key genes that formed the prognostic gene expression signature. These sixteen key genes could achieve a strong power for prognostic prediction of LUAD patients in cohort II (HR = 3.80, p = 1.63e-06, C-index = 0.656), and were further validated in the GSE72094 cohort (HR = 4.12, p = 1.34e-10, C-index = 0.672) and GSE11969 cohort (HR = 3.87, p = 6.81e-07, C-index = 0.670). The experimental results of three independent validation cohorts showed that compared with the traditional Cox model and the use of standalone RNA-Seq data, the machine-learning-based method effectively improved the prediction accuracy of LUAD prognosis, and the derived model was also superior to the other five existing prediction models. KEGG pathway analysis found eleven of the sixteen genes were associated with Nicotine addiction. Thirteen of the sixteen genes were reported for the first time as the LUAD prognosis-related key genes. In conclusion, we developed a sixteen-gene prognostic marker for LUAD, which may provide a powerful prognostic tool for precision oncology.

TXNDC12 promotes EMT and metastasis of hepatocellular carcinoma cells via activation of β-catenin

Metastasis is one of the main contributors to the poor prognosis of hepatocellular carcinoma (HCC). However, the underlying mechanism of HCC metastasis remains largely unknown. Here, we showed that TXNDC12, a thioredoxin-like protein, was upregulated in highly metastatic HCC cell lines as well as in portal vein tumor thrombus and lung metastasis tissues of HCC patients. We found that the enforced expression of TXNDC12 promoted metastasis both in vitro and in vivo. Subsequent mechanistic investigations revealed that TXNDC12 promoted metastasis through upregulation of the ZEB1-mediated epithelial-mesenchymal transition (EMT) process. We subsequently showed that TXNDC12 overexpression stimulated the nuclear translocation and activation of β-catenin, a positive transcriptional regulator of ZEB1. Accordingly, we found that TXNDC12 interacted with β-catenin and that the thioredoxin-like domain of TXNDC12 was essential for the interaction between TXNDC12 and β-catenin as well as for TXNDC12-mediated β-catenin activation. Moreover, high levels of TXNDC12 in clinical HCC tissues correlated with elevated nuclear β-catenin levels and predicted worse overall and disease-free survival. In summary, our study demonstrated that TXNDC12 could activate β-catenin via protein-protein interaction and promote ZEB1-mediated EMT and HCC metastasis.

Cigarette smoke alters the transcriptome of non-involved lung tissue in lung adenocarcinoma patients

Alterations in the gene expression of organs in contact with the environment may signal exposure to toxins. To identify genes in lung tissue whose expression levels are altered by cigarette smoking, we compared the transcriptomes of lung tissue between 118 ever smokers and 58 never smokers. In all cases, the tissue studied was non-involved lung tissue obtained at lobectomy from patients with lung adenocarcinoma. Of the 17,097 genes analyzed, 357 were differentially expressed between ever smokers and never smokers (FDR < 0.05), including 290 genes that were up-regulated and 67 down-regulated in ever smokers. For 85 genes, the absolute value of the fold change was ≥2. The gene with the smallest FDR was MYO1A (FDR = 6.9 × 10⁻⁴) while the gene with the largest difference between groups was FGG (fold change = 31.60). Overall, 100 of the genes identified in this study (38.6%) had previously been found to associate with smoking in at least one of four previously reported datasets of non-involved lung tissue. Seven genes (KMO, CD1A, SPINK5, TREM2, CYBB, DNASE2B, FGG) were differentially expressed between ever and never smokers in all five datasets, with concordant higher expression in ever smokers. Smoking-induced up-regulation of six of these genes was also observed in a transcription dataset from lung tissue of non-cancer patients. Among the three most significant gene networks, two are involved in immunity and inflammation and one in cell death. Overall, this study shows that the lung parenchyma transcriptome of smokers has altered gene expression and that these alterations are reproducible in different series of smokers across countries. Moreover, this study identified a seven-gene panel that reflects lung tissue exposure to cigarette smoke.

