Integrative analysis of DNA methylation and gene expression identified cervical cancer-specific diagnostic biomarkers

A novel tertiary lymphoid structure-associated signature accurately predicts patient prognosis and facilitates the selection of personalized treatment strategies for HNSCC

Background

Head and neck squamous cell carcinoma (HNSCC) is the most common type of head and neck cancer and is characterized by its aggressive nature and variable prognosis and response to immunotherapy. Tertiary lymphoid structures (TLSs) play crucial roles in creating a favourable immune microenvironment to control tumour progression. However, the specific impact of these structures on HNSCC has not been thoroughly studied.

Methods

In this study, a comprehensive review of tertiary lymphoid structures was conducted by analysing 9 TLS-associated genes in a cohort of 871 HNSCC patients. Distinct TLS-related subgroups were identified through unsupervised clustering analysis, and the associated genes were explored. Prognostic genes were identified via univariate Cox and Boruta algorithms, and a novel TLS-related scoring system was developed via the GSVA algorithm.

Results

Our study revealed that patients with higher TLS-related scores had improved overall survival and were more likely to benefit from immunotherapy. Furthermore, we observed a significant negative correlation between sensitivity to traditional chemotherapeutic agents and the TLS-related signature score.

Conclusions

Our findings suggest that the TLS-related features of HNSCC patients hold promise as predictive indicators for immunotherapy efficacy and may offer novel insights for tailoring personalized treatment strategies in clinical practice.

Predicting differentially methylated cytosines in TET and DNMT3 knockout mutants via a large language model

DNA methylation is an epigenetic marker that directly or indirectly regulates several critical cellular processes. While cytosines in mammalian genomes generally maintain stable methylation patterns over time, other cytosines that belong to specific regulatory regions, such as promoters and enhancers, can exhibit dynamic changes. These changes in methylation are driven by a complex cellular machinery, in which the enzymes DNMT3 and TET play key roles. The objective of this study is to design a machine learning model capable of accurately predicting which cytosines have a fluctuating methylation level [hereafter called differentially methylated cytosines (DMCs)] from the surrounding DNA sequence. Here, we introduce L-MAP, a transformer-based large language model that is trained on DNMT3-knockout and TET-knockout data in human and mouse embryonic stem cells. Our extensive experimental results demonstrate the high accuracy of L-MAP in predicting DMCs. Our experiments also explore whether a classifier trained on human knockout data could predict DMCs in the mouse genome (and vice versa), and whether a classifier trained on DNMT3 knockout data could predict DMCs in TET knockouts (and vice versa). L-MAP enables the identification of sequence motifs associated with the enzymatic activity of DNMT3 and TET, which include known motifs but also novel binding sites that could provide new insights into DNA methylation in stem cells. L-MAP is available at https://github.com/ucrbioinfo/dmc_prediction.

Identification of a novel hypermethylation marker, ZSCAN18, and construction of a diagnostic model in cervical cancer

PURPOSE

Cervical cancer (CC), a common female malignancy, has been linked to alterations in DNA methylation. This study employed an integrated "dry-wet lab" strategy combining bioinformatics, machine learning, and experimental validation to identify novel methylation biomarkers for CC.

METHODS

Methylome and transcriptome data from the TCGA and GEO cohorts (n=349 discovery, n=414 validation) were analyzed to identify differentially methylated CpGs. The top candidates were validated by pyrosequencing, methylation-specific PCR, and quantitative assays. Diagnostic models were developed, and functional studies were performed for the target markers.

RESULTS

Eighteen differentially methylated CpGs were identified, with five top candidates (three in the ZSCAN18 promoter) showing diagnostic potential. ZSCAN18 promoter methylation levels and positivity rates were significantly greater in CC tissues than in normal tissues (p<0.05), reaching 77.8% (21/27) in ThinPrep cytology test (TCT) samples. The ridge regression diagnostic model achieved an AUC of 0.9421 in the validation cohort. Similarly, ZSCAN18 overexpression suppressed CC cell proliferation (p<0.05).

CONCLUSIONS

This study established a rapid, effective and systematic systemic research strategy to screen novel methylation markers for CC. ZSCAN18 promoter methylation correlates with cervical lesion severity, and the diagnostic model enhances the diagnostic ability. These findings highlight the dual role of ZSCAN18 as a diagnostic marker and potential therapeutic target.

Adenosinergic Signalling in Cervical Cancer Microenvironment

Despite the emergence of the first human papillomavirus vaccine, the incidence of cervical cancer is still responsible for more than 350,000 deaths yearly. Over the past decade, ecto-5'-nucleotidase (CD73/5'-NT) and extracellular adenosine (ADO) signalling has been the subject of many investigations to target cancer progression. In general, the adenosinergic axis has been linked to tumourigenic effects. However, CD73 can play contradictory effects, probably dependent on the tumour type, tumour microenvironment and tumour stage, thus being in some circumstances, inversely related to tumour progression. We herein reviewed the pathophysiological function of CD73 in cervical cancer and performed in silico analysis of the main components of the adenosinergic signalling in human tissues of cervical cancer compared to non-tumour cervix tissue. Our data showed that the NT5E gene, that encoded CD73, is hypermethylated, leading to a decreased CD73 expression in cervical cancer cells compared to normal cells. Consequently, the high availability of ADO cytoplasmatic/extracellular leads to its conversion to AMP by ADK, culminating in global hypermethylation. Therefore, epigenetic modulation may reveal a new role for CD73 in cervical cancer.

RUNX1 promotes proliferation of cervical cancer through TGFB2-MAPK pathway

As cervical cancer (CC) caused more than 300,000 deaths in the world, it is urgent to identify therapeutic targets to improve survival. Though RUNX1 is overexpressed in CC, its specific role and underlying molecular mechanisms remain incompletely understood. Here we presented that RUNX1 was upregulated in CC and associated with poor prognosis. Functional studies demonstrated that RUNX1 acts as an oncogene in CC, with overexpression accelerating cell cycle progression and promoting cell proliferation. Mechanistically, RUNX1 regulates the MAPK pathway by modulating TGFB2 expression, while TGFB2 inhibition impaired MAPK pathway activation and the proliferation driven by RUNX1 overexpression. Comprehensive analyses also suggested that RUNX1 may modulate the immune microenvironment in CC through TGFB2. These findings indicate that RUNX1 promotes CC progression by activating the MAPK pathway through upregulation of TGFB2. Our study provides new insights into the role of RUNX1 in CC proliferation and suggests RUNX1 as a potential therapeutic target in CC.

Silencing of STEAP3 suppresses cervical cancer cell proliferation and migration via JAK/STAT3 signaling pathway

BACKGROUND

Six-transmembrane epithelial antigen of prostate 3 (STEAP3), an essential constituent of the STEAP family protein, plays a notable role in promoting cancer proliferation and metastasis. Despite the importance of the STEAP gene family in tumor progression, the function of STEAP3 in cervical cancer (CC) remains unclear.

MATERIALS AND METHODS

The expression of STEAP3 protein in CC tissues and cell lines was identified using immunohistochemistry. The Reduced Representation Bisulfite Sequencing (RRBS) was used to detect global gene DNA methylation in CC tissues and paracancerous tissues. Cell viability, proliferation, migration, and invasion, were evaluated using the Cell Counting Kit-8 (CCK8), 5-ethynyl-2'-deoxyuridine (EdU), wound repair assay, and transwell assay, respectively. RNA sequencing was applied to explore STEAP3-related signaling pathways. Western blotting was performed to detect the expression of related proteins, including epithelial-mesenchymal transition (EMT) and Janus kinase/signal transducer and activator of transcription (JAK/STAT) signaling markers.

RESULTS

Herein, STEAP3 was strongly expressed in CC tissues and associated with poor prognosis. CC samples exhibited lower levels of STEAP3 methylation than normal samples, and the methylation levels of CpG islands in STEAP3 were association with prognosis. In contrast to control group, STEAP3 knockdown suppressed the proliferation and invasion of CC cells and enhanced sensitivity to oxaliplatin. Silencing of STEAP3 led to reduced N-cadherin and vimentin levels and increased E-cadherin expression. RNA sequencing analysis suggested that STEAP3 mediated the activation of the JAK STAT3 signaling pathway. Additionally, inhibition of STEAP3 decreased the phosphorylation of JAK2 and STAT3. Interestingly, colivelin (a STAT3 activator) modified STEAP3-induced cell proliferation, invasion, and expression of related proteins in the EMT and JAK/STAT3 signaling pathway.

CONCLUSION

STEAP3 was significantly associated with CC progression mediated via the JAK/STAT3 signaling pathway and may serve as an effective therapeutic target.

