Integrative Analysis of Methylation and Transcriptome Identified Epigenetically Regulated lncRNAs With Prognostic Relevance for Thyroid Cancer

Early detection and diagnosis of cancer with interpretable machine learning to uncover cancer-specific DNA methylation patterns

Cancer, a collection of more than two hundred different diseases, remains a leading cause of morbidity and mortality worldwide. Usually detected at the advanced stages of disease, metastatic cancer accounts for 90% of cancer-associated deaths. Therefore, the early detection of cancer, combined with current therapies, would have a significant impact on survival and treatment of various cancer types. Epigenetic changes such as DNA methylation are some of the early events underlying carcinogenesis. Here, we report on an interpretable machine learning model that can classify 13 cancer types as well as non-cancer tissue samples using only DNA methylome data, with 98.2% accuracy. We utilize the features identified by this model to develop EMethylNET, a robust model consisting of an XGBoost model that provides information to a deep neural network that can generalize to independent data sets. We also demonstrate that the methylation-associated genomic loci detected by the classifier are associated with genes, pathways and networks involved in cancer, providing insights into the epigenomic regulation of carcinogenesis.

Progress in long non-coding RNAs as prognostic factors of papillary thyroid carcinoma

Papillary thyroid carcinoma (PTC) is generally recognized as a slow-growing tumor. However, a small subset of patients may still experience relapse or metastasis shortly after therapy, leading to a poor prognosis and raising concerns about excessive medical treatment. One major challenge lies in the inadequacy of effective biomarkers for accurate risk stratification. Long non-coding RNAs (lncRNAs), which are closely related to malignant characteristics and poor prognosis, play a significant role in the genesis and development of PTC through various pathways. The objective of this review is to provide a comprehensive summary of the biological functions of lncRNAs in PTC, identify prognosis-relevant lncRNAs, and explore their potential mechanisms in drug resistance to BRAF kinase inhibitors, tumor dedifferentiation, and lymph node metastasis. By doing so, this review aims to offer valuable references for both basic research and the prediction of PTC prognosis.

Prognostic significance of MALAT1 in clear cell renal cell carcinoma based on TCGA and GEO

Long noncoding RNAs metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) can regulate tumorigenesis and progression of various cancers. However, there is little known about the tumor biology and regulatory mechanism of MALAT1 in clear cell renal cell carcinoma (ccRCC). The objective of this study was to evaluate the prognostic value and potential functions of MALAT1 in ccRCC based on the cancer genome atlas. Through bioinformatics research, we analyzed the expression of MALAT1 in ccRCC, and the relationship with clinicopathological features, overall survival and infiltration of immune cells, and established the prognostic models. The results showed that MALAT1 was highly expressed in ccRCC tissues and predicted poor ccRCC patient outcome. The expression level of MALAT1 was significantly correlated with histologic grade, pathologic grade, T stage, M stage. ROC curve showed that MALAT1 had a good diagnostic accuracy, area under the curve of 0.752. The univariate and multivariate cox regression analysis showed that high MALAT1 expression was an independent prognostic factor for overall survival in the cancer genome atlas (hazard ratio = 2.271, 95% confidence interval: 1.435-3.593, P < .001). Gene set enrichment analysis revealed that MALAT1 expression was associated with the DNA methylation, epigenetic regulation of gene expression signaling pathway. In addition, the prognostic models were established to predict 1-, 3- and 5-year survival. This study showed that high expression of MALAT1 might be a potential diagnostic and prognostic biomarker.

Advances in transcriptomics and proteomics in differentiated thyroid cancer: An updated perspective (Review)

Thyroid cancer (TC) is a broad classification of neoplasms that includes differentiated thyroid cancer (DTC) as a common histological subtype. DTC is characterized by an increased mortality rate in advanced stages, which contributes to the overall high mortality rate of DTC. This progression is mainly attributed to alterations in molecular driver genes, resulting in changes in phenotypes such as invasion, metastasis and dedifferentiation. Clinical management of DTC is challenging due to insufficient diagnostic and therapeutic options. The advent of-omics technology has presented a promising avenue for the diagnosis and treatment of DTC. Identifying molecular markers that can predict the early progression of DTC to a late adverse outcome is essential for precise diagnosis and treatment. The present review aimed to enhance our understanding of DTC by integrating big data with biological systems through-omics technology, specifically transcriptomics and proteomics, which can shed light on the molecular mechanisms underlying carcinogenesis.

