Eight-lncRNA signature of cervical cancer were identified by integrating DNA methylation, copy number variation and transcriptome data

Human papilloma virus (HPV) mediated cancers: an insightful update

Human papillomavirus (HPV), a DNA virus, is a well-documented causative agent of several cancers, including cervical, vulvar, vaginal, penile, anal, and head & neck cancers. Major factors contributing to HPV-related cancers include persistent infection and the oncogenic potential of particular HPV genotypes. High-risk HPV strains, particularly HPV-16 and HPV-18, are responsible for over 70% of cervical cancer cases worldwide, as well as a significant proportion of other genital and head and neck cancers. At the molecular level, the oncogenic activity of these viruses is driven by the overexpression of E6 and E7 oncoproteins. These oncoproteins dysregulate the cell cycle, inhibit apoptosis, and promote the accumulation of DNA damage, ultimately transforming normal cells into cancerous ones. This review aims to provide a comprehensive overview of the recent advances in HPV-related cancer biology and epidemiology. The review highlights the molecular pathways of HPV-driven carcinogenesis, focusing on the role of viral oncoproteins in altering host cell targets and disrupting cellular signalling pathways. The review explores the therapeutic potential of these viral proteins, and discusses current diagnostic and treatment strategies for HPV-associated cancers. Furthermore, the review highlights the critical role of HPV in the development of various malignancies, emphasizing the persistent challenges in combating these cancers despite advancements in vaccination and therapeutic strategies. We also emphasize recent breakthroughs in utilizing biomarkers to monitor cancer therapy responses, such as mRNAs, miRNAs, lncRNAs, proteins, and genetic markers. We hope this review will serve as a valuable resource for researchers working on HPV, providing insights that can guide future investigations into this complex virus, which continues to be a major contributor to global morbidity and mortality.

Construction of a Prognostic Model for Cervical Cancer Related to lncRNA Based on Differential Co-expression Network and Functional Study of Key Gene EGFR-AS1

Cervical cancer is a common gynecological malignancy, and the average age of onset is decreasing gradually. Therefore, an effective predictive model is urgently needed to improve the personalized treatment of cervical cancer patients. Long non-coding RNAs (lncRNAs) play crucial roles in the occurrence, development, and prognosis of malignant tumors. In this study, we used cervical cancer multi-omics data and single-cell sequencing data for analysis, and established a 33-lncRNA-CESC model by using the random forest algorithm in ensemble learning and mRNA and lncRNA co-expression network technology. The results demonstrated that the model exhibited strong discriminative ability, accuracy, and clinical utility. Furthermore, we investigated the relationship between the model and immune cell infiltration. Enrichment analysis revealed associations between the model and cellular proliferation as well as epidermal growth factor receptor (EGFR) signaling pathways. Subsequently, attention was directed toward the gene EGFR-AS1 in the model, which was identified within the co-expression network and exhibited a significant association with patient prognosis. Additionally, EGFR-AS1 was found to be specifically associated with FAM83B. Analysis of single-cell data confirmed that FAM83B plays a role in the late stage of cervical cancer development mainly through the EGFR signaling pathway. Functional experiments showed that knockdown of either EGFR-AS1 or FAM83B inhibited cervical cancer cell proliferation and migration capabilities, and the phosphorylated ERK and AKT levels. In addition, there was a mutual regulatory effect between EGFR-AS1 and FAM83B expression. In conclusion, this study identifies that EGFR-AS1 served as a key factor in our 33-lncRNA-CESC model and potentially interacted with FAM83B to regulate the EGFR pathway which significantly impacting cervical cancer development.

The relationship between long non-coding gene CASC21 polymorphisms and cervical cancer

BACKGROUND

CASC21 was reported to be a hotspot gene in cervical cancer. The relationship between CASC21 genetic polymorphisms and cervical cancer has not been reported. Genetic factors influence the occurrence of cervical cancer. Thus, we explored the correlation between CASC21 polymorphisms and cervical cancer.

METHODS

A total of 973 participants within 494 cervical cancer cases and 479 healthy controls were recruited. Five single nucleotide polymorphisms (SNPs) in the CASC21 gene were genotyped using the Agena MassARRAY platform. Chi-squared test, logistic regression analysis, odds ratio (OR), multifactor dimensionality reduction (MDR), and 95% confidence interval (95%CI) were used for data analysis.