Association of dietary folate and vitamin B-12 intake with genome-wide DNA methylation in blood: a large-scale epigenome-wide association analysis in 5841 individuals

BACKGROUND

Folate and vitamin B-12 are essential micronutrients involved in the donation of methyl groups in cellular metabolism. However, associations between intake of these nutrients and genome-wide DNA methylation levels have not been studied comprehensively in humans.

OBJECTIVE

The aim of this study was to assess whether folate and/or vitamin B-12 intake are asssociated with genome-wide changes in DNA methylation in leukocytes.

METHODS

A large-scale epigenome-wide association study of folate and vitamin B-12 intake was performed on DNA from 5841 participants from 10 cohorts using Illumina 450k arrays. Folate and vitamin B-12 intakes were calculated from food-frequency questionnaires (FFQs). Continuous and categorical (low compared with high intake) linear regression mixed models were applied per cohort, controlling for confounders. A meta-analysis was performed to identify significant differentially methylated positions (DMPs) and regions (DMRs), and a pathway analysis was performed on the DMR annotated genes.

RESULTS

The categorical model resulted in 6 DMPs, which are all negatively associated with folate intake, annotated to FAM64A, WRAP73, FRMD8, CUX1, and LCN8 genes, which have a role in cellular processes including centrosome localization, cell proliferation, and tumorigenesis. Regional analysis showed 74 folate-associated DMRs, of which 73 were negatively associated with folate intake. The most significant folate-associated DMR was a 400-base pair (bp) spanning region annotated to the LGALS3BP gene. In the categorical model, vitamin B-12 intake was associated with 29 DMRs annotated to 48 genes, of which the most significant was a 1100-bp spanning region annotated to the calcium-binding tyrosine phosphorylation-regulated gene (CABYR). Vitamin B-12 intake was not associated with DMPs.

CONCLUSIONS

We identified novel epigenetic loci that are associated with folate and vitamin B-12 intake. Interestingly, we found a negative association between folate and DNA methylation. Replication of these methylation loci is necessary in future studies.

Computational identification of miRNA and their cross kingdom targets from expressed sequence tags of Ocimum basilicum

MicroRNAs (miRNAs) are conserved small non coding RNAs, which are typically 22-24 nucleotides long and play an important role in post transcription regulation andin various biological processes in both animals and plants. Ocimum basilicum is an important medicinal plant having different bioactive compounds eugenol and essential oils that possess numerous therapeutic properties. However, only a few miRNAs of Ocimum basilicum and its function have been studied till date. The present study focusses on the identification of miRNA from expressed sequenced tags by carrying out computational approaches based on the homology search method. A total of 10 potential miRNAs with 8 different families were predicted in O.basilicum. Furthermore, the psRNA target server was used to predict cross kingdom target genes on human transcriptome for identification ofpotential miRNAs. Eight miRNA families were found to modulate the 87 human target genes which were associated with RAS/MAPK signalling cascade, cardiomyopathy, HIV, breast cancer, lung cancer, Alzheimer's diseases and several neurological disorders. Moreover, O.basilicum miRNAs regulate the key human target genes having significance in various diseases and important biological networks with 10 hub nodes interactions. Thus this study gives the pave for further studies to explore the potential of miRNA mediated cross kingdom regulation and treatment of various diseases including cancer.

Screening of important lncRNAs associated with the prognosis of lung adenocarcinoma, based on integrated bioinformatics analysis