Bidirectional recurrent neural network approach for predicting cervical cancer recurrence and survival

Cervical cancer is a deadly disease in women globally. There is a greater chance of getting rid of cervical cancer in case of earliest diagnosis. But for some patients, there is a chance of recurrence. The chances of treating the Recurrence of cervical carcinoma arelimited. The main objective of a research is to find the key features that will predict the cervical cancer recurrence and survival rates accurately by utilizing a neural network that is bidirectionally recurrent. The goal is to reduce risk factors of cervical cancer recurrence by identifying genes with positive coefficients and targeting them for preventive interventions. First step is identification of risk factors for cervical carcinoma recurrence by utilising clinical attributes. This research uses following Random forest, Logistic regression, Gradient boosting and support vector machine algorithms are applied for classification. Random forest offers the maximum precision of these four techniques at 91.2%. The second step is identifying long noncoding RNA (lnRNA) gene signatures among people with cervical carcinomaby implementingHSIC model. Intended to discover biomarkers in initial cervical carcinoma clinical data from people who experienced a distant repetition that could be connected to lnRNA gene signatures and utilized for forecasting survival rates using a bidirectional recurrent neural network(Bi-RNN). The results shows that Bi-RNN model effectively forecast the cervical cancer recurrence and survival.

The current state of DNA methylation biomarkers in self-collected liquid biopsies for the early detection of cervical cancer: a literature review

Cervical cancer (CC) is a preventable disease and treatable cancer. Most of the new cases and deaths from CC occur in Low- and Middle-Income Countries (LMICs) due to cultural and systematic barriers leading to low CC screening uptake. In recent years, self-sampling has been proposed as a method to increase CC screening uptake and is slowly being implemented into screening programmes worldwide. Simultaneously, DNA methylation has been proposed as a novel biomarker that could be used for the triage of self-collected samples that test positive for high-risk types of Human Papillomavirus (HPV). In this paper, we conducted a literature review of studies assessing the efficacy of DNA methylation markers to detect Cervical Intraepithelial Neoplasia (CIN) in self-collected cervicovaginal swabs or urine (2019-2024). Our review showed that, of the available data, DNA methylation together with self-sampling could perform as well as cytology in the detection of CIN as well as improve uptake of CC screening and reduce loss to follow up, especially in LMICs. However, more data is still needed to understand which methylation tests are most efficacious. Future studies should assess the full potential of DNA methylation and self-sampling in large, diverse screening cohorts.

Direction-aware functional class scoring enrichment analysis of infinium DNA methylation data

Infinium Methylation BeadChip arrays remain one of the most popular platforms for epigenome-wide association studies, but tools for downstream pathway analysis have their limitations. Functional class scoring (FCS) is a group of pathway enrichment techniques that involve the ranking of genes and evaluation of their collective regulation in biological systems, but the implementations described for Infinium methylation array data do not retain direction information, which is important for mechanistic understanding of genomic regulation. Here, we evaluate several candidate FCS methods that retain directional information. According to simulation results, the best-performing method involves the mean aggregation of probe limma t-statistics by gene followed by a rank-ANOVA enrichment test using the mitch package. This method, which we call 'LAM,' outperformed an existing over-representation analysis method in simulations, and showed higher sensitivity and robustness in an analysis of real lung tumour-normal paired datasets. Using matched RNA-seq data, we examine the relationship of methylation differences at promoters and gene bodies with RNA expression at the level of pathways in lung cancer. To demonstrate the utility of our approach, we apply it to three other contexts where public data were available. First, we examine the differential pathway methylation associated with chronological age. Second, we investigate pathway methylation differences in infants conceived with in vitro fertilization. Lastly, we analyse differential pathway methylation in 19 disease states, identifying hundreds of novel associations. These results show LAM is a powerful method for the detection of differential pathway methylation complementing existing methods. A reproducible vignette is provided to illustrate how to implement this method.

An integrative analysis to identify pancancer epigenetic biomarkers

Integrating and analyzing the pancancer data collected from different experiments is crucial for gaining insights into the common mechanisms in the molecular level underlying the development and progression of cancers. Epigenetic study of the pancancer data can provide promising results in biomarker discovery. The genes that are epigenetically dysregulated in different cancers are powerful biomarkers for drug-related studies. This paper identifies the genes having altered expression due to aberrant methylation patterns using differential analysis of TCGA pancancer data of 12 different cancers. We identified a comprehensive set of 115 epigenetic biomarker genes out of which 106 genes having pancancer properties. The correlation analysis, gene set enrichment, protein-protein interaction analysis, pancancer characteristics analysis, and diagnostic modeling were performed on these biomarkers to illustrate the power of this signature and found to be important in different molecular operations related to cancer. An accuracy of 97.56% was obtained on TCGA pancancer gene expression dataset for predicting the binary class tumor or normal. The source code and dataset of this work are available at https://github.com/panchamisuneeth/EpiPanCan.git.

Environmental Factor Index (EFI): A Novel Approach to Measure the Strength of Environmental Influence on DNA Methylation in Identical Twins

BACKGROUND/OBJECTIVES

The dynamic interaction between genomic DNA, epigenetic modifications, and phenotypic traits was examined in identical twins. Environmental perturbations can induce epigenetic changes in DNA methylation, influencing gene expression and phenotypes. Although DNA methylation mediates gene-environment correlations, the quantitative effects of external factors on DNA methylation remain underexplored. This study aimed to quantify these effects using a novel approach.

METHODS

A cohort study was conducted on healthy monozygotic twins to evaluate the influence of environmental stimuli on DNA methylation. We developed the Environmental Factor Index (EFI) to identify methylation sites showing statistically significant changes in response to environmental stimuli. We analyzed the identified sites for associations with disorders, DNA methylation markers, and CpG islands.

RESULTS

The EFI identified methylation sites that exhibited significant associations with genes linked to various disorders, particularly cancer. These sites were overrepresented on CpG islands compared to other genomic features, highlighting their regulatory importance.

CONCLUSIONS

The EFI is a valuable tool for understanding the molecular mechanisms underlying disease pathogenesis. It provides insights into the development of preventive and therapeutic strategies and offers a new perspective on the role of environmental factors in epigenetic regulation.

Comprehensive insights into human papillomavirus and cervical cancer: Pathophysiology, screening, and vaccination strategies

This article provides an in-depth review of the Human Papillomavirus (HPV), a predominant etiological factor in cervical cancer, exploring its pathophysiology, epidemiology, and mechanisms of oncogenesis. We examine the role of proteins, DNA methylation markers, and non-coding RNAs as predictive biomarkers in cervical cancer, highlighting their potential in refining diagnostic and prognostic practices. The evolution and efficacy of cervical cancer screening methods, including the Papanicolaou smear, HPV testing, cytology and HPV test, and colposcopy techniques, are critically analyzed. Furthermore, the article delves into the current landscape and future prospects of prophylactic HPV vaccines and therapeutic vaccines, underscoring their significance in the prevention and potential treatment of HPV-related diseases. This comprehensive review aims to synthesize recent advances and ongoing challenges in the field, providing a foundation for future research and clinical strategies in the prevention and management of cervical cancer.

Comprehensive assessment of postoperative recurrence and survival in patients with cervical cancer

BACKGROUND

The prediction of postoperative recurrence and survival in cervical cancer patients has been a major clinical challenge. The combination of clinical parameters, inflammatory markers, intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI), and MRI-derived radiomics is expected to support the prediction of recurrence-free survival (RFS), disease-free survival (DFS), tumor-specific survival (CSS), and overall survival (OS) of cervical cancer patients after surgery.

METHODS

A retrospective analysis of 181 cervical cancer patients with continuous follow-up was completed. The parameters of IVIM-DWI and radiomics were measured, analyzed, and screened. The LASSO regularization was used to calculate the radiomics score (Rad-score). Multivariate Cox regression analysis was used to construct nomogram models for predicting postoperative RFS, DFS, CSS, and OS in cervical cancer patients, with internal and external validation.

RESULTS

Clinical stage, parametrial infiltration, internal irradiation, D-value, and Rad-score were independent prognostic factors for RFS; Squamous cell carcinoma antigen, internal irradiation, D-value, f-value and Rad-score were independent prognostic factors for DFS; Maximum tumor diameter, lymph node metastasis, platelets, D-value and Rad-score were independent prognostic factors for CSS; Lymph node metastasis, systemic inflammation response index, D-value and Rad-score were independent prognostic factors for OS. The AUCs of each model predicting RFS, DFS, CSS, and OS at 1, 3, and 5 years were 0.985, 0.929, 0.910 and 0.833, 0.818, 0.816 and 0.832, 0.863, 0.891 and 0.804, 0.812, 0.870, respectively.

CONCLUSIONS

Nomograms based on clinical and imaging parameters showed high clinical value in predicting postoperative RFS, DFS, CSS, and OS of cervical cancer patients and can be used as prognostic markers.

Predicting Differentially Methylated Cytosines in TET and DNMT3 Knockout Mutants via a Large Language Model

DNA cytosine methylation is an epigenetic marker which regulates many cellular processes. Mammalian genomes typically maintain consistent methylation patterns over time, except in specific regulatory regions like promoters and certain types of enhancers. The dynamics of DNA methylation is controlled by a complex cellular machinery, in which the enzymes DNMT3 and TET play a major role. This study explores the identification of differentially methylated cytosines (DMCs) in TET and DNMT3 knockout mutants in mice and human embryonic stem cells. We investigate (i) whether a large language model can be trained to recognize DMCs in human and mouse from the sequence surrounding the cytosine of interest, (ii) whether a classifier trained on human knockout data can predict DMCs in the mouse genome (and vice versa), (iii) whether a classifier trained on DNMT3 knockout can predict DMCs for TET knockout (and vice versa). Our study identifies statistically significant motifs associated with the prediction of DMCs each mutant, casting a new light on the understanding of DNA methylation dynamics in stem cells. Our software tool is available at https://github.com/ucrbioinfo/dmc_prediction.