RESULTS

In the overall analysis, rs16902094 (p = .014, OR = 1.86, 95% CI = 1.12-3.08) and rs16902104 (p = .014, OR = 1.86, 95% CI = 1.12-3.09) had the risk-increasing correlation with the occurrence of cervical cancer. Stratification analysis showed that rs16902094 and rs16902104 were still associated with cervical cancer risk in the subgroups with age > 51, BMI < 24 kg/m2, smokers, and patients with cervical squamous cell carcinoma. MDR analysis displayed that rs16902094 (.49%) and rs16902104 (.52%) were the main influential attribution factor for cervical cancer risk.

CONCLUSION

Our finding firstly determined that two CASC21 SNPs (rs16902094, rs16902104) were associated with an increased risk of cervical cancer, which adds to our knowledge regarding the effect of CASC21 on cervical carcinogenesis.

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.

Single-Cell Transcriptome Sequencing and Analysis Provide a New Approach for the Treatment of Small Cell Neuroendocrine Carcinoma of the Cervix

INTRODUCTION

Small cell neuroendocrine carcinoma of the cervix (SCNECC) is a rare gynecologic malignant tumor, which has lack of systematic research. In order to investigate its molecular characteristics, origin, and pathogenesis, single-cell transcriptome sequencing (scRNA-Seq) of SCNECC was performed for the first time, the cellular and molecular landscape was revealed, and the key genes for clinical prognosis were screened.

METHODS

This article initially performed the scRNA-Seq on a tumor tissue sample from an SCNECC patient, combined with scRNA-Seq data from a healthy cervical tissue sample downloaded from a public database; the single-cell transcriptome landscape was constructed. Then, we investigated the cell types, intratumoral heterogeneity, characteristics of tumor microenvironment, and potential predictive markers of SCNECC.

RESULTS

We identified two malignant cell populations, tumor stem cells and malignant carcinoma cells, and revealed two tumor progression pathways of SCNECC. By analyzing gene expression levels in the pathophysiology of SCNECC, we found that the expression levels of ERBB4 and NRG1, as well as the expression profile of mTOR signaling pathway mediated by them, were significantly upregulated in malignant carcinoma cells. In addition, we also found that carcinoma cells were able to stimulate malignant cell proliferation through the FN1 signaling pathway. The immune cells were in a stress state, with T-cell depletion, macrophage polarization, and mast cell glycolysis. These results suggested that carcinoma cells could interfere with immune response and promote tumor escape through MIF, TGFb, and other immunosuppressive-related signaling pathways.

CONCLUSION

This study revealed the mechanism of genesis and progression in SCNECC and the related important signaling pathways, such as mTOR, and provided new insights into the treatment of SCNECC.

FunlncModel: integrating multi-omic features from upstream and downstream regulatory networks into a machine learning framework to identify functional lncRNAs

Accumulating evidence indicates that long noncoding RNAs (lncRNAs) play important roles in molecular and cellular biology. Although many algorithms have been developed to reveal their associations with complex diseases by using downstream targets, the upstream (epi)genetic regulatory information has not been sufficiently leveraged to predict the function of lncRNAs in various biological processes. Therefore, we present FunlncModel, a machine learning-based interpretable computational framework, which aims to screen out functional lncRNAs by integrating a large number of (epi)genetic features and functional genomic features from their upstream/downstream multi-omic regulatory networks. We adopted the random forest method to mine nearly 60 features in three categories from >2000 datasets across 11 data types, including transcription factors (TFs), histone modifications, typical enhancers, super-enhancers, methylation sites, and mRNAs. FunlncModel outperformed alternative methods for classification performance in human embryonic stem cell (hESC) (0.95 Area Under Curve (AUROC) and 0.97 Area Under the Precision-Recall Curve (AUPRC)). It could not only infer the most known lncRNAs that influence the states of stem cells, but also discover novel high-confidence functional lncRNAs. We extensively validated FunlncModel's efficacy by up to 27 cancer-related functional prediction tasks, which involved multiple cancer cell growth processes and cancer hallmarks. Meanwhile, we have also found that (epi)genetic regulatory features, such as TFs and histone modifications, serve as strong predictors for revealing the function of lncRNAs. Overall, FunlncModel is a strong and stable prediction model for identifying functional lncRNAs in specific cellular contexts. FunlncModel is available as a web server at https://bio.liclab.net/FunlncModel/.