The study aimed to elucidate the mechanisms underlying the occurrence and development of lung adenocarcinoma, and to reveal long non-coding RNA (lncRNA) prognostic factors to identify patients at high risk of disease recurrence or metastasis. Based on extensive RNA sequencing data and clinical survival prognosis information from patients with lung adenocarcinoma, obtained from The Cancer Genome Atlas and the Gene Expression Omnibus databases, a co-expression network of lncRNAs with different expression levels was built using weighted correlation network analysis and MetaDE.ES. The prognostic lncRNAs were identified using the Cox proportional hazards model and Kaplan-Meier survival curves to construct a risk scoring system. The reliability of the system was confirmed in validation datasets. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed on the genes significantly associated with the prognostic lncRNAs using gene set enrichment analysis. A total of 58 and 1,633 differentially expressed lncRNAs and mRNAs were identified, respectively. Considering the module stability, annotation, correlation between modules and clinical factors, and the differential expression levels of lncRNAs, 32 differentially expressed lncRNAs were selected from the brown, red, blue, green and yellow modules for subsequent survival analysis. A signature-based risk scoring system involving five lncRNAs [DIAPH2 antisense RNA 1, FOXN3 antisense RNA 2, long intergenic non-protein coding RNA 652, maternally expressed 3 and RHPN1 antisense RNA 1 (head to head)] was developed. The system successfully distinguished between low- and high-risk prognostic samples. System effectiveness was further verified using two independent validation datasets. Further KEGG pathway analysis indicated that the target genes of the five prognostic lncRNAs were associated with a number of cellular processes and signaling pathways, including the cell receptor-mediated signaling and cell adhesion pathways. A five-lncRNA signature predicts the prognosis of patients with lung adenocarcinoma. These prognostic lncRNAs may be potential diagnostic markers. The present results may help elucidate the pathogenesis of lung adenocarcinoma.

Identification of target gene and prognostic evaluation for lung adenocarcinoma using gene expression meta-analysis, network analysis and neural network algorithms

BACKGROUND

Lung adenocarcinoma (LUAD) is a heterogeneous disease with poor survival in the advanced stage and a high incidence rate in the world. Novel drug targets are urgently required to improve patient treatment. Therefore, we aimed to identify therapeutic targets for LUAD based on protein-protein and protein-drug interaction network analysis with neural network algorithms using mRNA expression profiles.

RESULTS

A comprehensive meta-analysis of selective non-small cell lung cancer (NSCLC) mRNA expression profile datasets from Gene Expression Omnibus were used to identify potential biomarkers and the molecular mechanisms related to the prognosis of NSCLC patients. Using the Network Analyst tool, based on combined effect size (ES) methods, we recognized 6566 differentially expressed genes (DEGs), which included 3036 downregulated and 3530 upregulated genes linked to NSCLC patient survival. ClueGO, a Cytoscape plugin, was exploited to complete the function and pathway enrichment analysis, which disclosed "regulated exocytosis", "purine nucleotide binding", "pathways in cancer", and "cell cycle" between exceptionally supplemented terms. Enrichr, a web tool examination, demonstrated "early growth response protein 1 (EGR-1)", "hepatocyte nuclear factor 4α (HNF4A)", "mitogen-activated protein kinase 14 (MAP3K14)", and "cyclin-dependent kinase 1 (CDK1)" to be among the most prevalent TFs and kinases associated with NSCLC. Our meta-analysis identified that MAPK1 and aurora kinase (AURKA) are the most obvious class of hub nodes. Furthermore, protein-drug interaction network and neural network algorithms identified candidate drugs such as phosphothreonine and 4-(4-methylpiperazin-1-yl)-n-[5-(2-thienylacetyl)-1,5-dihydropyrrolo[3,4-c]pyrazol-3-yl] benzamide and for the targets MAPK1 and AURKA, respectively.

CONCLUSION

Our study has identified novel candidate biomarkers, pathways, transcription factors (TFs), and kinases associated with NSCLC prognosis, as well as drug candidates, which may assist treatment strategy for NSCLC patients.