Epigenetics in thyroid cancer: a bibliometric analysis

Background

Epigenetics, which involves regulatory modifications that do not alter the DNA sequence itself, is crucial in the development and progression of thyroid cancer. This study aims to provide a comprehensive analysis of the epigenetic research landscape in thyroid cancer, highlighting current trends, major research areas, and potential future directions.

Methods

A bibliometric analysis was performed using data from the Web of Science Core Collection (WOSCC) up to 1 November 2023. Analytical tools such as VOSviewer, CiteSpace, and the R package 'bibliometrix' were employed for comprehensive data analysis and visualization. This process identified principal research themes, along with influential authors, institutions, and countries contributing to the field.

Results

The analysis reveals a marked increase in thyroid cancer epigenetics research over the past two decades. Emergent key themes include the exploration of molecular mechanisms and biomarkers, various subtypes of thyroid cancer, implications for therapeutic interventions, advancements in technologies and methodologies, and the scope of translational research. Research hotspots within these themes highlight intensive areas of study and the potential for significant breakthroughs.

Conclusion

This study presents an in-depth overview of the current state of epigenetics in thyroid cancer research. It underscores the potential of epigenetic strategies as viable therapeutic options and provides valuable insights for researchers and clinicians in advancing the understanding and treatment of this complex disease. Future research is vital to fully leverage the therapeutic possibilities offered by epigenetics in the management of thyroid cancer.

Evaluation of silica spin‑column and magnetic bead formats for rapid DNA methylation analysis in clinical and point‑of‑care settings

Late-stage cancers lack effective treatment, underscoring the need for early diagnosis to improve prognosis and decrease mortality rates. Molecular markers, such as DNA methylation, offer promise in early cancer detection. The present study compared commercial kits for analyzing DNA from cervical liquid cytology samples in cancer screening. Rapid bisulfite conversion kits using silica spin-columns and magnetic beads were assessed against standard DNA extraction and bisulfite conversion methods for profiling DNA methylation using quantitative methylation-specific PCR. β-actin amplification indicated the suitability of small sample volumes for methylation studies using either the pellet or supernatant (cell-free DNA) parts. Comparison of Bisulfite Conversion Kit-Whole Cell (Abcam), Methylamp Bisulfite Modification (Epigentek), EpiTect Fast LyseAll Bisulfite Kit (Qiagen GmbH) and EZ DNA Methylation-Direct Kit (Zymo Research Corp.) showed no significant differences in β-actin cycle threshold values. EZ-96 DNA Methylation-Lightning MagPrep (Zymo Research Corp.), a hybrid kit in a 96-well plate format, exhibited swift turnaround time and similar amplification efficiency. Automation with magnetic bead kits increased throughput without compromising amplification efficiency in open PCR systems. Cost analysis favored direct kits over the gold standard manual protocol. This comparison aids in selecting cost-effective DNA methylation diagnostic tests. The present study confirmed comparable kit performance in methylation-based analysis, highlighting the adequacy of cytology samples and the potential of bodily fluids as alternatives for liquid biopsy.

A Novel Feature Selection Strategy Based on the Harris Hawks Optimization Algorithm for the Diagnosis of Cervical Cancer

Cervical cancer is the fourth most commonly diagnosed cancer and one of the leading causes of cancer-related deaths among females worldwide. Early diagnosis can greatly increase the cure rate for cervical cancer. However, due to the need for substantial medical resources, it is difficult to implement in some areas. With the development of machine learning, utilizing machine learning to automatically diagnose cervical cancer has currently become one of the main research directions in the field. Such an approach typically involves a large number of features. However, a portion of these features is redundant or irrelevant. The task of eliminating redundant or irrelevant features from the entire feature set is known as feature selection (FS). Feature selection methods can roughly be divided into three types, including filter-based methods, wrapper-based methods, and embedded-based methods. Among them, wrapper-based methods are currently the most commonly used approach, and many researchers have demonstrated that these methods can reduce the number of features while improving the accuracy of diagnosis. However, this method still has some issues. Wrapper-based methods typically use heuristic algorithms for FS, which can result in significant computational time. On the other hand, heuristic algorithms are often sensitive to parameters, leading to instability in performance. To overcome this challenge, a novel wrapper-based method named the Binary Harris Hawks Optimization (BHHO) algorithm is proposed in this paper. Compared to other wrapper-based methods, the BHHO has fewer hyper-parameters, which contributes to better stability. Furthermore, we have introduced a rank-based selection mechanism into the algorithm, which endows BHHO with enhanced optimization capabilities and greater generalizability. To comprehensively evaluate the performance of the proposed BHHO, we conducted a series of experiments. The experimental results show that the proposed BHHO demonstrates better accuracy and stability compared to other common wrapper-based FS methods on the cervical cancer dataset. Additionally, even on other disease datasets, the proposed algorithm still provides competitive results, proving its generalizability.

Activation of AMPK inhibits cervical cancer growth by hyperacetylation of H3K9 through PCAF

BACKGROUND

Dysregulation in histone acetylation, a significant epigenetic alteration closely associated with major pathologies including cancer, promotes tumorigenesis, inactivating tumor-suppressor genes and activating oncogenic pathways. AMP-activated protein kinase (AMPK) is a cellular energy sensor that regulates a multitude of biological processes. Although a number of studies have identified the mechanisms by which AMPK regulates cancer growth, the underlying epigenetic mechanisms remain unknown.

METHODS

The impact of metformin, an AMPK activator, on cervical cancer was evaluated through assessments of cell viability, tumor xenograft model, pan-acetylation analysis, and the role of the AMPK-PCAF-H3K9ac signaling pathway. Using label-free quantitative acetylproteomics and chromatin immunoprecipitation-sequencing (ChIP) technology, the activation of AMPK-induced H3K9 acetylation was further investigated.

RESULTS

In this study, we found that metformin, acting as an AMPK agonist, activates AMPK, thereby inhibiting the proliferation of cervical cancer both in vitro and in vivo. Mechanistically, AMPK activation induces H3K9 acetylation at epigenetic level, leading to chromatin remodeling in cervical cancer. This also enhances the binding of H3K9ac to the promoter regions of multiple tumor suppressor genes, thereby promoting their transcriptional activation. Furthermore, the absence of PCAF renders AMPK activation incapable of inducing H3K9 acetylation.

CONCLUSIONS

In conclusion, our findings demonstrate that AMPK mediates the inhibition of cervical cancer growth through PCAF-dependent H3K9 acetylation. This discovery not only facilitates the clinical application of metformin but also underscores the essential role of PCAF in AMPK activation-induced H3K9 hyperacetylation.

Recent Progress in Prompt Molecular Detection of Exosomes Using CRISPR/Cas and Microfluidic-Assisted Approaches Toward Smart Cancer Diagnosis and Analysis

Exosomes are essential indicators of molecular mechanisms involved in interacting with cancer cells and the tumor environment. As nanostructures based on lipids and nucleic acids, exosomes provide a communication pathway for information transfer by transporting biomolecules from the target cell to other cells. Importantly, these extracellular vesicles are released into the bloodstream by the most invasive cells, i. e., cancer cells; in this way, they could be considered a promising specific biomarker for cancer diagnosis. In this matter, CRISPR-Cas systems and microfluidic approaches could be considered practical tools for cancer diagnosis and understanding cancer biology. CRISPR-Cas systems, as a genome editing approach, provide a way to inactivate or even remove a target gene from the cell without affecting intracellular mechanisms. These practical systems provide vital information about the factors involved in cancer development that could lead to more effective cancer treatment. Meanwhile, microfluidic approaches can also significantly benefit cancer research due to their proper sensitivity, high throughput, low material consumption, low cost, and advanced spatial and temporal control. Thereby, employing CRISPR-Cas- and microfluidics-based approaches toward exosome monitoring could be considered a valuable source of information for cancer therapy and diagnosis. This review assesses the recent progress in these promising diagnosis approaches toward accurate cancer therapy and in-depth study of cancer cell behavior.