Epitranscriptomics and cervical cancer: the emerging role of m6A, m5C and m1A RNA modifications

Cervical cancer (CC), one of the most prevalent and detrimental gynaecologic cancers, evolves through genetic and epigenetic alterations resulting in the promotion of oncogenic activity and dysfunction of tumour-suppressing mechanisms. Despite medical advancement, the prognosis for advanced-stage patients remains extremely low due to high recurrence rates and resistance to existing treatments. Thereby, the search for potential prognostic biomarkers is heightened to unravel new modalities of CC pathogenesis and to develop novel anti-cancer therapies. Epitranscriptomic modifications, reversible epigenetic RNA modifications, regulate various biological processes by deciding RNA fate to mediating RNA interactions. This narrative review provides insight into the cellular and molecular roles of endogenous RNA-editing proteins and their associated epitranscriptomic modifications, especially N6-methyladenosine (m6A), 5-methylcytosine (m5C) and N1-methyladenosine (m1A), in governing the development, progression and metastasis of CC. We discussed the in-depth epitranscriptomic mechanisms underlying the regulation of over 50 RNAs responsible for tumorigenesis, proliferation, migration, invasion, survival, autophagy, stemness, epithelial-mesenchymal transition, metabolism (glucose, lipid, glutamate and glutamine), resistance (drug and radiation), angiogenesis and recurrence of CC. Additionally, we provided a concise overview of the therapeutic potential of targeting the altered expression of endogenous RNA-editing proteins and aberrant deposition of RNA modifications on both coding and non-coding RNAs in CC.

Multi-omics approach for identifying CNV-associated lncRNA signatures with prognostic value in prostate cancer

BACKGROUND

Prostate cancer, the second most prevalent malignancy among men, poses a significant threat to affected patients' well-being due to its poor prognosis. Novel biomarkers are required to enhance clinical outcomes and tailor personalized treatments. Herein, we describe our research to explore the prognostic value of long non-coding RNAs (lncRNAs) deregulated by copy number variations (CNVs) in prostate cancer.

METHODS

The study employed an integrative multi-omics data analysis of the prostate cancer transcriptomic, CNV and methylation datasets to identify prognosis-related subtypes. Subtype-specific expression profiles of protein-coding genes (PCGs) and lncRNAs were determined. We analysed CNV patterns of lncRNAs across the genome to identify subtype-specific lncRNAs with CNV changes. LncRNAs exhibiting significant amplification or deletion and a positive correlation were designated CNV-deregulated lncRNAs. A prognostic risk score model was subsequently developed using these CNV-driven lncRNAs.

RESULTS

Six molecular subtypes of prostate cancer were identified, demonstrating significant differences in prognosis (P = 0.034). The CNV profiles of subtype-specific lncRNAs were examined, revealing their correlation with CNV amplification or deletion. Six lncRNAs (CCAT2, LINC01593, LINC00276, GACAT2, LINC00457, LINC01343) were selected based on significant CNV amplifications or deletions using a rigorous univariate Cox proportional risk regression model. A robust risk score model was developed, stratifying patients into high-risk and low-risk categories. Notably, our prognostic model based on these six lncRNAs exhibited exceptional predictive capabilities for recurrence-free survival (RFS) in prostate cancer patients (P = 0.024).

CONCLUSIONS

Our study successfully identified a prognostic risk score model comprising six CNV-driven lncRNAs that could potentially be prognostic biomarkers for prostate cancer. These lncRNA signatures are closely associated with RFS, providing promising prospects for improved patient prognostication and personalized therapeutic strategies for novel prostate cancer treatment.

Multi-Omics Mining of lncRNAs with Biological and Clinical Relevance in Cancer

In this review, we provide a general overview of the current panorama of mining strategies for multi-omics data to investigate lncRNAs with an actual or potential role as biological markers in cancer. Several multi-omics studies focusing on lncRNAs have been performed in the past with varying scopes. Nevertheless, many questions remain regarding the pragmatic application of different molecular technologies and bioinformatics algorithms for mining multi-omics data. Here, we attempt to address some of the less discussed aspects of the practical applications using different study designs for incorporating bioinformatics and statistical analyses of multi-omics data. Finally, we discuss the potential improvements and new paradigms aimed at unraveling the role and utility of lncRNAs in cancer and their potential use as molecular markers for cancer diagnosis and outcome prediction.

Unlocking the predictive potential of long non-coding RNAs: a machine learning approach for precise cancer patient prognosis

The intricate web of cancer biology is governed by the active participation of long non-coding RNAs (lncRNAs), playing crucial roles in cancer cells' proliferation, migration, and drug resistance. Pioneering research driven by machine learning algorithms has unveiled the profound ability of specific combinations of lncRNAs to predict the prognosis of cancer patients. These findings highlight the transformative potential of lncRNAs as powerful therapeutic targets and prognostic markers. In this comprehensive review, we meticulously examined the landscape of lncRNAs in predicting the prognosis of the top five cancers and other malignancies, aiming to provide a compelling reference for future research endeavours. Leveraging the power of machine learning techniques, we explored the predictive capabilities of diverse lncRNA combinations, revealing their unprecedented potential to accurately determine patient outcomes.

