Genome-wide analysis of DNA methylation and their associations with long noncoding RNA/mRNA expression in non-small-cell lung cancer

BigMPI4py: Python Module for Parallelization of Big Data Objects Discloses Germ Layer Specific DNA Demethylation Motifs

Parallelization in Python integrates Message Passing Interface via the mpi4py module. Since mpi4py does not support parallelization of objects greater than 2³¹ bytes, we developed BigMPI4py, a Python module that wraps mpi4py, supporting object sizes beyond this boundary. BigMPI4py automatically determines the optimal object distribution strategy, and uses vectorized methods, achieving higher parallelization efficiency. BigMPI4py facilitates the implementation of Python for Big Data applications in multicore workstations and High Performance Computer systems. We use BigMPI4py to speed-up the search for germ line specific de novo DNA methylated/unmethylated motifs from the 59 whole genome bisulfite sequencing DNA methylation samples from 27 human tissues of the ENCODE project. We developed a parallel implementation of the Kruskall-Wallis test to find CpGs with differential methylation across germ layers. The parallel evaluation of the significance of 55 million CpG achieved a 22x speedup with 25 cores allowing us an efficient identification of a set of hypermethylated genes in ectoderm and mesoderm-related tissues, and another set in endoderm-related tissues and finally, the discovery of germ layer specific DNA demethylation motifs. Our results point out that DNA methylation signal provide a higher degree of information for the demethylated state than for the methylated state. BigMPI4py is available at https://https://www.arauzolab.org/tools/bigmpi4py and https://gitlab.com/alexmascension/bigmpi4py and the Jupyter Notebook with WGBS analysis at https://gitlab.com/alexmascension/wgbs-analysis.

The Value of Dysregulated LncRNAs on Clinicopathology and Survival in Non-Small-Cell Lung Cancer: A Systematic Review and Meta-Analysis

Background: There is growing evidence that a number of lncRNAs are involved in the pathogenesis of non-small-cell lung cancer (NSCLC). However, studies on lncRNA expression in NSCLC patients are far from conclusive. Therefore, we performed a systematic review of such studies to collect and examine the evidence on the potential role of lncRNAs in the development of NSCLC.

Methods: We systematically searched seven literature databases to identify all published studies that evaluated the expression of one or more lncRNAs in human samples with NSCLC (cases) and without NSCLC (controls) from January 1, 1995 to May 24, 2021. Quality assessment of studies was conducted by using the “Quality in Prognosis Studies” (QUIPS) tool, and the heterogeneity across studies was analyzed with the I-squared statistic and chi-square-based Q-tests. Either fixed or random-effect meta-analysis was performed to summarize effect size to investigate the association between lncRNA expression and overall survival (OS), disease-free survival (DFS), progression-free survival (PFS), and clinicopathological features. The R statistical software program was used to conduct standard meta-analysis.

Results: We finally obtained 48 studies with 5,211 patients included in this review after screening. Among the 48 lncRNAs, 38 lncRNAs were consistently upregulated, and 10 were deregulated in patients with NSCLC compared with the control groups. The upregulated lncRNAs were positively associated with histological type: study number (n) = 18, odds ratio (OR) = 0.78, 95% CI: 0.65–0.95 and OR = 1.30, 95% CI: 1.08–1.57, p < 0.01; TNM stages: n = 20, OR = 0.41, 95% CI: 0.29–0.57 and OR = 2.44, 95% CI: 1.73–3.44, p < 0.01; lymph node metastasis: n = 29, OR = 0.49, 95% CI: 0.34–0.71 and OR = 2.04, 95% CI: 1.40–2.96, p < 0.01; differentiation grade: n = 6, OR = 0.61, 95% CI: 0.38–0.99 and OR = 1.63, 95% CI: 1.01–2.64, p < 0.01; distant metastasis: n = 9, OR = 0.37, 95% CI: 0.26–0.53 and OR = 2.72, 95% CI: 1.90–3.90, p < 0.01; tumor size: n = 16, OR = 0.52, 95% CI: 0.43–0.64 and OR = 1.92, 95% CI: 1.57–2.34, p < 0.01; and overall survival [n = 38, hazard ratio (HR) = 1.79, 95% CI = 1.59–2.02, p < 0.01]. Especially, five upregulated lncRNAs (linc01234, ZEB1-AS1, linc00152, PVT1, and BANCR) were closely associated with TNM Ⅲa stage (n = 5, OR = 4.07, 95% CI: 2.63–6.28, p < 0.01). However, 10 deregulated lncRNAs were not significantly associated with the pathogenesis and overall survival in NSCLC in the meta-analysis (p ≥ 0.05).