Molecular gene signature and prognosis of non-small cell lung cancer

The current staging system for non–small cell lung cancer (NSCLC) is inadequate for predicting outcome. Risk score, a linear combination of the values for the expression of each gene multiplied by a weighting value which was estimated from univariate Cox proportional hazard regression, can be useful. The aim of this study is to analyze survival-related genes with TaqMan Low-Density Array (TLDA) and risk score to explore gene-signature in lung cancer. A total of 96 NSCLC specimens were collected and randomly assigned to a training (n = 48) or a testing cohort (n = 48). A panel of 219 survival-associated genes from published studies were used to develop a 6-gene risk score. The risk score was used to classify patients into high or low-risk signature and survival analysis was performed. Cox models were used to evaluate independent prognostic factors. A 6-gene signature including ABCC4, ADRBK2, KLHL23, PDS5A, UHRF1 and ZNF551 was identified. The risk score in both training (HR = 3.14, 95% CI: 1.14–8.67, p = 0.03) and testing cohorts (HR = 5.42, 95% CI: 1.56–18.84, p = 0.01) was the independent prognostic factor. In merged public datasets including GSE50081, GSE30219, GSE31210, GSE19188, GSE37745, GSE3141 and GSE31908, the risk score (HR = 1.50, 95% CI: 1.25–1.80, p < 0.0001) was also the independent prognostic factor. The risk score generated from expression of a small number of genes did perform well in predicting overall survival and may be useful in routine clinical practice.

LncRNA Expression Signature in Prediction of the Prognosis of Lung Adenocarcinoma

OBJECTIVE

To determine if lncRNA expression can be used for the prognoses of patients diagnosed with lung adenocarcinoma (LUAD) patients.

METHODS

The Cancer Genome Atlas database was used to identify 409 LUAD patients for whom there were both, gene expression data and relevant clinical information available. LncRNAs were then selected from the expression data through record linkage between the National Center for Biotechnology Information (NCBI) and Ensemble databases. lncRNAs with significantly different expression levels between normal and tumor tissues were screened, and those whose levels correlated with a positive LUAD prognosis were identified. Based on the expression of the selected lncRNAs, an unsupervised learning method was used to cluster these patients into two groups, and survival analyses were performed to assess the overall survival (OS) between the two groups. Receiver operating characteristic curves were used to calculate the specificity and sensitivity of the models based on the presence of these lncRNAs, and the model was tested with another dataset from the Gene Expression Omnibus.

RESULTS

A total of 151 lncRNAs were found to be differentially expressed between tumor and normal tissues (permutation p-values <0.05) based on the Cancer Genome Atlas dataset, and 20 lncRNAs were associated with OS. Two lncRNAs (DKFZP434 L187 and LOC285548) were correlated with LUAD. All patients with high expression of these two lncRNAs from the two datasets exhibited poor OS compared with those with low expression (p < 0.05).

CONCLUSIONS

The expression of DKFZP434 L187 and LOC285548 may have prognostic value for the OS of LUAD patients.

Read-through transcripts in normal human lung parenchyma are down-regulated in lung adenocarcinoma

Read-through transcripts result from the continuous transcription of adjacent, similarly oriented genes, with the splicing out of the intergenic region. They have been found in several neoplastic and normal tissues, but their pathophysiological significance is unclear. We used high-throughput sequencing of cDNA fragments (RNA-Seq) to identify read-through transcripts in the non-involved lung tissue of 64 surgically treated lung adenocarcinoma patients. A total of 52 distinct read-through species was identified, with 24 patients having at least one read-through event, up to a maximum of 17 such transcripts in one patient. Sanger sequencing validated 28 of these transcripts and identified an additional 15, for a total of 43 distinct read-through events involving 35 gene pairs. Expression levels of 10 validated read-through transcripts were measured by quantitative PCR in pairs of matched non-involved lung tissue and lung adenocarcinoma tissue from 45 patients. Higher expression levels were observed in normal lung tissue than in the tumor counterpart, with median relative quantification ratios between normal and tumor varying from 1.90 to 7.78; the difference was statistically significant (P < 0.001, Wilcoxon's signed-rank test for paired samples) for eight transcripts: ELAVL1–TIMM44, FAM162B–ZUFSP, IFNAR2–IL10RB, INMT–FAM188B, KIAA1841–C2orf74, NFATC3–PLA2G15, SIRPB1–SIRPD, and SHANK3–ACR. This report documents the presence of read-through transcripts in apparently normal lung tissue, with inter-individual differences in patterns and abundance. It also shows their down-regulation in tumors, suggesting that these chimeric transcripts may function as tumor suppressors in lung tissue.