Multidimensional outlook on the pathophysiology of cervical cancer invasion and metastasis

Cervical cancer being one of the primary causes of high mortality rates among women is an area of concern, especially with ineffective treatment strategies. Extensive studies are carried out to understand various aspects of cervical cancer initiation, development and progression; however, invasive cervical squamous cell carcinoma has poor outcomes. Moreover, the advanced stages of cervical cancer may involve lymphatic circulation with a high risk of tumor recurrence at distant metastatic sites. Dysregulation of the cervical microbiome by human papillomavirus (HPV) together with immune response modulation and the occurrence of novel mutations that trigger genomic instability causes malignant transformation at the cervix. In this review, we focus on the major risk factors as well as the functionally altered signaling pathways promoting the transformation of cervical intraepithelial neoplasia into invasive squamous cell carcinoma. We further elucidate genetic and epigenetic variations to highlight the complexity of causal factors of cervical cancer as well as the metastatic potential due to the changes in immune response, epigenetic regulation, DNA repair capacity, and cell cycle progression. Our bioinformatics analysis on metastatic and non-metastatic cervical cancer datasets identified various significantly and differentially expressed genes as well as the downregulation of potential tumor suppressor microRNA miR-28-5p. Thus, a comprehensive understanding of the genomic landscape in invasive and metastatic cervical cancer will help in stratifying the patient groups and designing potential therapeutic strategies.

Preoperative urine sediment chromosomal instability level predicts urothelial cancer prognosis

PURPOSE

Urothelial carcinomas (UCs) are often characterized by frequent recurrences after surgery, making UC one of the costliest cancers. Chromosomal instability (CIN) has been proven to be a hallmark of UCs and is related to the prognosis of many cancer types. In this study, we evaluated CIN of urine sediments as a prognostic indicator for UCs.

METHODS

Patients with UC were prospectively recruited. Preoperative urine samples were collected for whole genome sequencing and urine cytology tests. Patients underwent standard-of-care treatment and were followed up until disease relapse or study ended. Concordance and accuracy of CIN alone or in combination with cytology in predicting disease relapse were assessed. The value of CIN combined with European Organization for Research and Treatment of Cancer (EORTC) model were also analyzed.

RESULTS

A total of 137 patients with UCs were included in this study. Median follow-up was 44.2 months and 41.61% patients suffered from cancer relapse. Patients with CIN-high indicated higher relapse rate, and this distinction was significant for patients underwent transurethral resection of bladder tumor (57.89% vs. 34.29%, P = 0.016). Combination of cytology and CIN result could further classified patients into subgroups with distinct relapse risks. Meanwhile, the combination of CIN and EORTC model significantly improved the prediction accuracy compared with EORTC alone (Harrel's C-index: 0.71 vs. 0.65).

CONCLUSION

CIN level of preoperative urine exfoliated cells had robust prognostic value for bladder cancer patients underwent TURBT. The prognostic model by combining CIN and EORTC may help in stratifying patients to optimize follow-up regimen.

DoxoDB: A Database for the Expression Analysis of Doxorubicin-Induced lncRNA Genes

Cancer and cardiovascular disease are the leading causes of death worldwide. Recent evidence suggests that these two life-threatening diseases share several features in disease progression, such as angiogenesis, fibrosis, and immune responses. This has led to the emergence of a new field called cardio-oncology. Doxorubicin is a chemotherapy drug widely used to treat cancer, such as bladder and breast cancer. However, this drug causes serious side effects, including acute ventricular dysfunction, cardiomyopathy, and heart failure. Based on this evidence, we hypothesize that comparing the expression profiles of cells and tissues treated with doxorubicin may yield new insights into the adverse effects of the drug on cellular activities. To test this hypothesis, we analyzed published RNA sequencing (RNA-seq) data from doxorubicin-treated cells to identify commonly differentially expressed genes, including long non-coding RNAs (lncRNAs) as they are known to be dysregulated in diseased tissues and cells. From our systematic analysis, we identified several doxorubicin-induced genes. To confirm these findings, we treated human cardiac fibroblasts with doxorubicin to record expression changes in the selected doxorubicin-induced genes and performed a loss-of-function experiment of the lncRNA MAP3K4-AS1. To further disseminate the analyzed data, we built the web database DoxoDB.