PAX1 and SEPT9 methylation analyses in cervical exfoliated cells are highly efficient for detecting cervical (pre)cancer in hrHPV-positive women

Studies have investigated PAX1 and SEPT methylation were closely associated with cervical cancer. For this study, we verified the expressions of PAX1 and SEPT9 methylation in 236 hrHPV women cervical exfoliated cells by using quantitative methylation-specific PCR and we further explored their diagnostic value in cervical (pre)cancer detection. Our results identified that the methylation rates and levels of PAX1 and SEPT9 increased with cervical lesion severity. For a diagnosis of cervical (pre)cancer, the area under the curve (AUC) of PAX1 methylation was 0.77 (95% CI 0.71-0.83) and the AUC of SEPT9 methylation was 0.86 (95% CI 0.81∼0.90). Analyses of the PAX1 and SEPT9 methylation statuses alone or combined with commonly used tests can efficiently identify cervical (pre)cancer. In particular, SEPT9 methylation might serve as an effective and powerful biomarker for the diagnosis of cervical (pre)cancer and as an alternative triage test in HPV-based cervical (pre)cancer screening programs.Impact StatementWhat is already known on this subject? This subject showed that PAX1 and SEPT9 methylation were closely associated with cervical cancer. The methylation rates and levels of PAX1 and SEPT9 increased with cervical lesion severity and reached a peak in cervical cancer exfoliated cells. We further assessed the diagnostic performances of PAX1 and SEPT9 methylation in cervical cancer screening. In detecting cervical (pre)cancer, the sensitivity values of PAX1 and SEPT9 methylation were up to 61.18% and 82.35%, respectively, and the specificity values of PAX1 and SEPT9 methylation were up to 95.36% and 86.75%, respectively. Moreover, the ROC curve analysis showed AUC values of 0.77 for PAX1 methylation and 0.86 for SEPT9 methylation tests, which were significantly superior to other commonly used tests. These findings suggest that PAX1 and SEPT9 methylation detection may have great clinical potential in cervical cancer screening.What the results of this study add? The rates and levels of PAX1 and SEPT9 methylation increased with the severity of the cervical lesions. For a diagnosis of cervical (pre)cancer, the area under the curve (AUC) of PAX1 methylation was 0.77 (95% CI 0.71-0.83), and the sensitivity and specificity values were 61.18% and 95.36%, respectively. The AUC value of the SEPT9 methylation was 0.86 (95% CI 0.81 ∼ 0.90), and the sensitivity and specificity values were 82.35% and 86.75%, respectively. Compared with the various tests we conducted, the PAX1 methylation showed the highest specificity (95.36%), and the SEPT9 methylation demonstrated the highest accuracy(86.00%).What the implications are of these findings for clinical practice and/or further research? The methylation levels of PAX1 and SEPT9 had a certain predictive effect on the severity of cervical lesions in hrHPV-positive women. In addition, SEPT9 methylation analysis performs better than PAX1 methylation analysis and commonly used tests in cervical exfoliated cells for detecting cervical (pre)cancer in hrHPV-positive women. SEPT9 methylation analysis merits consideration as an effective and objective, alternative triage test in HPV-based cervical (pre)cancer screening programs.

Artificial intelligence-based risk stratification, accurate diagnosis and treatment prediction in gynecologic oncology

As data-driven science, artificial intelligence (AI) has paved a promising path toward an evolving health system teeming with thrilling opportunities for precision oncology. Notwithstanding the tremendous success of oncological AI in such fields as lung carcinoma, breast tumor and brain malignancy, less attention has been devoted to investigating the influence of AI on gynecologic oncology. Hereby, this review sheds light on the ever-increasing contribution of state-of-the-art AI techniques to the refined risk stratification and whole-course management of patients with gynecologic tumors, in particular, cervical, ovarian and endometrial cancer, centering on information and features extracted from clinical data (electronic health records), cancer imaging including radiological imaging, colposcopic images, cytological and histopathological digital images, and molecular profiling (genomics, transcriptomics, metabolomics and so forth). However, there are still noteworthy challenges beyond performance validation. Thus, this work further describes the limitations and challenges faced in the real-word implementation of AI models, as well as potential solutions to address these issues.

Methylomics and cancer: the current state of methylation profiling and marker development for clinical care

Epigenetic modifications have long been recognized as an essential level in transcriptional regulation linking behavior and environmental conditions or stimuli with biological processes and disease development. Among them, methylation is the most abundant of these reversible epigenetic marks, predominantly occurring on DNA, RNA, and histones. Methylation modification is intimately involved in regulating gene transcription and cell differentiation, while aberrant methylation status has been linked with cancer development in several malignancies. Early detection and precise restoration of dysregulated methylation form the basis for several epigenetics-based therapeutic strategies. In this review, we summarize the current basic understanding of the regulation and mechanisms responsible for methylation modification and cover several cutting-edge research techniques for detecting methylation across the genome and transcriptome. We then explore recent advances in clinical diagnostic applications of methylation markers of various cancers and address the current state and future prospects of methylation modifications in therapies for different diseases, especially comparing pharmacological methylase/demethylase inhibitors with the CRISPRoff/on methylation editing systems. This review thus provides a resource for understanding the emerging role of epigenetic methylation in cancer, the use of methylation-based biomarkers in cancer detection, and novel methylation-targeted drugs.

Cis- and trans-eQTM analysis reveals novel epigenetic and transcriptomic immune markers of atopic asthma in airway epithelium

BACKGROUND

Expression quantitative trait methylation (eQTM) analyses uncover associations between DNA methylation markers and gene expression. Most eQTM analyses of complex diseases have focused on cis-eQTM pairs (within 1 megabase).

OBJECTIVES

This study sought to identify cis- and trans-methylation markers associated with gene expression in airway epithelium from youth with and without atopic asthma.

METHODS

In this study, the investigators conducted both cis- and trans-eQTM analyses in nasal (airway) epithelial samples from 158 Puerto Rican youth with atopic asthma and 100 control subjects without atopy or asthma. The investigators then attempted to replicate their findings in nasal epithelial samples from 2 studies of children, while also examining whether their results in nasal epithelium overlap with those from an eQTM analysis in white blood cells from the Puerto Rican subjects.

RESULTS

This study identified 9,108 cis-eQTM pairs and 2,131,500 trans-eQTM pairs. Trans-associations were significantly enriched for transcription factor and microRNA target genes. Furthermore, significant cytosine-phosphate-guanine sites (CpGs) were differentially methylated in atopic asthma and significant genes were enriched for genes differentially expressed in atopic asthma. In this study, 50.7% to 62.6% of cis- and trans-eQTM pairs identified in Puerto Rican youth were replicated in 2 smaller cohorts at false discovery rate-adjusted P < .1. Replicated genes in the trans-eQTM analysis included biologically plausible asthma-susceptibility genes (eg, HDC, NLRP3, ITGAE, CDH26, and CST1) and are enriched in immune pathways.

CONCLUSIONS

Studying both cis- and trans-epigenetic regulation of airway epithelial gene expression can identify potential causal and regulatory pathways or networks for childhood asthma. Trans-eQTM CpGs may regulate gene expression in airway epithelium through effects on transcription factor and microRNA target genes.

A nomogram based on adipogenesis-related methylation sites in intraoperative visceral fat to predict EWL% at 1 year following laparoscopic sleeve gastrectomy

BACKGROUND

Laparoscopic sleeve gastrectomy (LSG) is a crucial surgical procedure for patients with obesity. However, epigenetic research in LSG is still in its infancy from the perspective of adipogenesis.

OBJECTIVES

This work aims to develop a model to predict 1 year excess weight loss percentage (EWL)% following LSG in Chinese patients with obesity by examining the DNA methylation profiles of intraoperative visceral fat.

SETTING

University hospital, Beijing, China.

METHODS

Firstly, we classified patients with obesity as either the satisfied group or unsatisfied group depending on whether their EWL% was 50% or higher at 1 year following LSG. After that, we analyzed differentially methylated sites (DMSs) between the satisfied group and unsatisfied group. DMSs were mapped to the corresponding differentially methylated genes. Then, we took the intersection of adipogenesis-related genes and differentially methylated genes and obtained adipogenesis-related DMSs. Next, hub methylation sites were identified by least absolute shrinkage and selection operator analysis. Finally, a nomogram was developed to predict EWL% of Chinese patients with obesity at 1 -year following LSG.

RESULTS

A total of 26 patients with obesity were enrolled in the study, including 13 in the satisfied group and 13 in the unsatisfied group. A total of 16 genes and 31 DMSs were involved in the adipogenesis signaling pathway. Finally, 4 hub methylation sites (cg06093355, cg00294552, cg00753924, and cg17092065) were identified and a predictive nomogram was established.

CONCLUSIONS

The predictive nomogram based on methylation sites including cg06093355, cg00294552, cg00753924, and cg17092065 can predict EWL% at 1 year following LSG in Chinese patients with obesity efficiently.

Construction of a prediction model for prognosis of bladder cancer based on the expression of ion channel-related genes

OBJECTIVES

To construct a prediction model for the prognosis of bladder cancer patients based on the expression of ion channel-related genes (ICRGs).

METHODS

ICRGs were obtained from the existing researches. The clinical information and the expression of ICRGs mRNA in breast cancer patients were obtained from the Cancer Genome Atlas database. Cox regression analysis, minimum absolute shrinkage and selection operator regression analysis were used to screen breast cancer prognosis related genes, which were verified by immunohistochemistry and qRT-PCR. The risk scoring equation for predicting the prognosis of patients with bladder cancer was constructed, and the patients were divided into high-risk group and low-risk group according to the median risk score. Immune cell infiltration was compared between the two groups. Kaplan-Meier survival curve and receiver operating characteristic (ROC) curve were used to evaluate the accuracy and clinical application value of the risk scoring equation. The factors related to the prognosis of bladder cancer patients were analyzed by univariate and multivariate Cox regression, and a nomogram for predicting the prognosis of bladder cancer patients was constructed.