Conclusion: This systematic review suggests that the upregulated lncRNAs could serve as biomarkers for predicting promising prognosis of NSCLC. The prognostic value of downregulated lncRNA in NSCLC needs to be further explored.

Systematic Review Registration: (http://www.crd.york.ac.uk/PROSPERO).identifier CRD42021240635.

A novel prognostic biomarker for muscle invasive bladder urothelial carcinoma based on 11 DNA methylation signature

Muscle-invasive bladder urothelial carcinoma (MIBC) is a highly invasive cancer, which leads to prevalent recurrence and poor prognosis. Exploring the association of DNA methylation and the prognosis of MIBC will thus be of important value in clinical management and treatment. Bumphunter method and adaptive lasso regression were used to explore the relationship between different methylation regions (DMRs) and the prognosis of MIBC. Next, we constructed a risk prognosis model and validated this model. Moreover, the performance of this risk model was examined by using time-dependent receiver operating characteristic curve (ROC). We identified 58,449 different methylation sites and 490 different methylation regions. Among them, 11 DMRs were associated with the prognosis of MIBC through rigorous screening. Through the linear combination of 11 DMRs, a putative marker was developed, which can distinguish the survival risk in both the training dataset (HR = 2.58, 95% CI = (1.64, 4.05)) and the verification dataset (HR = 2.77, 95% CI = (1.25, 6.15)). Relatively high predictive values were observed from this model for training dataset (AUC = 0.791) and verification dataset (AUC = 0.668). Stratified analysis showed that the association was independent of gender. A nomogram was additionally generated to predict 5-year survival probability containing risk score and pathological stage. Its performance was evaluated by applying calibration curve. The methylation signature risk model based on 11 DMRs may be a reliable prognostic signature for MIBC, which provides new insights into development of individualized therapy for MIBC.

New insights into long non-coding RNAs in non-small cell lung cancer

Lung cancer is a malignant tumor that seriously threatens human life and health. Non-small cell lung cancer (NSCLC) accounts for 85 % of all lung cancer cases, and its global 5-year survival rate is only approximately 5%. Thus, the identification of new prognostic biomarkers has become one of the most urgent challenges in NSCLC research. Long noncoding RNAs (LncRNAs) are a kind of noncoding RNA whose length exceeds 200 nucleotides (nt). LncRNAs are transcribed by RNA pol II and can be subjected to posttranscriptional modifications such as blocking, polyadenylation and splicing; moreover, their expression profiles are more specific than those of mRNAs. Emerging evidence confirms that lncRNAs are associated with the occurrence and development of NSCLC and play an important role in NSCLC drug resistance. The purpose of this review was to describe the roles of lncRNAs in the development, diagnosis and prognosis of NSCLC and to explore new evidence of lncRNAs in the treatment of NSCLC drug resistance. This review provides a new perspective of lncRNAs in the treatment of NSCLC.

Discriminating Origin Tissues of Tumor Cell Lines by Methylation Signatures and Dys-Methylated Rules

DNA methylation is an essential epigenetic modification for multiple biological processes. DNA methylation in mammals acts as an epigenetic mark of transcriptional repression. Aberrant levels of DNA methylation can be observed in various types of tumor cells. Thus, DNA methylation has attracted considerable attention among researchers to provide new and feasible tumor therapies. Conventional studies considered single-gene methylation or specific loci as biomarkers for tumorigenesis. However, genome-scale methylated modification has not been completely investigated. Thus, we proposed and compared two novel computational approaches based on multiple machine learning algorithms for the qualitative and quantitative analyses of methylation-associated genes and their dys-methylated patterns. This study contributes to the identification of novel effective genes and the establishment of optimal quantitative rules for aberrant methylation distinguishing tumor cells with different origin tissues.