Underexplored reciprocity between genome-wide methylation status and long non-coding RNA expression reflected in breast cancer research: potential impacts for the disease management in the framework of 3P medicine

Breast cancer (BC) is the most common female malignancy reaching a pandemic scale worldwide. A comprehensive interplay between genetic alterations and shifted epigenetic regions synergistically leads to disease development and progression into metastatic BC. DNA and histones methylations, as the most studied epigenetic modifications, represent frequent and early events in the process of carcinogenesis. To this end, long non-coding RNAs (lncRNAs) are recognized as potent epigenetic modulators in pathomechanisms of BC by contributing to the regulation of DNA, RNA, and histones' methylation. In turn, the methylation status of DNA, RNA, and histones can affect the level of lncRNAs expression demonstrating the reciprocity of mechanisms involved. Furthermore, lncRNAs might undergo methylation in response to actual medical conditions such as tumor development and treated malignancies. The reciprocity between genome-wide methylation status and long non-coding RNA expression levels in BC remains largely unexplored. Since the bio/medical research in the area is, per evidence, strongly fragmented, the relevance of this reciprocity for BC development and progression has not yet been systematically analyzed. Contextually, the article aims at:consolidating the accumulated knowledge on both-the genome-wide methylation status and corresponding lncRNA expression patterns in BC andhighlighting the potential benefits of this consolidated multi-professional approach for advanced BC management. Based on a big data analysis and machine learning for individualized data interpretation, the proposed approach demonstrates a great potential to promote predictive diagnostics and targeted prevention in the cost-effective primary healthcare (sub-optimal health conditions and protection against the health-to-disease transition) as well as advanced treatment algorithms tailored to the individualized patient profiles in secondary BC care (effective protection against metastatic disease). Clinically relevant examples are provided, including mitochondrial health control and epigenetic regulatory mechanisms involved.

Development and validation of ferroptosis-related lncRNA signature and immune-related gene signature for predicting the prognosis of cutaneous melanoma patients

Ferroptosis, a form of cell death caused by iron-dependent peroxidation of lipids, plays an important role in cancer. Recent studies have shown that long noncoding RNAs (lncRNAs) are involved in the regulation of ferroptosis in tumor cells and are also closely related to tumor immunity. Immune cell infiltration in the tumor microenvironment affects the prognosis and clinical outcome of immunotherapy in melanoma patients, and immune cell classification may be able to accurately predict the prognosis of melanoma patients. However, the prognostic value of ferroptosis-related lncRNAs (FRLs) in melanoma has not been thoroughly explored, and it is difficult to define the immune characteristics of melanoma. We used The Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression (GTEx) database, and the FerrDb database to identify FRLs. FRLs with prognostic value were evaluated in an experimental cohort utilizing univariate, LASSO (least absolute shrinkage and selection operator) and multivariate Cox regression, followed by in vitro assays evaluating the expression levels and the biological functions of three candidate FRLs. Kaplan-Meier (K-M) and receiver operating characteristic (ROC) curve analyses were used to assess the validity of the risk model, and the drug sensitivity of FRLs was examined by drug sensitivity analysis. The differentially expressed genes between the high- and low-risk groups in the risk model were enriched in the immune pathway, and we further found immune gene signatures (IRGs) that could predict the prognosis of melanoma patients through a series of methods including single-sample Gene Set Enrichment Analysis (ssGSEA). Finally, two GEO cohorts were used to validate the predictive accuracy and reliability of these two signature models. Our findings suggest that FRLs and IRGs have the potential to predict the prognosis of patients with cutaneous melanoma.

A comprehensive overview of exosome lncRNAs: Emerging biomarkers and potential therapeutics in gynecological cancers

Ovarian, endometrial, and cervical cancer are common gynecologic malignancies, and their incidence is increasing year after year, with a younger patient population at risk. An exosome is a tiny “teacup-like” blister that can be secreted by most cells, is highly concentrated and easily enriched in body fluids, and contains a large number of lncRNAs carrying some biological and genetic information that can be stable for a long time and is not affected by ribonuclease catalytic activity. As a cell communication tool, exosome lncRNA has the advantages of high efficiency and high targeting. Changes in serum exosome lncRNA expression in cancer patients can accurately reflect the malignant biological behavior of cancer cells. Exosome lncRNA has been shown in studies to have broad application prospects in cancer diagnosis, monitoring cancer recurrence or progression, cancer treatment, and prognosis. The purpose of this paper is to provide a reference for clinical research on the pathogenesis, diagnosis, and treatment of gynecologic malignant tumors by reviewing the role of exosome lncRNA in gynecologic cancers and related molecular mechanisms.