RESULTS

By comparing the expression levels of ICRGs in bladder cancer tissues and normal bladder tissues, 73 differentially expressed ICRGs were dentified, of which 11 were related to the prognosis of bladder cancer patients. Kaplan-Meier survival curve suggested that the risk score based on these 11 genes was negatively correlated with the prognosis of patients. The area under the ROC curve of the risk score for predicting the prognosis of patients at 1, 3 and 5 year was 0.634, 0.665 and 0.712, respectively. Stratified analysis showed that the ICRGs-based risk score performed well in predicting the prognosis of patients with American Joint Committee on Cancer (AJCC) stage Ⅲ-Ⅳ bladder cancer (P<0.05), while it had a poor value in predicting the prognosis of patients with AJCC stage Ⅰ-Ⅱ (P>0.05). There were significant differences in the infiltration of plasma cells, activated natural killer cells, resting mast cells and M2 macrophages between the high-risk group and the low-risk group. Cox regression analysis showed that risk score, smoking, age and AJCC stage were independently associated with the prognosis of patients with bladder cancer (P<0.05). The nomogram constructed by combining risk score and clinical parameters has high accuracy in predicting the 1, 3 and 5 year overall survival rate of bladder cancer patients.

CONCLUSIONS

The study shows the potential value of ICRGs in the prognostic risk assessment of bladder cancer patients. The constructed prognostic nomogram based on ICRGs risk score has high accuracy in predicting the prognosis of bladder cancer patients.

Serum CXCL8 and CXCR2 as diagnostic biomarkers for noninvasive screening of cervical cancer

BACKGROUND

Cervical cancer (CC) is the fourth most frequently diagnosed cancer and the fourth leading cause of cancer-related death in women. Identifying new biomarkers for the early detection of CC is an essential requirement in this field. CXCL8 was originally discovered because of its role in inflammation by binding to CXCR1 and CXCR2; however, it is now known to play an important role in cancer. In this study, we aimed to evaluate the expression levels of potential biomarkers (CXCL8, CXCR1, and CXCR2) and to explore their diagnostic potential in CC.

METHODS

The expression levels of serum CXCL8, CXCR1, and CXCR2 were investigated by kit method on Immulite-1000 in 30 healthy volunteers, 30 precancerous patients and 70 CC patients.

RESULTS

The results indicated that the expression of CXCL8 and CXCR2 was significantly higher in the serum of CC patients than in healthy volunteers, similar to the well-established tumor marker (squamous-cell cancerantigen [SCC]). Receiver operating characteristic analyses showed that the combination of CXCL8, CXCR2, and SCC had the highest diagnostic sensitivity and area under the curve value. Meanwhile, the positive predictive value and negative predictive value were not very low. Moreover, high concentrations of CXCL8 and CXCR2 are associated with an increased risk of CC.

CONCLUSIONS

In conclusion, our data demonstrated that combined serum CXCL8, CXCR2, and SCC measurements are helpful for CC diagnosis and can be used as potential biomarkers for the early detection of CC. Cytokines, such as CXCL8 and CXCR2, can be easily measured in most university hospital laboratories and in some private laboratories with a routine test.

Prognostic Values of Core Genes in Pilocytic Astrocytom

BACKGROUND

Pilocytic astrocytoma (PA) is the most common primary brain tumor in children and adolescents. Treatment strategy largely depends on its key genes and molecular mutations. This study aimed to identify potential biomarkers of PA closely related to its prognosis.

METHODS

The gene expression profiles (series numbers GSE50161, GSE66354, and GSE86574) of PA and normal brain tissues were downloaded from the Gene Expression Omnibus database. The Gene Expression Omnibus2R was used to identify differentially expressed genes. The overlapping differentially expressed genes were subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) database. A protein-protein interaction network was constructed using Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape. The Gene Expression Profiling Interactive Analysis 2 (GEPIA2) tool analyzed the impact of hub genes on PA prognosis based on the Kaplan-Meier curves.

RESULTS

Compared with normal brain tissues (n = 36), a total of 37 upregulated and 144 downregulated genes were identified in PA (n = 40). In the protein-protein interaction network construction and GEPIA2 survival analysis, 2 of the top 10 hub genes were significantly associated with decreased overall survival of PA patients, namely Gamma-aminobutyric acid A receptor alpha 2 (hazard ratio  =  2.8, P  <  0.01) and regulating synaptic membrane exocytosis protein 1) (hazard ratio =  3.2, P  <  0.01).

CONCLUSIONS

This bioinformatics analysis reveals that low expression of Gamma-aminobutyric acid A receptor alpha 2 and regulating synaptic membrane exocytosis protein 1 is associated with a favorable prognosis for PA patients. These 2 hub genes could be novel biomarkers for prognosis assessment, furthermore a key element for treatment decisions in the future.

Optimized screening of DNA methylation sites combined with gene expression analysis to identify diagnostic markers of colorectal cancer

BACKGROUND

The prognosis of patients with colorectal cancer is related to early detection. However, commonly used screening markers lack sensitivity and specificity. In this study, we identified diagnostic methylation sites for colorectal cancer.

METHODS

After screening the colorectal cancer methylation dataset, diagnostic sites were identified via survival analysis, difference analysis, and ridge regression dimensionality reduction. The correlation between the selected methylation sites and the estimation of immune cell infiltration was analyzed. The accuracy of the diagnosis was verified using different datasets and the 10-fold crossover method.

RESULTS

According to Gene Ontology, the main enrichment pathways of genes with hypermethylation sites are axon development, axonogenesis, and pattern specification processes. However, the Kyoto Encyclopedia of Genes and Genomes (KEGG) suggests the following main enrichment pathways: neuroactive ligand-receptor interaction, calcium signaling, and cAMP signaling. In The Cancer Genome Atlas (TCGA) and GSE131013 datasets, the area under the curve of cg07628404 was > 0.95. For the NaiveBayes machine model of cg02604524, cg07628404, and cg27364741, the accuracies of 10-fold cross-validation in the GSE131013 and TCGA datasets were 95% and 99.4%, respectively. The survival prognosis of the hypomethylated group (cg02604524, cg07628404, and cg27364741) was better than that of the hypermethylated group. The mutation risk did not differ between the hypermethylated and hypomethylated groups. The correlation coefficient between the three loci and CD4 central memory T cells, hematological stem cells, and other immune cells was not high (p < 0.05).

CONCLUSION

In cases of colorectal cancer, the main enrichment pathway of genes with hypermethylated sites was axon and nerve development. In the biopsy tissues, the hypermethylation sites were diagnostic for colorectal cancer, and the NaiveBayes machine model of the three loci showed good diagnostic performance. Site (cg02604524, cg07628404, and cg27364741) hypermethylation predicts poor survival for colorectal cancer. Three methylation sites were weakly correlated with individual immune cell infiltration. Hypermethylation sites may be a useful repository for diagnosing colorectal cancer.

Construction of the XGBoost model for early lung cancer prediction based on metabolic indices

BACKGROUND

Lung cancer is a malignant tumour, and early diagnosis has been shown to improve the survival rate of lung cancer patients. In this study, we assessed the use of plasma metabolites as biomarkers for lung cancer diagnosis. In this work, we used a novel interdisciplinary mechanism, applied for the first time to lung cancer, to detect biomarkers for early lung cancer diagnosis by combining metabolomics and machine learning approaches.

RESULTS

In total, 478 lung cancer patients and 370 subjects with benign lung nodules were enrolled from a hospital in Dalian, Liaoning Province. We selected 47 serum amino acid and carnitine indicators from targeted metabolomics studies using LC‒MS/MS and age and sex demographic indicators of the subjects. After screening by a stepwise regression algorithm, 16 metrics were included. The XGBoost model in the machine learning algorithm showed superior predictive power (AUC = 0.81, accuracy = 75.29%, sensitivity = 74%), with the metabolic biomarkers ornithine and palmitoylcarnitine being potential biomarkers to screen for lung cancer. The machine learning model XGBoost is proposed as an tool for early lung cancer prediction. This study provides strong support for the feasibility of blood-based screening for metabolites and provide a safer, faster and more accurate tool for early diagnosis of lung cancer.

CONCLUSIONS

This study proposes an interdisciplinary approach combining metabolomics with a machine learning model (XGBoost) to predict early the occurrence of lung cancer. The metabolic biomarkers ornithine and palmitoylcarnitine showed significant power for early lung cancer diagnosis.