Promoter hypermethylation of cysteine dioxygenase type 1 in patients with non-small cell lung cancer

In the present study, promoter hypermethylation of cysteine dioxygenase type 1 (CDO1) was evaluated in non-small cell lung cancer (NSCLC) tissues to assess the value of CDO1 as a novel biomarker to improve the diagnosis of NSCLC. Tumor tissue samples and corresponding normal lung tissue samples from 42 patients with NSCLC were obtained at the Department of Thoracic Surgery, The Second Xiangya Hospital (Changsha, China). Conventional methylation-specific PCR (cMSP) and methylation-on-beads followed by quantitative methylation-specific PCR (MOB-qMSP) were used to analyze the tumor and normal lung tissue samples. Using these two methods, promoter DNA hypermethylation of the CDO1 gene was detected in 59.4 and 71.0% of tumor tissues of patients with NSCLC and in 9.4 and 0% of normal lung tissue, respectively. Compared with the rate of methylation in the well-differentiated NSCLC tissues (15.4 and 55.6%, respectively), the rate of CDO1 gene promoter methylation was higher in the poorly differentiated tissues (89.5 and 92.3%, respectively). Overall, it was demonstrated that the MOB-qMSP method had a higher positive detection rate for CDO1 hypermethylation compared with the cMSP method. In conclusion, CDO1 gene promoter hypermethylation was more frequently observed in NSCLC tissues compared with in normal lung tissues, and a high methylation frequency of the CDO1 gene in biopsy specimens of NSCLC was associated with the degree of differentiation.

Long non-coding RNA KDM5B anti-sense RNA 1 enhances tumor progression in non-small cell lung cancer

BACKGROUND

The long non-coding RNAs (lncRNAs) have been shown as a novel class of transcripts with no protein coding functions. LncRNAs can play diverse roles in cancer cell proliferation, differentiation, metastasis, and apoptosis. However, the exact contributions of lncRNA KDM5B anti-sense RNA 1 (KDM5BAS1) to non-small cell lung cancer (NSCLC) remain poorly understood.

METHODS

In current study, we have unraveled a novel function of KDM5BAS1 in NSCLC.

RESULTS

We found that KDM5BAS1 was significantly overexpressed in tumor specimens and selected cancerous cell lines. Meanwhile, higher KDM5BAS1 expression predicted poor overall survival. Increased KDM5BAS1 expression can promote proliferation or migration and inhibit apoptosis in H1838 and H1299 cells. Furthermore, knocking down of KDM5BAS1 levels can also reduce tumor growth in in vivo implantation experiments. Overexpression of KDM5BAS1 also decreased the caspase-3 immunostaining but enhanced Ki-67 staining.

CONCLUSION

Taken together, our findings indicated that KDM5BAS1 might play an oncogenic role in NSCLC and provided clues into pharmacological intervention targeting KDM5BAS1.

A long non-coding RNA LINC01288 facilitates non-small cell lung cancer progression through stabilizing IL-6 mRNA

The non-small cell lung cancer (NSCLC) denotes a malignant type of cancers. Long non-coding RNAs (lncRNAs) can actively participate in cancer development. However, the exact role of lncRNAs in NSCLC remains largely elusive. In current work, we report a novel intergenic lncRNA LINC01288 involved in NSCLC. We found that LINC01288 is frequently upregulated in NSCLC samples and cell lines. LINC01288 significantly promotes viability, migration, xenograft tumor growth and metastasis in vitro and in vivo. LINC01288 physically interacts with the IL-6 mRNA and increase the stability of IL-6 transcripts. Subsequently, the autocrine induction of IL-6 and enhanced STAT3 activation may facilitate NSCLC progression. Collectively, our data have demonstrated that LINC01288 serves as a crucial mediator of IL-6/STAT3 pathway and created novel interplay between lncRNAs and tumor development.

Long non-coding RNA as potential biomarkers in non-small-cell lung cancer: What do we know so far?