Ferroptosis-associated lncRNA prognostic signature predicts prognosis and immune response in clear cell renal cell carcinoma

Clear cell Renal Cell Carcinoma (ccRCC), the most deadly and life-threatening tumor in the urinary system, has a dismal prognosis and a high risk of metastasizing. Regulation of ferroptosis is a prospective therapeutic target to eradicate malignant cells. Our objective was to seek ferroptosis-associated long non-coding RNAs (FALs) and developed a prediction signature for ccRCC. We extracted transcriptome data and clinical information from The Cancer Genome Atlas (TCGA) databases. Ferroptosis-associated genes (FAGs) were obtained from FerrDb database. A ferroptosis-associated lncRNA prognostic signature (FLPS) of ccRCC was generated utilizing univariate Cox regression, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression, sequentially, based on 8 lncRNAs (LINC00460, AC124854.1, AC084876.1, IGFL2-AS1, LINC00551, AC083967.1, AC073487.1, and LINC02446). The signature's independent predictive value for ccRCC was demonstrated using univariate and multivariate regression analysis (P < 0.05). Subsequently, by combining independent predictive factors, a prognostic nomogram was established. Immunity analysis proclaimed a striking difference in terms of cells, function, checkpoints, and ESTIMATE scores between low- and high-risk groups. Overall, the innovative signature of ferroptosis-associated signatures may have a considerable effect on the immune response and prognosis for ccRCC.

Identification of long non-coding RNA using single nucleotide epimutation analysis: a novel gene discovery approach

BACKGROUND

Long non-coding RNAs (lncRNAs) are involved in a variety of mechanisms related to tumorigenesis by functioning as oncogenes or tumor-suppressors or even harboring oncogenic and tumor-suppressing effects; representing a new class of cancer biomarkers and therapeutic targets. It is predicted that more than 35,000 ncRNA especially lncRNA are positioned at the intergenic regions of the human genome. Emerging research indicates that one of the key pathways controlling lncRNA expression and tissue specificity is epigenetic regulation.

METHODS

In the current article, a novel approach for lncRNA discovery based on the intergenic position of most lncRNAs and a single CpG site methylation level representing epigenetic characteristics has been suggested.

RESULTS

Using this method, a novel antisense lncRNA named LINC02892 presenting three transcripts without the capacity of coding a protein was found exhibiting nuclear, cytoplasmic, and exosome distributions.

CONCLUSION

The current discovery strategy could be applied to identify novel non-coding RNAs influenced by methylation aberrations.

The Crosstalk Between Long Non-Coding RNAs and Various Types of Death in Cancer Cells

With the increasing aging population, cancer has become one of the leading causes of death worldwide, and the number of cancer cases and deaths is only anticipated to grow further. Long non-coding RNAs (lncRNAs), which are closely associated with the expression level of downstream genes and various types of bioactivity, are regarded as one of the key regulators of cancer cell proliferation and death. Cell death, including apoptosis, necrosis, autophagy, pyroptosis, and ferroptosis, plays a vital role in the progression of cancer. A better understanding of the regulatory relationships between lncRNAs and these various types of cancer cell death is therefore urgently required. The occurrence and development of tumors can be controlled by increasing or decreasing the expression of lncRNAs, a method which confers broad prospects for cancer treatment. Therefore, it is urgent for us to understand the influence of lncRNAs on the development of different modes of tumor death, and to evaluate whether lncRNAs have the potential to be used as biological targets for inducing cell death and predicting prognosis and recurrence of chemotherapy. The purpose of this review is to provide an overview of the various forms of cancer cell death, including apoptosis, necrosis, autophagy, pyroptosis, and ferroptosis, and to describe the mechanisms of different types of cancer cell death that are regulated by lncRNAs in order to explore potential targets for cancer therapy.

An epigenomic landscape of cervical intraepithelial neoplasia and cervical cancer using single-base resolution methylome and hydroxymethylome

BACKGROUND

Cervical cancer (CC) is the second leading cause of cancer death among women worldwide. Epigenetic regulation of gene expression through DNA methylation and hydroxymethylation plays a pivotal role during tumorigenesis. In this study, to analyze the epigenomic landscape and identify potential biomarkers for CCs, we selected a series of samples from normal to cervical intra-epithelial neoplasia (CINs) to CCs and performed an integrative analysis of whole-genome bisulfite sequencing (WGBS-seq), oxidative WGBS, RNA-seq, and external histone modifications profiling data.