Prognostic markers of ferroptosis-related long non-coding RNA in lung adenocarcinomas

Ferroptosis is a recently established type of iron-dependent programmed cell death. Growing studies have focused on the function of ferroptosis in cancers, including lung adenocarcinoma (LUAD). However, the factors involved in the regulation of ferroptosis-related genes are not fully understood. In this study, we collected data from lung adenocarcinoma datasets of the Cancer Genome Atlas (TCGA-LUAD). The expression profiles of 60 ferroptosis-related genes were screened, and two differentially expressed ferroptosis subtypes were identified. We found the two ferroptosis subtypes can predict clinical outcomes and therapeutic responses in LUAD patients. Furthermore, key long non-coding RNAs (lncRNAs) were screened by single factor Cox and least absolute shrinkage and selection operator (LASSO) based on which co-expressed with the 60 ferroptosis-related genes. We then established a risk score model which included 13 LUAD ferroptosis-related lncRNAs with a multi-factor Cox regression. The risk score model showed a good performance in evaluating the outcome of LUAD. What's more, we divided TCGA-LUAD tumor samples into two groups with high- and low-risk scores and further explored the differences in clinical characteristics, tumor mutation burden, and tumor immune cell infiltration among different LUAD tumor risk score groups and evaluate the predictive ability of risk score for immunotherapy benefit. Our findings provide good support for immunotherapy in LUAD in the future.

Insights into the role of long non-coding RNAs in DNA methylation mediated transcriptional regulation

DNA methylation is one of the most important epigenetic mechanisms that governing regulation of gene expression, aberrant DNA methylation patterns are strongly associated with human malignancies. Long non-coding RNAs (lncRNAs) have being discovered as a significant regulator on gene expression at the epigenetic level. Emerging evidences have indicated the intricate regulatory effects between lncRNAs and DNA methylation. On one hand, transcription of lncRNAs are controlled by the promoter methylation, which is similar to protein coding genes, on the other hand, lncRNA could interact with enzymes involved in DNA methylation to affect the methylation pattern of downstream genes, thus regulating their expression. In addition, circular RNAs (circRNAs) being an important class of noncoding RNA are also found to participate in this complex regulatory network. In this review, we summarize recent research progress on this crosstalk between lncRNA, circRNA, and DNA methylation as well as their potential functions in complex diseases including cancer. This work reveals a hidden layer for gene transcriptional regulation and enhances our understanding for epigenetics regarding detailed mechanisms on lncRNA regulatory function in human cancers.

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.

Pan-cancer analysis of LncRNA XIST and its potential mechanisms in human cancers

Background

Methods

Results

Conclusion

Exploration of biomarkers for the diagnosis, treatment and prognosis of cervical cancer: a review

As the fourth most diagnosed cancer, cervical cancer (CC) is one of the major causes of cancer-related mortality affecting females globally, particularly when diagnosed at advanced stage. Discoveries of CC biomarkers pave the road to precision medicine for better patient outcomes. High throughput omics technologies, characterized by big data production further accelerate the process. To date, various CC biomarkers have been discovered through the advancement in technologies. Despite, very few have successfully translated into clinical practice due to the paucity of validation through large scale clinical studies. While vast amounts of data are generated by the omics technologies, challenges arise in identifying the clinically relevant data for translational research as analyses of single-level omics approaches rarely provide causal relations. Integrative multi-omics approaches across different levels of cellular function enable better comprehension of the fundamental biology of CC by highlighting the interrelationships of the involved biomolecules and their function, aiding in identification of novel integrated biomarker profile for precision medicine. Establishment of a worldwide Early Detection Research Network (EDRN) system helps accelerating the pace of biomarker translation. To fill the research gap, we review the recent research progress on CC biomarker development from the application of high throughput omics technologies with sections covering genomics, transcriptomics, proteomics, and metabolomics.

Signature of seven cuproptosis-related lncRNAs as a novel biomarker to predict prognosis and therapeutic response in cervical cancer