Genetic susceptibility variants for lung cancer: replication study and assessment as expression quantitative trait loci

Many single nucleotide polymorphisms (SNPs) have been associated with lung cancer but lack confirmation and functional characterization. We retested the association of 56 candidate SNPs with lung adenocarcinoma risk and overall survival in a cohort of 823 Italian patients and 779 healthy controls, and assessed their function as expression quantitative trait loci (eQTLs). In the replication study, eight SNPs (rs401681, rs3019885, rs732765, rs2568494, rs16969968, rs6495309, rs11634351, and rs4105144) associated with lung adenocarcinoma risk and three (rs9557635, rs4105144, and rs735482) associated with survival. Five of these SNPs acted as cis-eQTLs, being associated with the transcription of IREB2 (rs2568494, rs16969968, rs11634351, rs6495309), PSMA4 (rs6495309) and ERCC1 (rs735482), out of 10,821 genes analyzed in lung. For these three genes, we obtained experimental evidence of differential allelic expression in lung tissue, pointing to the existence of in-cis genomic variants that regulate their transcription. These results suggest that these SNPs exert their effects on cancer risk/outcome through the modulation of mRNA levels of their target genes.

Human Lung Tissue Transcriptome: Influence of Sex and Age

Background

Sex and age strongly influence the pathophysiology of human lungs, but scarce information is available about their effects on pulmonary gene expression.

Methods

We followed a discovery-validation strategy to identify sex- and age-related transcriptional differences in lung.

Results

We identified transcriptional profiles significantly associated with sex (215 genes; FDR < 0.05) and age at surgery (217 genes) in non-involved lung tissue resected from 284 lung adenocarcinoma patients. When these profiles were tested in three independent series of non-tumor lung tissue from an additional 1,111 patients, we validated the association with sex and age for 25 and 22 genes, respectively. Among the 17 sex-biased genes mapping on chromosome X, 16 have been reported to escape X-chromosome inactivation in other tissues or cells, suggesting that this mechanism influences lung transcription too. Our 22 age-related genes partially overlap with genes modulated by age in other tissues, suggesting that the aging process has similar consequences on gene expression in different organs. Finally, seven genes whose expression was modulated by sex in non-tumor lung tissue, but no age-related gene, were also validated using publicly available data from 990 lung adenocarcinoma samples, suggesting that the physiological regulatory mechanisms are only partially active in neoplastic tissue.

Conclusions

Gene expression in non-tumor lung tissue is modulated by both sex and age. These findings represent a validated starting point for research on the molecular mechanisms underlying the observed differences in the course of lung diseases among men and women of different ages.

SMC4, which is essentially involved in lung development, is associated with lung adenocarcinoma progression

Structural maintenance of chromosome 4 (SMC4) is a core subunit of condensin complexes that mainly contributes to chromosome condensation and segregation. Our previous study demonstrated that the gene expression profile during lung development is of great values for the study of lung cancer. In this study, we identified SMC4 through co-expression network analysis and clique percolation clustering using genes that constant changes during four stages of lung development. Gene ontology and KEGG pathway enrichment analysis demonstrated that SMC4 is closely related to cell cycle, cell adhesion, and RNA processing in lung development and carcinogenesis. Moreover, SMC4 is overexpressed in lung adenocarcinoma tissues and acts as an independent prognostic factor. SMC4 knockdown significantly inhibits the proliferation and invasion of A549 cells. Furthermore, we found that SMC4 interacts with DDX46 (DEAD-box helicase 46). In conclusion, the pivotal role of SMC4 in lung development and carcinogenesis suggests that genes with a similar expression pattern to SMC4 in lung development may also contribute to lung cancer progression. The identification of genes that are essentially involved in development through a comparative study between development and cancer may be a practical strategy for discovering potential biomarkers and illuminating the mechanisms of carcinogenesis.