RESULTS

In the development and progression of CC, there were genome-wide hypo-methylation and hypo-hydroxymethylation, accompanied by local hyper-methylation and hyper-hydroxymethylation. Hydroxymethylation prefers to distribute in the CpG islands and CpG shores, as displayed a trend of gradual decline from health to CIN2, while a trend of increase from CIN3 to CC. The differentially methylated and hydroxymethylated region-associated genes both enriched in Hippo and other cancer-related signaling pathways that drive cervical carcinogenesis. Furthermore, we identified eight novel differentially methylated/hydroxymethylated-associated genes (DES, MAL, MTIF2, PIP5K1A, RPS6KA6, ANGEL2, MPP, and PAPSS2) significantly correlated with the overall survival of CC. In addition, no any correlation was observed between methylation or hydroxymethylation levels and somatic copy number variations in CINs and CCs.

CONCLUSION

Our current study systematically delineates the map of methylome and hydroxymethylome from CINs to CC, and some differentially methylated/hydroxymethylated-associated genes can be used as the potential epigenetic biomarkers in CC prognosis.

Integrative Analysis of DNA Methylation Data and Transcriptome Data Identified a DNA Methylation-Dysregulated Four-LncRNA Signature for Predicting Prognosis in Head and Neck Squamous Cell Carcinoma

Increasing evidence has demonstrated the crosstalk between DNA epigenetic alterations and aberrant expression of long non-coding RNAs (lncRNAs) during carcinogenesis. However, epigenetically dysregulated lncRNAs and their functional and clinical roles in Head and Neck Squamous Cell Carcinoma (HNSCC) are still not explored. In this study, we performed an integrative analysis of DNA methylation data and transcriptome data and identified a DNA methylation-dysregulated four-lncRNA signature (DNAMeFourLncSig) from 596 DNA methylation-dysregulated lncRNAs using a machine-learning-based feature selection method, which classified the patients of the discovery cohort into two risk groups with significantly different survival including overall survival, disease-specific survival, and progression-free survival. Then the DNAMeFourLncSig was implemented to another two HNSCC patient cohorts and showed similar prognostic values in both. Results from multivariable Cox regression analysis revealed that the DNAMeFourLncSig might be an independent prognostic factor. Furthermore, the DNAMeFourLncSig was substantially correlated with the complete response rate of chemotherapy and may predict chemotherapy response. Functional in silico analysis found that DNAMeFourLncSig-related mRNAs were mainly enriched in cell differentiation, tissue development and immune-related pathways. Overall, our study will improve our understanding of underlying transcriptional and epigenetic mechanisms in HNSCC carcinogenesis and provided a new potential biomarker for the prognosis of patients with HNSCC.

Identification and Analysis of An Epigenetically Regulated Five-lncRNA Signature Associated With Outcome and Chemotherapy Response in Ovarian Cancer

The deregulation of long non-coding RNAs (lncRNAs) by epigenetic alterations has been implicated in cancer initiation and progression. However, the epigenetically regulated lncRNAs and their association with clinical outcome and therapeutic response in ovarian cancer (OV) remain poorly investigated. This study performed an integrative analysis of DNA methylation data and transcriptome data and identified 419 lncRNAs as potential epigenetically regulated lncRNAs. Using machine-learning and multivariate Cox regression analysis methods, we identified and developed an epigenetically regulated lncRNA expression signature (EpiLncRNASig) consisting of five lncRNAs from the list of 17 epigenetically regulated lncRNAs significantly associated with outcome. The EpiLncRNASig could stratify patients into high-risk groups and low-risk groups with significantly different survival and chemotherapy response in different patient cohorts. Multivariate Cox regression analyses, after adjusted by other clinical features and treatment response, demonstrated the independence of the DEpiLncSig in predicting survival. Functional analysis for relevant protein-coding genes of the DEpiLncSig indicated enrichment of known immune-related or cancer-related biological pathways. Taken together, our study not only provides a promising prognostic biomarker for predicting outcome and chemotherapy response but also will improve our understanding of lncRNA epigenetic regulation mechanisms in OV.