Identification of four differentially methylated genes as prognostic signatures for stage I lung adenocarcinoma

Comprehensive DNA methylation profiling by MeDIP-NGS identifies potential genes and pathways for epithelial ovarian cancer

Ovarian cancer, among all gynecologic malignancies, exhibits the highest incidence and mortality rate, primarily because it is often presents with non-specific or no symptoms during its early stages. For the advancement of Ovarian Cancer Diagnosis, it is crucial to identify the potential molecular signatures that could significantly differentiate between healthy and ovarian cancerous tissues and can be used further as a diagnostic biomarker for detecting ovarian cancer. In this study, we investigated the genome-wide methylation patterns in ovarian cancer patients using Methylated DNA Immunoprecipitation (MeDIP-Seq) followed by NGS. Identified differentially methylated regions (DMRs) were further validated by targeted bisulfite sequencing for CpG site-specific methylation profiles. Furthermore, expression validation of six genes by Quantitative Reverse Transcriptase-PCR was also performed. Out of total 120 differentially methylated genes (DMGs), 68 genes were hypermethylated, and 52 were hypomethylated in their promoter region. After analysis, we identified the top 6 hub genes, namely POLR3B, PLXND1, GIGYF2, STK4, BMP2 and CRKL. Interestingly we observed Non-CpG site methylation in the case of POLR3B and CRKL which was statistically significant in discriminating ovarian cancer samples from normal controls. The most significant pathways identified were focal adhesion, the MAPK signaling pathway, and the Ras signaling pathway. Expression analysis of hypermethylated genes was correlated with the downregulation of the genes. POLR3B and GIGYF2 turned out to be the novel genes associated with the carcinogenesis of EOC. Our study demonstrated that methylation profiling through MeDIP-sequencing has effectively identified six potential hub genes and pathways that might exacerbate our understanding of underlying molecular mechanisms of ovarian carcinogenesis.

A Novel Methylation-based Model for Prognostic Prediction in Lung Adenocarcinoma

Objectives

Specific methylation sites have shown promise in the early diagnosis of lung adenocarcinoma (LUAD). However, their utility in predicting LUAD prognosis remains unclear. This study aimed to construct a reliable methylation-based predictor for accurately predicting the prognosis of LUAD patients.

Methods

DNA methylation data and survival data from LUAD patients were obtained from the TCGA and a GEO series. A DNA methylation-based signature was developed using univariate least absolute shrinkage and selection operators and multivariate Cox regression models.

Results

Eight CpG sites were identified and validated as optimal prognostic signatures for the overall survival of LUAD patients. Receiver operating characteristic analysis demonstrated the high predictive ability of the eight-site methylation signature combined with clinical factors for overall survival.

Conclusion

This research successfully identified a novel eight-site methylation signature for predicting the overall survival of LUAD patients through bioinformatic integrated analysis of gene methylation markers used in the early diagnosis of lung cancer.

A novel necroptosis related gene signature and regulatory network for overall survival prediction in lung adenocarcinoma

We downloaded the mRNA expression profiles of patients with LUAD and corresponding clinical data from The Cancer Genome Atlas (TCGA) database and used the Least Absolute Shrinkage and Selection Operator Cox regression model to construct a multigene signature in the TCGA cohort, which was validated with patient data from the GEO cohort. Results showed differences in the expression levels of 120 necroptosis-related genes between normal and tumor tissues. An eight-gene signature (CYLD, FADD, H2AX, RBCK1, PPIA, PPID, VDAC1, and VDAC2) was constructed through univariate Cox regression, and patients were divided into two risk groups. The overall survival of patients in the high-risk group was significantly lower than of the patients in the low-risk group in the TCGA and GEO cohorts, indicating that the signature has a good predictive effect. The time-ROC curves revealed that the signature had a reliable predictive role in both the TCGA and GEO cohorts. Enrichment analysis showed that differential genes in the risk subgroups were associated with tumor immunity and antitumor drug sensitivity. We then constructed an mRNA-miRNA-lncRNA regulatory network, which identified lncRNA AL590666. 2/let-7c-5p/PPIA as a regulatory axis for LUAD. Real-time quantitative PCR (RT-qPCR) was used to validate the expression of the 8-gene signature. In conclusion, necroptosis-related genes are important factors for predicting the prognosis of LUAD and potential therapeutic targets.

DNA methylation molecular subtypes for prognosis prediction in lung adenocarcinoma

Aims

Lung cancer is one of the main results in tumor-related mortality. Methylation differences reflect critical biological features of the etiology of LUAD and affect prognosis.