Multi-View Clustering for Integration of Gene Expression and Methylation Data With Tensor Decomposition and Self-Representation Learning

The accumulated DNA methylation and gene expression provide a great opportunity to exploit the epigenetic patterns of genes, which is the foundation for revealing the underlying mechanisms of biological systems. Current integrative algorithms are criticized for undesirable performance because they fail to address the heterogeneity of expression and methylation data, and the intrinsic relations among them. To solve this issue, a novel multi-view clustering with self-representation learning and low-rank tensor constraint (MCSL-LTC) is proposed for the integration of gene expression and DNA methylation data, which are treated as complementary views. Specifically, MCSL-LTC first learns the low-dimensional features for each view with the linear projection, and then these features are fused in a unified tensor space with low-rank constraints. In this case, the complementary information of various views is precisely captured, where the heterogeneity of omic data is avoided, thereby enhancing the consistency of different views. Finally, MCSL-LTC obtains a consensus cluster of genes reflecting the structure and features of various views. Experimental results demonstrate that the proposed approach outperforms state-of-the-art baselines in terms of accuracy on both the social and cancer data, which provides an effective and efficient method for the integration of heterogeneous genomic data.

High-resolution Nanopore methylome-maps reveal random hyper-methylation at CpG-poor regions as driver of chemoresistance in leukemias

Aberrant DNA methylation at CpG dinucleotides is a cancer hallmark that is associated with the emergence of resistance to anti cancer treatment, though molecular mechanisms and biological significance remain elusive. Genome scale methylation maps by currently used methods are based on chemical modification of DNA and are best suited for analyses of methylation at CpG rich regions (CpG islands). We report the first high coverage whole-genome map in cancer using the long read nanopore technology, which allows simultaneous DNA-sequence and -methylation analyses on native DNA. We analyzed clonal epigenomic/genomic evolution in Acute Myeloid Leukemias (AMLs) at diagnosis and relapse, after chemotherapy. Long read sequencing coupled to a novel computational method allowed definition of differential methylation at unprecedented resolution, and showed that the relapse methylome is characterized by hypermethylation at both CpG islands and sparse CpGs regions. Most differentially methylated genes, however, were not differentially expressed nor enriched for chemoresistance genes. A small fraction of under-expressed and hyper-methylated genes at sparse CpGs, in the gene body, was significantly enriched in transcription factors (TFs). Remarkably, these few TFs supported large gene-regulatory networks including 50% of all differentially expressed genes in the relapsed AMLs and highly-enriched in chemoresistance genes. Notably, hypermethylated regions at sparse CpGs were poorly conserved in the relapsed AMLs, under-represented at their genomic positions and showed higher methylation entropy, as compared to CpG islands. Analyses of available datasets confirmed TF binding to their target genes and conservation of the same gene-regulatory networks in large patient cohorts. Relapsed AMLs carried few patient specific structural variants and DNA mutations, apparently not involved in drug resistance. Thus, drug resistance in AMLs can be mainly ascribed to the selection of random epigenetic alterations at sparse CpGs of a few transcription factors, which then induce reprogramming of the relapsing phenotype, independently of clonal genomic evolution.

Advances in epigenetic modifications and cervical cancer research

Cervical cancer (CC) is an important public health problem for women, and perspectives and information regarding its prevention and treatment are quickly evolving. Human papilloma virus (HPV) has been recognized as a major contributor to CC development; however, HPV infection is not the only cause of CC. Epigenetics refers to changes in gene expression levels caused by non-gene sequence changes. Growing evidence suggests that the disruption of gene expression patterns which were governed by epigenetic modifications can result in cancer, autoimmune diseases, and various other maladies. This article mainly reviews the current research status of epigenetic modifications in CC based on four aspects, respectively DNA methylation, histone modification, noncoding RNA regulation and chromatin regulation, and we also discuss their functions and molecular mechanisms in the occurrence and progression of CC. This review provides new ideas for early screening, risk assessment, molecular targeted therapy and prognostic prediction of CC.

Identification of Mir-182-3p/FLI-1 Axis as a Key Signaling in Immune Response in Cervical Cancer: A Comprehensive Bioinformatic Analysis

miRNAs modulate gene expression and play critical functions as oncomiRs or tumor suppressors. The miR-182-3p is important in chemoresistance and cancer progression in breast, lung, osteosarcoma, and ovarian cancer. However, the role of miR-182-3p in cervical cancer (CC) has not been elucidated.

AIM

To analyze the role of miR-182-3p in CC through a comprehensive bioinformatic analysis.

METHODS

Gene Expression Omnibus (GEO) databases were used for the expression analysis. The mRNA targets of miR-182-3p were identified using miRDB, TargetScanHuman, and miRPathDB. The prediction of island CpG was performed using the MethPrimer program. The transcription factor binding sites in the FLI-1 promoter were identified using ConSite+, Alibaba2, and ALGGEN-PROMO. The protein-protein interaction (PPI) analysis was performed in STRING 11.5.

RESULTS

miR-182-3p was significantly overexpressed in CC patients and has potential as a diagnostic. We identified 330 targets of miR-182-3p including FLI-1, which downregulates its expression in CC. Additionally, the aberrant methylation of the FLI-1 promoter and Ap2a transcription factor could be involved in downregulating FLI1 expression. Finally, we found that FLI-1 is a possible key gene in the immune response in CC.

CONCLUSIONS

The miR-182-3p/FLI-1 axis plays a critical role in immune response in CC.

Epigenotoxicity: a danger to the future life

Environmental toxicants can regulate gene expression in the absence of DNA mutations via epigenetic mechanisms such as DNA methylation, histone modifications, and non-coding RNAs' (ncRNAs). Here, all three epigenetic modifications for seven important categories of diseases and the impact of eleven main environmental factors on epigenetic modifications were discussed. Epigenetic-related mechanisms are among the factors that could explain the root cause of a wide range of common diseases. Its overall impression on the development of diseases can help us diagnose and treat diseases, and besides, predict transgenerational and intergenerational effects. This comprehensive article attempted to address the relationship between environmental factors and epigenetic modifications that cause diseases in different categories. The studies main gap is that the precise role of environmentally-induced epigenetic alterations in the etiology of the disorders is unknown; thus, still more well-designed researches need to be accomplished to fill this gap. The present review aimed to first summarize the adverse effect of certain chemicals on the epigenome that may involve in the onset of particular disease based on in vitro and in vivo models. Subsequently, the possible adverse epigenetic changes that can lead to many human diseases were discussed.

Blood Inflammatory Biomarkers Differentiate Inpatient and Outpatient Coronavirus Disease 2019 From Influenza

Background

The ongoing circulation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a diagnostic challenge because symptoms of coronavirus disease 2019 (COVID-19) are difficult to distinguish from other respiratory diseases. Our goal was to use statistical analyses and machine learning to identify biomarkers that distinguish patients with COVID-19 from patients with influenza.

Methods

Cytokine levels were analyzed in plasma and serum samples from patients with influenza and COVID-19, which were collected as part of the Centers for Disease Control and Prevention's Hospitalized Adult Influenza Vaccine Effectiveness Network (inpatient network) and the US Flu Vaccine Effectiveness (outpatient network).

Results

We determined that interleukin (IL)-10 family cytokines are significantly different between COVID-19 and influenza patients. The results suggest that the IL-10 family cytokines are a potential diagnostic biomarker to distinguish COVID-19 and influenza infection, especially for inpatients. We also demonstrate that cytokine combinations, consisting of up to 3 cytokines, can distinguish SARS-CoV-2 and influenza infection with high accuracy in both inpatient (area under the receiver operating characteristics curve [AUC] = 0.84) and outpatient (AUC = 0.81) groups, revealing another potential screening tool for SARS-CoV-2 infection.

Conclusions

This study not only reveals prospective screening tools for COVID-19 infections that are independent of polymerase chain reaction testing or clinical condition, but it also emphasizes potential pathways involved in disease pathogenesis that act as potential targets for future mechanistic studies.

Integrative analysis of DNA methylation and gene expression through machine learning identifies stomach cancer diagnostic and prognostic biomarkers

DNA methylation is an early event in tumorigenesis. Here, by integrative analysis of DNA methylation and gene expression and utilizing machine learning approaches, we introduced potential diagnostic and prognostic methylation signatures for stomach cancer. Differentially-methylated positions (DMPs) and differentially-expressed genes (DEGs) were identified using The Cancer Genome Atlas (TCGA) stomach adenocarcinoma (STAD) data. A total of 256 DMPs consisting of 140 and 116 hyper- and hypomethylated positions were identified between 443 tumour and 27 nontumour STAD samples. Gene expression analysis revealed a total of 2821 DEGs with 1247 upregulated and 1574 downregulated genes. By analysing the impact of cis and trans regulation of methylation on gene expression, a dominant negative correlation between methylation and expression was observed, while for trans regulation, in hypermethylated and hypomethylated genes, there was mainly a negative and positive correlation with gene expression, respectively. To find diagnostic biomarkers, we used 28 hypermethylated probes locating in the promoter of 27 downregulated genes. By implementing a feature selection approach, eight probes were selected and then used to build a support vector machine diagnostic model, which had an area under the curve of 0.99 and 0.97 in the training and validation (GSE30601 with 203 tumour and 94 nontumour samples) cohorts, respectively. Using 412 TCGA-STAD samples with both methylation and clinical data, we also identified four prognostic probes by implementing univariate and multivariate Cox regression analysis. In summary, our study introduced potential diagnostic and prognostic biomarkers for STAD, which demands further validation.