ERp19 contributes to tumorigenicity in human gastric cancer by promoting cell growth, migration and invasion

ERp19, a mammalian thioredoxin-like protein, plays a key role in defense against endoplasmic reticulum stress. It belongs to the protein disulfide isomerize (PDI) family, whose members have been implicated in development of breast, ovarian and gastrointestinal cancers. However, the role of ERp19 in gastric cancer (GC) remains undefined. Therefore, we sought to investigate the expression and prognostic value of ERp19 in GC patients, and to explore the role of ERp19 in tumorigenicity. Expression of ERp19 in gastric tissues was assessed by immunohistochemical staining and real-time PCR in clinical samples of GC patients. Statistical analysis of clinical cases revealed that the expression levels of ERp19 were higher in tumor tissues than non-tumor tissues. And the level of ERp19 expression was correlated with tumor size, lymph node involvement and poor clinical prognosis. Furthermore, ERp19 knockdown dramatically suppressed gastric cancer cell growth, inhibited cellular migration/invasion and down-regulated the phosphorylation of FAK and paxillin, whereas ERp19 over-expression reversed these changes. We conclude that ERp19 contributes to tumorigenicity and metastasis of GC by activating the FAK signaling pathway, and may function as an oncogene in GC. ERp19 may represent a new diagnostic and prognostic marker and a novel target for the treatment of GC.

A Bagged, Partially Linear, Tree-Based Regression Procedure for Prediction and Variable Selection

OBJECTIVES

In genomics, variable selection and prediction accounting for the complex interrelationships between explanatory variables represent major challenges. Tree-based methods are powerful alternatives to classical regression models. We have recently proposed the generalized, partially linear, tree-based regression (GPLTR) procedure that integrates the advantages of generalized linear regression (allowing the incorporation of confounding variables) and of tree-based models. In this work, we use bagging to address a classical concern of tree-based methods: their instability.

METHODS

We present a bagged GPLTR procedure and three scores for variable importance. The prediction accuracy and the performance of the scores are assessed by simulation. The use of this procedure is exemplified by the analysis of a lung cancer data set. The aim is to predict the epidermal growth factor receptor (EGFR) mutation based on gene expression measurements, taking into account the ethnicity (confounder variable) and perform variable selection.

RESULTS

The procedure performs well in terms of prediction accuracy. The scores differentiate predictive variables from noise variables. Based on a lung adenocarcinoma data set, the procedure achieves good predictive performance for EGFR mutation and selects relevant genes.

CONCLUSION

The proposed bagged GPLTR procedure performs well for prediction and variable selection.

Germline polymorphisms and survival of lung adenocarcinoma patients: a genome-wide study in two European patient series

In lung cancer, the survival of patients with the same clinical stage varies widely for unknown reasons. In this two-phase study, we examined the hypothesis that germline variations influence the survival of patients with lung adenocarcinoma. First, we analyzed existing genotype and clinical data from 289 UK-resident patients with lung adenocarcinoma, identifying 86 single nucleotide polymorphisms (SNPs) that associated with survival (p < 0.01). We then genotyped these candidate SNPs in a validation series of 748 patients from Italy that resulted genetically compatible with the UK series based on principal component analysis. In a Cox proportional hazard model adjusted for age, sex and clinical stage, four SNPs were confirmed on the basis of their having a hazard ratio (HR) indicating the same direction of effect in the two series and p < 0.05. The strongest association was provided by rs2107561, an intronic SNP of PTPRG, protein tyrosine phosphatase, receptor type, G; the C allele was associated with poorer survival in both patient series (pooled analysis loge HR = 0.31; 95% CI: 0.15-0.46, p = 8.5 × 10(-5) ). PTPRG mRNA levels in 43 samples of lung adenocarcinoma were 40% of those observed in noninvolved lung tissue from the same patients. PTPRG overexpression significantly inhibited the clonogenicity of A549 lung carcinoma cells and the anchorage-independent growth of the NCI-H460 large cell lung cancer line. These four germline variants represent promising candidates that, with further study, may help predict clinical outcome. In addition, the PTPRG locus may have a role in tumor progression.