Background: Given the high incidence and high mortality of cervical cancer (CC) among women in developing countries, identifying reliable biomarkers for the prediction of prognosis and therapeutic response is crucial. We constructed a prognostic signature of cuproptosis-related long non-coding RNAs (lncRNAs) as a reference for individualized clinical treatment. Methods: A total of seven cuproptosis-related lncRNAs closely related to the prognosis of patients with CC were identified and used to construct a prognostic signature via least absolute shrinkage and selection operator regression analysis in the training set. The predictive performance of the signature was evaluated by Kaplan-Meier (K-M) analysis, receiver operating characteristic (ROC) analysis, and univariate and multivariate Cox analyses. Functional enrichment analysis and single-sample gene set enrichment analysis were conducted to explore the potential mechanisms of the prognostic signature, and a lncRNA-microRNA-mRNA network was created to investigate the underlying regulatory relationships between lncRNAs and cuproptosis in CC. The associations between the prognostic signature and response to immunotherapy and targeted therapy were also assessed. Finally, the prognostic value of the signature was validated using the CC tissues with clinical information in my own center. Results: A prognostic signature was developed based on seven cuproptosis-related lncRNAs, including five protective factors (AL441992.1, LINC01305, AL354833.2, CNNM3-DT, and SCAT2) and two risk factors (AL354733.3 and AC009902.2). The ROC curves confirmed the superior predictive performance of the signature compared with conventional clinicopathological characteristics in CC. The ion transport-related molecular function and various immune-related biological processes differed significantly between the two risk groups according to functional enrichment analysis. Furthermore, we discovered that individuals in the high-risk group were more likely to respond to immunotherapy and targeted therapies including trametinib and cetuximab than those in the low-risk group. Finally, CC tissues with clinical data from my own center further verify the robustness of the seven-lncRNA risk signature. Conclusion: We generated a cuproptosis-related lncRNA risk signature that could be used to predict prognosis of CC patients. Moreover, the signature could be used to predict response to immunotherapy and chemotherapy and thus could assist clinicians in making personalized treatment plans for CC patients.

Four-copy number alteration (CNA)-related lncRNA prognostic signature for liver cancer

The objective of this study was to identify CNA-related lncRNAs that can better evaluate the prognosis of patients with liver cancer. Prognostic molecular subtypes were identified, followed by tumor mutation and differential expression analyses. Genomic copy number anomalies and their association with lncRNAs were also evaluated. A risk model was built based on lncRNAs, as well as a nomogram, and the differences in the tumor immune microenvironment and drug sensitivity between the High_ and Low_risk groups were compared. Weighted gene co-expression network analysis was used to identify modules with significant enrichment in prognostic-related lncRNAs. In total, two subtypes were identified, TP53 and CTNNB1 were common high-frequency mutated genes in the two subtypes. A total of 8,372 differentially expressed (DE) mRNAs and 798 DElncRNAs were identified between cluster1 and cluster2. In addition, a four-lncRNA signature was constructed, and statistically significant differences between the Low_ and High_risk groups were found in terms of CD8 T cells, resting memory CD4 T cells, etc. Enrichment analysis showed that prognostic-related lncRNAs were involved in the cell cycle, p53 signaling pathway, non-alcoholic fatty liver disease, etc. A prognostic prediction signature, based on four-CNA-related lncRNAs, could contribute to a more accurate prognosis of patients with liver cancer.

ABHD11-AS1: An Emerging Long Non-Coding RNA (lncRNA) with Clinical Significance in Human Malignancies

The aberrant expression of lncRNAs has been linked to the development and progression of different cancers. One such lncRNA is ABHD11 antisense RNA 1 (ABHD11-AS1), which has recently gained attention for its significant role in human malignancies. ABHD11-AS1 is highly expressed in gastric, lung, breast, colorectal, thyroid, pancreas, ovary, endometrium, cervix, and bladder cancers. Several reports highlighted the clinical significance of ABHD11-AS1 in prognosis, diagnosis, prediction of cancer progression stage, and treatment response. Significantly, the levels of ABHD11-AS1 in gastric juice had been exhibited as a clinical biomarker for the assessment of gastric cancer, while its serum levels have prognostic potential in thyroid cancers. The ABHD11-AS1 has been reported to exert oncogenic effects by sponging different microRNAs (miRNAs), altering signaling pathways such as PI3K/Akt, epigenetic mechanisms, and N6-methyladenosine (m⁶A) RNA modification. In contrast, the mouse homolog of AHD11-AS1 (Abhd11os) overexpression had exhibited neuroprotective effects against mutant huntingtin-induced toxicity. Considering the emerging research reports, the authors attempted in this first review on ABHD11-AS1 to summarize and highlight its oncogenic potential and clinical significance in different human cancers. Lastly, we underlined the necessity for future mechanistic studies to unravel the role of ABHD11-AS1 in tumor development, prognosis, progression, and targeted therapeutic approaches.

piRNA-14633 promotes cervical cancer cell malignancy in a METTL14-dependent m6A RNA methylation manner

Background

Cervical cancer (CC) is one of the most common gynecological tumors that threatens women's health and lives. Aberrant expression of PIWI-interacting RNA (piRNA) is closely related with a range of cancers and can serve as a tumor promoter or suppressor in proliferation, migration and invasion. In this study, the aim was not only to discover differential expression of piRNA in CC tissue (CC cells) and normal cervical tissue (normal cervical epithelium cells), but also to investigate the biological function and action mechanism of piRNA in CC.