Non-Association of Driver Alterations in PTEN with Differential Gene Expression and Gene Methylation in IDH1 Wildtype Glioblastomas

During oncogenesis, alterations in driver genes called driver alterations (DAs) modulate the transcriptome, methylome and proteome through oncogenic signaling pathways. These modulatory effects of any DA may be analyzed by examining differentially expressed mRNAs (DEMs), differentially methylated genes (DMGs) and differentially expressed proteins (DEPs) between tumor samples with and without that DA. We aimed to analyze these modulations with 12 common driver genes in Isocitrate Dehydrogenase 1 wildtype glioblastomas (IDH1-W-GBs). Using Cbioportal, groups of tumor samples with and without DAs in these 12 genes were generated from the IDH1-W-GBs available from "The Cancer Genomics Atlas Firehose Legacy Study Group" (TCGA-FL-SG) on Glioblastomas (GBs). For all 12 genes, samples with and without DAs were compared for DEMs, DMGs and DEPs. We found that DAs in PTEN were unassociated with any DEM or DMG in contrast to DAs in all other drivers, which were associated with several DEMs and DMGs. This contrasting PTEN-related property of being unassociated with differential gene expression or methylation in IDH1-W-GBs was unaffected by concurrent DAs in other common drivers or by the types of DAs affecting PTEN. From the lists of DEMs and DMGs associated with some common drivers other than PTEN, enriched gene ontology terms and insights into the co-regulatory effects of these drivers on the transcriptome were obtained. The findings from this study can improve our understanding of the molecular mechanisms underlying gliomagenesis with potential therapeutic benefits.

Identification of potential biomarkers for systemic lupus erythematosus by integrated analysis of gene expression and methylation data

INTRODUCTION

Systemic lupus erythematosus (SLE) is a heterogeneous and chronic autoimmune disease. Aberrant DNA methylation occurs during various processes of SLE development regulating the mRNA expression of interrelated genes. This study aims to screen potential DNA methylation markers for SLE.

METHODS

Gene expression and methylation datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between SLE patients and healthy controls were screened using the limma R package, and differentially methylated positions (DMPs) and regions (DMRs) were identified using dmpfinder and bumphunter (minfi). Additionally, the DNA methylation markers to distinguish SLE patients from healthy controls were explored through receiver operating characteristic (ROC) curves and logistic regression analyses. Finally, we validated the results of the bioinformatic analysis by pyrosequencing.

RESULTS

In total, 91 DEGs, 90,092 DMPs, 15 DMRs, and 13 DMR-associated genes were identified. Through the integrative analysis of DEG- and DMR-associated genes, we identified five type I interferon (IFN)-related genes as key epigenetic-driven genes in SLE. GO enrichment analysis showed that the five SLE-associated epigenetic-driven genes were mainly enriched in the type I IFN signaling pathway involved in immune response and defense response to virus. Moreover, we identified two SLE-specific DNA methylation markers, three SLE without lupus nephritis (SLE-LN^-)-specific DNA methylation markers, and two SLE with lupus nephritis (SLE-LN⁺)-specific DNA methylation markers by stepwise logistic regression.

CONCLUSIONS

Overall, our study demonstrates potential DNA methylation markers of SLE, SLE-LN^-, and SLE-LN⁺, which may help the diagnosis, boost the development of new epigenetic therapy, and contribute to individualized treatment. Key Points • This study identified five type I IFN-related genes as key epigenetic-driven genes in SLE, which support the importance of the type I IFN pathway in the pathogenesis of SLE • We identified novel DNA methylation biomarkers in SLE, SLE-LN-, and SLE-LN+ by a comprehensive analysis of bioinformatics methods and executed experimental validation, and binary logistic regression analysis showed that they have excellent potential • These results may provide new insights into the biological mechanisms of SLE, and identify reliable biomarkers for SLE, SLE-LN-, and SLE-LN+, which may contribute to diagnosis and individualized treatment.

Effects of paternal methionine supplementation on sperm DNA methylation and embryo transcriptome in sheep

Environmental effects on gene expression and offspring development can be mediated by epigenetic modifications. It is well established that maternal diet influences DNA methylation patterns and phenotypes in the offspring; however, the epigenetic effects of paternal diet on developing offspring warrants further investigation. Here, we examined how a prepubertal methionine-enriched paternal diet affected sperm DNA methylation and its subsequent effects on embryo gene expression. Three treatment and three control rams were bred to seven ewes, and blastocysts were flushed for RNA extraction. Semen was collected from all rams and submitted for reduced representation bisulfite sequencing analysis. In total, 166 differentially methylated cytosines were identified in the sperm from treatment versus control rams. Nine genes were found to be differentially expressed in embryos produced from treatment versus control rams, and seven differentially methylated cytosines in the sperm were found to be highly correlated with gene expression in the embryos. Our results demonstrate that sperm methylation differences induced by diet may influence fetal programming.

A 5‐year survival status prognosis of nonmetastatic cervical cancer patients through machine learning algorithms

BACKGROUND

Prediction models with high accuracy rates for nonmetastatic cervical cancer (CC) patients are limited. This study aimed to construct and compare predictive models on the basis of machine learning (ML) algorithms for predicting the 5-year survival status of CC patients through using the Surveillance, Epidemiology, and End Results public database of the National Cancer Institute.

METHODS

The data registered from 2004 to 2016 were extracted and randomly divided into training and validation cohorts (8:2). The least absolute shrinkage and selection operator (LASSO) regression was employed to identify significant factors. Then, four predictive models were constructed, including logistic regression (LR), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost). The predictive models were evaluated and compared using Receiver-operating characteristics with areas under the curves (AUCs) and decision curve analysis (DCA), respectively.

RESULTS

A total of 13,802 patients were involved and classified into training (N = 11,041) and validation (N = 2761) cohorts. By using the LASSO regression method, seven factors were identified. In the training cohort, the XGBoost model showed the best performance (AUC = 0.8400) compared to the other three models (all p < 0.05 by Delong's test). In the validation cohort, the XGBoost model also demonstrated a superior prediction ability (AUC = 0.8365) than LR and SVM models (both p < 0.05 by Delong's test), although the difference was not statistically significant between the XGBoost and the RF models (p = 0.4251 by Delong's test). Based on the DCA results, the XGBoost model was also superior, and feature importance analysis indicated that the tumor stage was the most important variable among the seven factors.

CONCLUSIONS

The XGBoost model proved to be an effective algorithm with better prediction abilities. This model is proposed to support better decision-making for nonmetastatic CC patients in the future.

Human Papillomavirus: Challenges and Opportunities for the Control of Cervical Cancer

Viruses are the most abundant and genetically diverse entities on the planet, infect all life forms and have evolved with their hosts. To date, 263 viral species have been identified that infect humans, of which only seven are considered type I oncogenic. Human papillomavirus (HPV) is the main virus associated with cancer and is responsible for practically all cases of cervical carcinoma. Screening tests for early detection have been available since the 1960s. Undoubtedly, the entailment between knowledge of HPV biology and the natural history of cervical cancer has contributed to the significant advances that have been made for its prevention since the 21^st century, with the development of prophylactic vaccines and improved screening strategies. Therefore, it is possible to eradicate invasive cervical cancer as a worldwide public health problem, as proposed by the WHO with the 90-70-90 initiative based on vaccination coverage, screening, and treatment, respectively. In addition, the emerging knowledge of viral biology generates opportunities that will contribute to strengthening prevention and treatment strategies in HPV-associated neoplasms.

Consequences of aberrated DNA methylation in Colon Adenocarcinoma: a bioinformatic-based multi-approach

INTRODUCTION

The biology of colorectal cancer (CRC) is remained to be elucidated. Numerous genetic and epigenetic modifications are in concert to create and progress CRC. DNA methylation as a principal epigenetic factor has gained increased attention and could be utilized for biological studies. This study aims to find novel methylated and downregulated genes with a focus on HAND2 in CRC and decipher the biological consequences.

MATERIAL AND METHOD

Data on DNA methylation from GEO and SMART databases and the expression GEPIA2 database were downloaded. Afterward, a set of hypermethylated and downregulated genes in CRC was chosen by overlapping genes. Consequently, HAND2 was selected as a key gene for further investigation and confirmed with cell lines methylation and expression data. The functions of HAND2 were further analyzed using gene ontology analyses and the protein-protein interaction network.

RESULTS

The methylation (p < 0.01) and expression (p < 0.01) of HAND2 are significantly varied in CRC compared to normal control. The correlation analysis (Pearson's correlation coefficient = -0.44, p = 6.6e-14) conveys that HAND2 significantly downregulated and has a reverse correlation with the methylation status of CpG islands. The biological process analysis of HAND2 target genes conveyed that disruption in HAND2 expression could dysregulate ERK1 and ERK2 signaling pathways.

CONCLUSION

Together, the findings showed that DNA hypermethylation of HAND2 was critical evidence in CRC. Further validation and prospective studies are needed to utilize HAND2 methylation as a promising biomarker.