Transcription factor PREP1 induces EMT and metastasis by controlling the TGF-β-SMAD3 pathway in non-small cell lung adenocarcinoma

Pre-B-cell leukemia homeobox (Pbx)-regulating protein-1 (Prep1) is a ubiquitous homeoprotein involved in early development, genomic stability, insulin sensitivity, and hematopoiesis. Previously we have shown that Prep1 is a haploinsufficient tumor suppressor that inhibits neoplastic transformation by competing with myeloid ecotropic integration site 1 for binding to the common heterodimeric partner Pbx1. Epithelial-mesenchymal transition (EMT) is controlled by complex networks of proinvasive transcription factors responsive to paracrine factors such as TGF-β. Here we show that, in addition to inhibiting primary tumor growth, PREP1 is a novel EMT inducer and prometastatic transcription factor. In human non-small cell lung cancer (NSCLC) cells, PREP1 overexpression is sufficient to trigger EMT, whereas PREP1 down-regulation inhibits the induction of EMT in response to TGF-β. PREP1 modulates the cellular sensitivity to TGF-β by inducing the small mothers against decapentaplegic homolog 3 (SMAD3) nuclear translocation through mechanisms dependent, at least in part, on PREP1-mediated transactivation of a regulatory element in the SMAD3 first intron. Along with the stabilization and accumulation of PBX1, PREP1 induces the expression of multiple activator protein 1 components including the proinvasive Fos-related antigen 1 (FRA-1) oncoprotein. Both FRA-1 and PBX1 are required for the mesenchymal changes triggered by PREP1 in lung tumor cells. Finally, we show that the PREP1-induced mesenchymal transformation correlates with significantly increased lung colonization by cells overexpressing PREP1. Accordingly, we have detected PREP1 accumulation in a large number of human brain metastases of various solid tumors, including NSCLC. These findings point to a novel role of the PREP1 homeoprotein in the control of the TGF-β pathway, EMT, and metastasis in NSCLC.

Epigenomic characterization of locally advanced anal cancer: a radiation therapy oncology group 98-11 specimen study

BACKGROUND

The Radiation Therapy Oncology Group 98-11 clinical trial demonstrated the superiority of standard 5-fluorouracil/mitomycin-C over 5-fluorouracil/cisplatin in combination with radiation in the treatment of anal squamous cell cancer. Tumor size (>5 cm) and lymph node metastases are associated with disease progression. There may be key molecular differences (eg, DNA methylation changes) in tumors at high risk for progression.

OBJECTIVE

The objectives of this study were to determine whether there are differences in DNA methylation at individual CpG sites and within genes among locally advanced anal cancers, with large tumor size and/or nodal involvement, compared with those that are less advanced.

DESIGN

This was a case-case study among 121 patients defined as high risk (tumor size >5 cm and/or nodal involvement; n = 59) or low risk (≤5 cm, node negative; n = 62) within the mitomycin-C arm of the Radiation Therapy Oncology Group 98-11 trial. DNA methylation was measured using the Illumina HumanMethylation450 Array.

SETTINGS

The study was conducted in a tertiary care cancer center in collaboration with a national clinical trials cooperative group.

PATIENTS

The patients consisted of 74 women and 47 men with a median age of 54 years (range, 25-79 years).

MAIN OUTCOME MEASURES

DNA methylation differences at individual CpG sites and within genes between low- and high-risk patients were compared using the Mann-Whitney test (p < 0.001).

RESULTS

A total of 16 CpG loci were differentially methylated (14 increased and 2 decreased) in high- versus low-risk cases. Genes harboring differentially methylated CpG sites included known tumor suppressor genes and novel targets.

LIMITATIONS

This study only included patients in the mitomycin-C arm with tumor tissue; however, this sample was representative of the trial.

CONCLUSIONS

This is the first study to apply genome-wide methylation analysis to anal cancer. Biologically relevant differences in methylated targets were found to discriminate locally advanced from early anal cancer. Epigenetic events likely play a significant role in the progression of anal cancer and may serve as potential biomarkers.