Methods

The DESeq2 approach was used to estimate fold change in piRNA between CC tissue and normal cervical tissue. The relative expressions of piRNAs (piRNA-20657, piRNA-20497, piRNA-14633 and piRNA-13350) and RNA m6A methyltransferases/demethylases were detected using RT-qPCR. After intervention with piRNA-14633 and METTL14 expression, the viability of CaSki cells and SiHa cells was detected by CCK8. CC cell proliferation was detected by colony formation assay. Apoptosis rate and cell cycle were detected by flow cytometry. Transwell assay was performed to detect cell migration and invasion. EpiQuik m6A RNA Methylation Quantification Kit was used to evaluate m6A RNA methylation levels. Expression of methyltransferase-like protein 14 (METTL14), PIWIL-proteins and CYP1B1 were detected by RT-qPCR and western blot. The effect of piRNA-14633 on METTL14 was evaluated by a dual-luciferase reporter assay. The in vivo effects of piRNA-14633 on CC was assessed by nude mice experiments.

Results

piRNA-14633 showed high expression in CC tissues and cells, piRNA-14633 mimic (piRNA-14633 overexpression) promoted viability, proliferation, migration and invasion of CaSki cells and SiHa cells. Besides, piRNA-14633 mimic increased m6A RNA methylation levels and METTL14 mRNA stability. Results of dual luciferase reporter assays indicated that METTL14 was a directed target gene of piRNA-14633. Knockdown of METTL14 with siRNA attenuated proliferation, migration and invasion of CC cells. piRNA-14633 increased CYP1B1 expression, while silencing of METTL14 impaired its expression. The effect of piRNA overexpression on METTL14 expression has concentration-dependent characteristics. Results from in vivo experiment indicated that piRNA-14633 promoted cervical tumor growth.

Conclusion

piRNA-14633 promotes proliferation, migration and invasion of CC cells by METTL14/CYP1B1 signaling axis, highlighting the important role of piRNA-14633 in CC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03257-2.

Identification and Experimental Validation of Immune-Associate lncRNAs for Predicting Prognosis in Cervical Cancer

Purpose

Cervical cancer (CC) is a major risk for health of modern women. Immune-related long non-coding RNAs (lncRNAs) can also serve as prognostic markers of overall survival (OS) in patients with CC. This study aimed to identify an immune-related lncRNA signature for the prospective assessment of prognosis in CC patients.

Methods

We first calculated immune scores of CC patients in the Cancer Genome Atlas (TCGA) database. Univariate Cox, Lasso Cox and multivariate Cox regression analyses were perfumed to establish an immune-relative lncRNA signature. In addition, we processed pathway enrichment analysis and immune infiltration analysis between patients with higher or lower risk. Finally, T-cell Chemotaxis assays were processed to verify the function of 2 key lncRNAs.

Results

Our results suggested that patients with higher immune scores had longer survival time and some lncRNAs expressed differentially between two groups. Eight lncRNAs (LINC02802, LINC01877, RBAKDN, LINC02480, WWC2-AS2, LINC01281, ZBTB20-AS1, IFNG-AS1) were identified as prognostic signatures for CC. The immune-related lncRNA signature was correlated with disease progression and worse prognosis. Immune infiltration analysis indicated that the expression of 8-lncRNA signatures were corrected with infiltration level of immune cell subtypes. In addition, T-cell Chemotaxis assay validated that 2 key lncRNAs (ZBTB20-AS1 and LINC01281) could significantly promote the migration ability of T cells to CC cells.

Conclusion

Our finding demonstrated the value of lncRNAs in evaluating the immune infiltrate of the tumor. The 8-lncRNA signature could predict the prognosis of CC and contribute to decisions regarding the immunotherapeutic strategy.

Non-Coding Variants in Cancer: Mechanistic Insights and Clinical Potential for Personalized Medicine

The cancer genome is characterized by extensive variability, in the form of Single Nucleotide Polymorphisms (SNPs) or structural variations such as Copy Number Alterations (CNAs) across wider genomic areas. At the molecular level, most SNPs and/or CNAs reside in non-coding sequences, ultimately affecting the regulation of oncogenes and/or tumor-suppressors in a cancer-specific manner. Notably, inherited non-coding variants can predispose for cancer decades prior to disease onset. Furthermore, accumulation of additional non-coding driver mutations during progression of the disease, gives rise to genomic instability, acting as the driving force of neoplastic development and malignant evolution. Therefore, detection and characterization of such mutations can improve risk assessment for healthy carriers and expand the diagnostic and therapeutic toolbox for the patient. This review focuses on functional variants that reside in transcribed or not transcribed non-coding regions of the cancer genome and presents a collection of appropriate state-of-the-art methodologies to study them.