Methylation-Mediated Silencing of RBP7 Promotes Breast Cancer Progression through PPAR and PI3K/AKT Pathway

Retinoid-binding protein7 (RBP7) is a member of the cellular retinol-binding protein (CRBP) family, which is involved in the pathogenesis of breast cancer. The study aims to illustrate the prognostic value and the potential regulatory mechanisms of RBP7 expression in breast cancer. Bioinformatics analysis with the TCGA and CPTAC databases revealed that the mRNA and protein expression levels of RBP7 in normal were higher compared to breast cancer tissues. Survival analysis displayed that the lower expression of RBP7, the worse the prognosis in ER-positive (ER+) breast cancer patients. Genomic analysis showed that low expression of RBP7 correlates with its promoter hypermethylation in breast cancer. Functional enrichment analysis demonstrated that downregulation of RBP7 expression may exert its biological influence on breast cancer through the PPAR pathway and the PI3K/AKT pathway. In summary, we identified RBP7 as a novel biomarker that is helpful for the prognosis of ER+ breast cancer patients. Promoter methylation of RBP7 is involved in its gene silencing in breast cancer, thus regulating the occurrence and development of ER+ breast cancer through the PPAR and PI3K/AKT pathways.

Genomic Effect of DNA Methylation on Gene Expression in Colorectal Cancer

The aberrant expression of cancer-related genes can lead to colorectal cancer (CRC) carcinogenesis, and DNA methylation is one of the causes of abnormal expression. Although many studies have been conducted to reveal how DNA methylation affects transcription regulation, the ways in which it modulates gene expression and the regions that significantly affect DNA methylation-mediated gene regulation remain unclear. In this study, we investigated how DNA methylation in specific genomic areas can influence gene expression. Several regression models were constructed for gene expression prediction based on DNA methylation. Among these models, ElasticNet, which had the best performance, was chosen for further analysis. DNA methylation near transcription start sites (TSS), especially from 2 kb upstream to 7 kb downstream of TSS, had an essential regulatory role in gene expression. Moreover, methylation-affected and survival-associated genes were compiled and found to be mainly enriched in immune-related pathways. This study investigated genomic regions in which methylation changes can affect gene expression. In addition, this study proposed that aberrantly expressed genes due to DNA methylation can lead to CRC pathogenesis by the immune system.

The crossroads of adenosinergic pathway and epithelial-mesenchymal plasticity in cancer

Epithelial-mesenchymal transition (EMT) is a key mechanism related to tumor progression, invasion, metastasis, resistance to therapy and poor prognosis in several types of cancer. However, targeting EMT or partial-EMT, as well as the molecules involved in this process, has remained a challenge. Recently, the CD73 enzyme, which hydrolyzes AMP to produce adenosine (ADO), has been linked to the EMT process. This relationship is not only due to the production of the immunosuppressant ADO but also to its role as a receptor for extracellular matrix proteins, being involved in cell adhesion and migration. This article reviews the crosstalk between the adenosinergic pathway and the EMT program and the impact of this interrelation on cancer development and progression. An in silico analysis of RNAseq datasets showed that several tumor types have a significant correlation between an EMT score and NT5E (CD73) and ENTPD1 (CD39) expressions, with the strongest correlations in prostate adenocarcinoma. Furthermore, it is evident that the cooperation between EMT and adenosinergic pathway in tumor progression is context and tumor-dependent. The increased knowledge about this topic will help broaden the view to explore new treatments and therapies for different types of cancer.

Epigenetic and Transcriptomic Regulation Landscape in HPV+ Cancers: Biological and Clinical Implications

Human Papillomavirus (HPV) is an oncogenic virus that causes the highest number of viral-associated cancer cases and deaths worldwide, with more than 690,000 new cases per year and 342,000 deaths only for cervical cancer (CC). Although the incidence and mortality rates for CC are declining in countries where screening and vaccination programs have been implemented, other types of cancer in which HPV is involved, such as oropharyngeal cancer, are increasing, particularly in men. Mutational and transcriptional profiles of various HPV-associated neoplasms have been described, and accumulated evidence has shown the oncogenic capacity of E6, E7, and E5 genes of high-risk HPV. Interestingly, transcriptomic analysis has revealed that although a vast majority of the human genome is transcribed into RNAs, only 2% of transcripts are translated into proteins. The remaining transcripts lacking protein-coding potential are called non-coding RNAs. In addition to the transfer and ribosomal RNAs, there are regulatory non-coding RNAs classified according to size and structure in long non-coding RNAs (lncRNAs), circular RNAs (circRNAs), and small RNAs; such as microRNAs (miRNAs), piwi-associated RNAs (piRNAs), small nucleolar RNAs (snoRNAs) and endogenous short-interfering RNAs. Recent evidence has shown that lncRNAs, miRNAs, and circRNAs are aberrantly expressed under pathological conditions such as cancer. In addition, those transcripts are dysregulated in HPV-related neoplasms, and their expression correlates with tumor progression, metastasis, poor prognosis, and recurrence. Nuclear lncRNAs are epigenetic regulators involved in controlling gene expression at the transcriptional level through chromatin modification and remodeling. Moreover, disruption of the expression profiles of those lncRNAs affects multiple biological processes such as cell proliferation, apoptosis, and migration. This review highlights the epigenetic alterations induced by HPV, from infection to neoplastic transformation. We condense the epigenetic role of non-coding RNA alterations and their potential as biomarkers in transformation's early stages and clinical applications. We also summarize the molecular mechanisms of action of nuclear lncRNAs to understand better their role in the epigenetic control of gene expression and how they can drive the malignant phenotype of HPV-related neoplasia. Finally, we review several chemical and epigenetic therapy options to prevent and treat HPV-associated neoplasms.

Review of the Standard and Advanced Screening, Staging Systems and Treatment Modalities for Cervical Cancer

Simple Summary

This review discusses the timeline and development of the recommended screening tests, diagnosis system, and therapeutics implemented in clinics for precancer and cancer of the uterine cervix. The incorporation of the latest automation, machine learning modules, and state-of-the-art technologies into these aspects are also discussed.

Abstract

Cancer arising from the uterine cervix is the fourth most common cause of cancer death among women worldwide. Almost 90% of cervical cancer mortality has occurred in low- and middle-income countries. One of the major aetiologies contributing to cervical cancer is the persistent infection by the cancer-causing types of the human papillomavirus. The disease is preventable if the premalignant lesion is detected early and managed effectively. In this review, we outlined the standard guidelines that have been introduced and implemented worldwide for decades, including the cytology, the HPV detection and genotyping, and the immunostaining of surrogate markers. In addition, the staging system used to classify the premalignancy and malignancy of the uterine cervix, as well as the safety and efficacy of the various treatment modalities in clinical trials for cervical cancers, are also discussed. In this millennial world, the advancements in computer-aided technology, including robotic modules and artificial intelligence (AI), are also incorporated into the screening, diagnostic, and treatment platforms. These innovations reduce the dependence on specialists and technologists, as well as the work burden and time incurred for sample processing. However, concerns over the practicality of these advancements remain, due to the high cost, lack of flexibility, and the judgment of a trained professional that is currently not replaceable by a machine.

meGPS: a multi-omics signature for hepatocellular carcinoma detection integrating methylome and transcriptome data

MOTIVATION

Hepatocellular carcinoma (HCC) is a primary malignancy with poor prognosis. Recently, multi-omics molecular-level measurement enables HCC diagnosis and prognosis prediction, which is crucial for early intervention of personalized therapy to diminish mortality. Here, we introduce a novel strategy utilizing DNA methylation and RNA expression data to achieve a multi-omics gene pair signature (GPS) for HCC discrimination.

RESULTS

The immune genes with negative correlations between expression and promoter methylation are enriched in the highly connected cancer-related pathway network, which are considered as the candidates for HCC detection. After that, we separately construct a methylation GPS (mGPS) and an expression GPS (eGPS), and then assemble them as a meGPS with five gene pairs, in which the significant methylation and expression changes occur between HCC tumor and non-tumor groups. Reliable performance has been validated by independent tissue (age, gender, and etiology) and blood datasets. This study proposes a procedure for multi-omics GPS identification and develops a novel HCC signature using both methylome and transcriptome data, suggesting potential molecular targets for the detection and therapy of HCC.

AVAILABILITY AND IMPLEMENTATION

Models are available at https://github.com/bioinformaticStudy/meGPS.git.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

ERBB3 methylation and immune infiltration in tumor microenvironment of cervical cancer

ERBB3, a member of the ERBB family of receptor tyrosine kinases, plays an important role in cancer, despite its lack of intrinsic carcinogenic mechanism of cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC). Research on bioinformatics methods through multi-omics, this work proves that ERBB3 gene mutation, methylation modification have extensive regulatory mechanisms on the CESC microenvironment. We found that ERBB3 is involved in carcinogenesis of cervical cancer and is not associated with its prognosis. The carcinogenic mechanism is mainly related to the suppression of the immune system between tumor infiltrating lymphocytes (TILs) and the methylation of the RNA level. Our study indicated ERBB3 is more likely to be a carcinogenic factor than a key prognostic factor for cervical cancer. Methylation of ERBB3 may work as a checkpoint immunotherapy target in CESC, DNA methylation modification of the 4480 base pair downstream of ERBB3 transcription initiation site was the highest.