Non-small-cell lung cancer (NSCLC) remains one of the most frequent types of lung cancer characterized by its local advancement at diagnosis. Therefore, identification of new prognostic biomarkers has become one of the most important issue in NSCLC therapy. It is now well understood that genetic and epigenetic alterations are responsible for NSCLC development. Moreover, it has been recently revealed that the non-protein coding regions of the genome may serve as a template for transcription of various type of RNAs, collectively referred to as non-coding RNAs. Non-coding RNAs, including long non-coding RNAs (lncRNAs) are involved in multiple cellular processes and it has been suggested that aberrant expression of lncRNAs may lead to tumour development, including NSCLC. Furthermore, some of the established risk factors for NSCLC may have an impact on expression level of several types of lncRNAs, and thus, affect the lung carcinogenesis through lncRNAs regulation. In this review, we would like to summarise the to-date knowledge about lncRNAs as potential biomarkers in NSCLC and the role of various environmental factors, such as smoking and air pollution, in development and progression of this tumour and their effect on lncRNAs expression.

Proteomic biomarkers for lung cancer progression

Aim: Lung adenocarcinoma (LUAD) and lung squamous-cell carcinoma (LUSC) are two major subtypes of lung cancer and constitute about 70% of all the lung cancer cases. The patient's lifespan and living quality will be significantly improved if they are diagnosed at an early stage and adequately treated. Methods & results: This study comprehensively screened the proteomic dataset of both LUAD and LUSC, and proposed classification models for the progression stages of LUAD and LUSC with accuracies 86.51 and 89.47%, respectively. Discussion & conclusion: A comparative analysis was also carried out on related transcriptomic datasets, which indicates that the proposed biomarkers provide discerning power for accurate stage prediction, and will be improved when larger-scale proteomic quantitative technologies become available.

Long non-coding RNA cartilage injury-related promotes malignancy in bladder cancer

Recent advances have highlighted the important roles of long non-coding RNAs (lncRNAs) in a number of biological processes, including oncogenesis. However, the function of lncRNA cartilage injury-related (lncRNA-CIR) in bladder cancer progression remains elusive. A novel function for lncRNA-CIR in bladder cancer was identified in the present study. Reverse transcription quantitative polymerase chain reaction, viability, invasion assay and in vivo implantation were used to evaluate the role of lncRNA-CIR. It was identified that the expression of lncRNA-CIR was frequently upregulated in 52 cancerous tissues and selected bladder cancer cell lines. Additionally, upregulating lncRNA-CIR was demonstrated to promote viability and invasion in T24 and SW780 cells, whereas siRNA-mediated lncRNA-CIR-knockdown consistently exhibited the opposite effects. High lncRNA-CIR levels also dictated poor overall survival among patients with bladder cancer. Furthermore, in vivo implantation experiments also supported a tumorigenic function for lncRNA-CIR, as decreasing lncRNA-CIR levels markedly attenuated Ki-67 staining and xenograft tumor growth. Overall, the present study identified a novel function of lncRNA-CIR and indicates that lncRNA-CIR may serve as a potential biomarker for bladder cancer treatment.

Evaluation of Post-Mortem Effects on Global Brain DNA Methylation and Hydroxymethylation

The number of epigenetic studies on brain functions and diseases are dramatically increasing, but little is known about the impact of post-mortem intervals and post-sampling effects on DNA modifications such as 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC). Here, we examined post-mortem-induced changes in global brain 5mC and 5hmC levels at post-mortem intervals up to 540 min., and studied effects of tissue heat stabilization, using LUMA and ELISA. The global 5mC and 5hmC levels were generally higher in the cerebellum of adult rats than neonates. When measured by ELISA, the global 5mC content in adults, but not neonates, decreased with the post-mortem interval reaching a significantly lower level in cerebellum tissue at the post-mortem interval 540 min. (2.9 ± 0.7%; mean ± S.E.M.) compared to control (3.7 ± 0.6%). The global 5hmC levels increased with post-mortem interval reaching a significantly higher level at 540 min. (0.29 ± 0.06%) compared to control (0.19 ± 0.03%). This suggests that the post-mortem interval may confound 5mC and 5hmC analysis in human brain tissues as the post-mortem handling could vary substantially. The reactive oxygen species (ROS) level in cerebellum also increased over time, in particular in adults, and may be part of the mechanism that causes the observed post-mortem changes in 5mC and 5hmC. The global 5mC and 5hmC states were unaffected by heat stabilization, allowing analysis of tissues that are stabilized to preserve more labile analytes. Further studies in human samples are needed to confirm post-mortem effects on DNA methylation/hydroxymethylation and elucidate details of the underlying mechanisms.