Methods

In the present study, we constructed a prediction prognostic model integrating various DNA methylation used high-throughput omics data for improved prognostic evaluation.

Results

Overall 21,120 methylation sites were identified in the training dataset. Overall, 237 promoter genes were identified by genomic annotation of 205 CpG loci. We used Akakike Information Criteria (AIC) to obtain the validity of data fitting, but to prevent overfitting. After AIC clustering, specific methylation sites of cg19224164 and cg22085335 were left. Prognostic analysis showed a significant difference among the two groups (P = 0.017). In particular, the hypermethylated group had a poor prognosis, suggesting that these methylation sites may be a marker of prognosis.

Conclusion

The model might help in the identification of unknown biomarkers in predicting patient prognosis in LUAD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-022-01924-0.

Network based systems biology approach to identify diseasome and comorbidity associations of Systemic Sclerosis with cancers

Systemic Sclerosis (SSc) is an autoimmune disease associated with changes in the skin's structure in which the immune system attacks the body. A recent meta-analysis has reported a high incidence of cancer prognosis including lung cancer (LC), leukemia (LK), and lymphoma (LP) in patients with SSc as comorbidity but its underlying mechanistic details are yet to be revealed. To address this research gap, bioinformatics methodologies were developed to explore the comorbidity interactions between a pair of diseases. Firstly, appropriate gene expression datasets from different repositories on SSc and its comorbidities were collected. Then the interconnection between SSc and its cancer comorbidities was identified by applying the developed pipelines. The pipeline was designed as a generic workflow to demonstrate a premise comorbid condition that integrate regarding gene expression data, tissue/organ meta-data, Gene Ontology (GO), Molecular pathways, and other online resources, and analyze them with Gene Set Enrichment Analysis (GSEA), Pathway enrichment and Semantic Similarity (SS). The pipeline was implemented in R and can be accessed through our Github repository: https://github.com/hiddenntreasure/comorbidity. Our result suggests that SSc and its cancer comorbidities share differentially expressed genes, functional terms (gene ontology), and pathways. The findings have led to a better understanding of disease pathways and our developed methodologies may be applied to any set of diseases for finding any association between them. This research may be used by physicians, researchers, biologists, and others.

Comprehensive Bioinformatics Analysis to Identify the Gene HMMR Associated With Lung Adenocarcinoma Prognosis and Its Mechanism of Action in Multiple Cancers

Background

Lung cancer is the third most frequently diagnosed cancer in the world, with lung adenocarcinoma (LUAD) as the most common pathological type. But studies on the predictive effect of a single gene on LUAD are limited. We aimed to discover new predictive markers for LUAD.

Methods

Differentially high-expressed genes at each stage were obtained from the TCGA and GTEx databases. The functions of these genes were investigated through GO enrichment and KEGG pathway analyses. Then, the key genes were selected by applying whole gene overall survival time. The expression of the key gene was studied in LUAD, and survival analysis was performed using Kaplan-Meier mapper, followed by univariate and multifactorial COX analysis. Finally, the gene expression and its prognostic significance in the pan-cancer were examined.

Results

A total of 10,106 DEGs were obtained from the two datasets. The top 266 differentially upregulated genes intersected with the top 1,497 overall survival-related genes, and 87 key genes were identified. High-expressed HMMR was associated with a poor prognosis of LUAD. Univariate and multifactorial Cox analysis showed that HMMR was an independent prognostic factor for LUAD patients. A high HMMR expression was strongly associated with the overall survival (OS) and disease-specific survival (DSS) in 11 cancer types and with poorer OS, DSS, and PFI in 10 cancer types.

Conclusion

HMMR may be an independent prognostic indicator and an important biomarker in diagnosing and predicting the survival of LUAD patients. Also, HMMR may be a key predictor of a variety of cancers.

Development and validation of a novel epigenetic-related prognostic signature and candidate drugs for patients with lung adenocarcinoma

Background: Epigenetic dysregulation has been increasingly proposed as a hallmark of cancer. Here, the aim of this study is to establish an epigenetic-related signature for predicting the prognosis of lung adenocarcinoma (LUAD) patients.

Results: Five epigenetic-related genes (ERGs) (ARRB1, PARP1, PKM, TFDP1, and YWHAZ) were identified as prognostic hub genes and used to establish a prognostic signature. According our risk score system, LUAD patients were stratified into high and low risk groups, and patients in the high risk group had a worse prognosis. ROC analysis indicated that the signature was precise in predicting the prognosis. A new nomogram was constructed based on the five hub genes, which can predict the OS of every LUAD patients. The calibration curves showed that the nomogram had better accuracy in prediction. Finally, candidate drugs that aimed at hub ERGs were identified, which included 47 compounds.

Conclusions: Our epigenetic-related signature nomogram can effectively and reliably predict OS of LUAD patients, also we provide precise targeted chemotherapeutic drugs.

Methods: The genomic data and clinical data of LUAD cohort were downloaded from the TCGA database and ERGs were obtained from the EpiFactors database. GSE31210 and GSE50081 microarray datasets were included as independent external datasets. Univariate Cox, LASSO regression, and multivariate Cox analyses were applied to construct the epigenetic-related signature.

RFX1: a promising therapeutic arsenal against cancer

Regulatory factor X1 (RFX1) is an evolutionary conserved transcriptional factor that influences a wide range of cellular processes such as cell cycle, cell proliferation, differentiation, and apoptosis, by regulating a number of target genes that are involved in such processes. On a closer look, these target genes also play a key role in tumorigenesis and associated events. Such observations paved the way for further studies evaluating the role of RFX1 in cancer. These studies were indispensable due to the failure of conventional chemotherapeutic drugs to target key cellular hallmarks such as cancer stemness, cellular plasticity, enhanced drug efflux, de-regulated DNA repair machinery, and altered pathways evading apoptosis. In this review, we compile significant evidence for the tumor-suppressive activities of RFX1 while also analyzing its oncogenic potential in some cancers. RFX1 induction decreased cellular proliferation, modulated the immune system, induced apoptosis, reduced chemoresistance, and sensitized cancer stem cells for chemotherapy. Thus, our review discusses the pleiotropic function of RFX1 in multitudinous gene regulations, decisive protein–protein interactions, and also its role in regulating key cell signaling events in cancer. Elucidation of these regulatory mechanisms can be further utilized for RFX1 targeted therapy.

Screening of Methylation Gene Sites as Prognostic Signature in Lung Adenocarcinoma

PURPOSE

Most lung adenocarcinoma (LUAD) patients are diagnosed at the advanced stage and have poor prognosis. DNA methylation plays an important role in the prognosis prediction of cancers. The objective of this study was to identify new DNA methylation sites as biomarkers for LUAD prognosis.

MATERIALS AND METHODS

We downloaded DNA methylation data from The Cancer Genome Atlas data portal. Cox proportional hazard regression model and random survival forest algorithm were applied to identify the DNA-methylation sites. Methylation of sites were validated in the Gene Expression Omnibus cohorts. Function annotation were done to explore the biological function of DNA methylated sites signature.

RESULTS

Six DNA methylation sites were identified as prognosis signature. The signature yielded acceptable discrimination between the high-risk group and low-risk group. The discrimination effect of this DNA methylation signature for the OS was obvious, with a median OS of 21.89 months vs. 17.74 months for high-risk vs. low-risk groups. This prognostic prediction model was validated by the test group and GEO dataset. The predictive survival value was higher for the prognostic prediction model than that for the tumor node metastasis stage. Adjuvant hemotherapy could not affect the prediction of the signature. Functional analysis indicated that these signature genes were involved in protein binding and cytoplasm.

CONCLUSION

We identified the prognostic signature for LUAD by combining six DNA methylation sites. This could service as potential robust and specificity signature in the prognosis prediction of LUAD.

Molecular subtypes based on DNA promoter methylation predict prognosis in lung adenocarcinoma patients

BACKGROUND

The heterogeneity of lung adenocarcinoma (LADC) makes the early diagnosis and treatment of the disease difficult. Gene silencing of DNA methylation is an important mechanism of tumorigenesis. A combination of methylation and clinical features can improve the classification of LADC heterogeneity.

RESULTS

We investigated the prognostic significance of 335 specimen subgroups of Lung adenocarcinoma based on the DNA methylation level. The differences in DNA methylation levels were related to the TNM stage classification, age, gender, and prognostic values. Seven subtypes were determined using 774 CpG sites that significantly affected the survival rate based on the consensus clustering. Finally, we constructed a prognostic model that performed well and further verified it in our test group.

CONCLUSIONS

This study shows that classification based on DNA methylation might aid in demonstrating heterogeneity within formerly characterized LADC molecular subtypes, assisting in the development of efficient, personalized therapy.

METHODS

Methylation data of lung adenocarcinoma were downloaded from the University of California Santa Cruz (UCSC) cancer browser, and the clinical patient information and RNA-seq archives were acquired from the Cancer Genome Atlas (TCGA). CpG sites were identified based on the significant correlation with the prognosis and used further to cluster the cases uniformly into several subtypes.

A ten-gene signature-based risk assessment model predicts the prognosis of lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is a major cause of cancer death. Therefore, identifying potential prognostic risk factors is critical to improve the survival of patients with LUAD.

Methods

Here, relevant datasets were downloaded from TCGA and GEO databases to screen the differentially expressed genes (DEGs). Univariate Cox analysis, LASSO regression analysis and multivariate Cox analysis were conducted on the DEGs combined with TCGA clinical data, and finally a risk assessment model based on 10 feature genes was constructed.

Results

The prognosis of patients was evaluated after the patients were grouped based on the median risk score and the results showed that the survival time of patients in the high-risk group was significantly shorter than that in the low-risk group. ROC analysis showed that the AUC values of the 1, 3, 5-year survival were 0.753, 0.724, and 0.73, respectively, indicating that the model was precise in predicting the prognosis, which was also verified in the external dataset GSE72094. In addition, a significant correlation was found between the risk score and the clinical stages of LUAD, that is, a later stage always corresponded to a higher risk score. Then, we performed survival analysis on the 10 feature genes independently in the TCGA-LUAD dataset through the GEPIA database, finding that the high expression of 6 genes (COL5A2, PLEK2, BAIAP2L2, S100P, ZIC2, SFXN1) was associated with the poor prognosis of LUAD patients.

Conclusion

To sum, this study established a 10-gene risk assessment model and further evaluated its value in predicting LUAD prognosis, which provided a new method for the prognosis prediction of LUAD.

A 10-gene signature as a predictor of biochemical recurrence after radical prostatectomy in patients with prostate cancer and a Gleason score ≥7

The time and speed of biochemical recurrence (BCR) of prostate cancer (PCa) after radical prostatectomy (RP) is highly variable. Stratification methods based on TNM staging and Gleason score (GS) do not allow the identification of patients at risk of BCR following RP. Therefore, the aim of the present study was to identify molecular signatures that can predict BCR risk effectively and facilitate treatment-related decisions for patients with PCa. RNA sequencing data and corresponding clinical data were downloaded from The Cancer Genome Atlas (TCGA) and Oncomine databases. Bioinformatics analysis was performed to identify differentially expressed genes in patients with GS=6 and GS ≥7. Cox regression models were used to determine the PCa signature (PCasig) and a clinical nomogram for the prediction of BCR. The performance of nomograms was assessed using time-dependent receiver operating characteristic curves and the concordance index (C-index). A PCasig comprising 10 genes, including SEMG2, KCNJ16, TFAP2B, SYCE1, KCNU1, AFP, GUCY1B2, GRIA4, NXPH1 and SOX11, was significantly associated with BCR, which was identified in TCGA cohort [hazard ratio (HR), 5.18; 95% CI, 3.241-8.272; C-index, 0.777] and validated in the Oncomine cohort (HR, 2.78; 95% CI, 1.39-5.54; C-index, 0.66). The expression levels of SEMG2, KCNJ16 and TFAP2B were downregulated in patients with GS ≥7. The expression levels of SYCE1, KCNU1, AFP, GUCY1B2, GRIA4, NXPH1 and SOX11 were upregulated in patients with GS ≥7. The clinical nomogram was constructed based on the GS and pathologic T stage (HR, 4.15; 95% CI, 1.39-5.54; C-index, 0.713). The addition of the PCasig to the clinical nomogram significantly improved prognostic value (HR, 7.25; 95% CI, 4.54-11.56; C-index, 0.782) with an net reclassification improvement of 75.3% (95% CI, 46.8-104.6%). Furthermore, the endogenous expression of each gene in the PCasig was measured in five PCa cell lines and in normal prostate cells, and these genes exhibited different expression levels relative to one another. In conclusion, an PCasig was identified by mining TCGA and successfully validated in an Oncomine cohort. This PCasig was an independent prognostic factor with a greater prognostic value for all patients regardless of GS than traditional clinical variables, which can improve the performance of clinical nomograms in predicting BCR of patients with GS ≥7.

Identification of gene expression and DNA methylation of SERPINA5 and TIMP1 as novel prognostic markers in lower-grade gliomas

Background

Lower-grade gliomas (LGGs) is characteristic with great difference in prognosis. Due to limited prognostic biomarkers, it is urgent to identify more molecular markers to provide a more objective and accurate tumor classification system for LGGs.

Methods

In the current study, we performed an integrated analysis of gene expression data and genome-wide methylation data to determine novel prognostic genes and methylation sites in LGGs.

Results

To determine genes that differentially expressed between 44 short-term survivors (<2 years) and 48 long-term survivors (≥2 years), we searched LGGs TCGA RNA-seq dataset and identified 106 differentially expressed genes. SERPINA5 and TIMP1 were selected for further study. Kaplan-Meier plots showed that SERPINA5 and TIMP1 expression were significantly correlated with overall survival (OS) and relapse-free survival (RFS) in TCGA LGGs patients. We next validated the correlation between the candidate genes expression and clinical outcome in CGGA LGGs patients. Multivariate analysis showed that TIMP1 mRNA expression had a significant prognostic value independent of other variables (HR = 4.825, 95% CI = 1.370-17.000, P = 0.014). Then, differential methylation sites were identified from differentially candidate gene expression groups, and all four methylation sites were significantly negatively correlated with gene expression (spearman r <  - 0.5, P < 0.0001). Moreover, hyper-methylation of four methylation sites indicated better OS (P < 0.05), and three of them also shown statistical significantly association with better RFS, except for SERPINA5 cg15509705 (P = 0.0762).

Conclusion

Taken together, these findings indicated that the gene expression and methylation of SERPINA5 and TIMP1 may serve as prognostic predictors in LGGs and may help to precise the current histology-based tumors classification system and to provide better stratification for future clinical trials.

Systematic analyses of regulatory variants in DNase I hypersensitive sites identified two novel lung cancer susceptibility loci

DNase I hypersensitive sites (DHS) are abundant in regulatory elements, such as promoter, enhancer and transcription factor binding sites. Many studies have revealed that disease-associated variants were concentrated in DHS-related regions. However, limited studies are available on the roles of DHS-related variants in lung cancer. In this study, we performed a large-scale case-control study with 20 871 lung cancer cases and 15 971 controls to evaluate the associations between regulatory genetic variants in DHS and lung cancer susceptibility. The expression quantitative trait loci (eQTL) analysis and pathway-enrichment analysis were performed to identify the possible target genes and pathways. In addition, we performed motif-based analysis to explore the lung-cancer-related motifs using sequence kernel association test. Two novel variants, rs186332 in 20q13.3 (C>T, odds ratio [OR] = 1.17, 95% confidence interval [95% CI]: 1.10-1.24, P = 8.45 × 10-7) and rs4839323 in 1p13.2 (T>C, OR = 0.92, 95% CI: 0.89-0.95, P = 1.02 × 10-6) showed significant association with lung cancer risk. The eQTL analysis suggested that these two SNPs might regulate the expression of MRGBP and SLC16A1, respectively. What's more, the expression of both MRGBP and SLC16A1 was aberrantly elevated in lung tumor tissues. The motif-based analysis identified 10 motifs related to the risk of lung cancer (P < 1.71 × 10-4). Our findings suggested that variants in DHS might modify lung cancer susceptibility through regulating the expression of surrounding genes. This study provided us a deeper insight into the roles of DHS-related genetic variants for lung cancer.

Whole Exome Sequencing of Highly Aggregated Lung Cancer Families Reveals Linked Loci for Increased Cancer Risk on Chromosomes 12q, 7p, and 4q

BACKGROUND

Lung cancer kills more people than any other cancer in the United States. In addition to environmental factors, lung cancer has genetic risk factors as well, though the genetic etiology is still not well understood. We have performed whole exome sequencing on 262 individuals from 28 extended families with a family history of lung cancer.

METHODS

Parametric genetic linkage analysis was performed on these samples using two distinct analyses-the lung cancer only (LCO) analysis, where only patients with lung cancer were coded as affected, and the all aggregated cancers (AAC) analysis, where other cancers seen in the pedigree were coded as affected.

RESULTS

The AAC analysis yielded a genome-wide significant result at rs61943670 in POLR3B at 12q23.3. POLR3B has been implicated somatically in lung cancer, but this germline finding is novel and is a significant expression quantitative trait locus in lung tissue. Interesting genome-wide suggestive haplotypes were also found within individual families, particularly near SSPO at 7p36.1 in one family and a large linked haplotype spanning 4q21.3-28.3 in a different family. The 4q haplotype contains potential causal rare variants in DSPP at 4q22.1 and PTPN13 at 4q21.3.

CONCLUSIONS

Regions on 12q, 7p, and 4q are linked to increased cancer risk in highly aggregated lung cancer families, 12q across families and 7p and 4q within a single family. POLR3B, SSPO, DSPP, and PTPN13 are currently the best candidate genes.

IMPACT

Functional work on these genes is planned for future studies and if confirmed would lead to potential biomarkers for risk in cancer.