Noncoding RNAs Serve as Diagnosis and Prognosis Biomarkers for Hepatocellular Carcinoma

Integrating molecular, biochemical, and immunohistochemical features as predictors of hepatocellular carcinoma drug response using machine-learning algorithms

Introduction

Liver cancer, particularly Hepatocellular carcinoma (HCC), remains a significant global health concern due to its high prevalence and heterogeneous nature. Despite the existence of approved drugs for HCC treatment, the scarcity of predictive biomarkers limits their effective utilization. Integrating diverse data types to revolutionize drug response prediction, ultimately enabling personalized HCC management.

Method

In this study, we developed multiple supervised machine learning models to predict treatment response. These models utilized classifiers such as logistic regression (LR), k-nearest neighbors (kNN), neural networks (NN), support vector machines (SVM), and random forests (RF) using a comprehensive set of molecular, biochemical, and immunohistochemical features as targets of three drugs: Pantoprazole, Cyanidin 3-glycoside (Cyan), and Hesperidin. A set of performance metrics for the complete and reduced models were reported including accuracy, precision, recall (sensitivity), specificity, and the Matthews Correlation Coefficient (MCC).

Results and Discussion

Notably, (NN) achieved the best prediction accuracy where the combined model using molecular and biochemical features exhibited exceptional predictive power, achieving solid accuracy of 0.9693 ∓ 0.0105 and average area under the ROC curve (AUC) of 0.94 ∓ 0.06 coming from three cross-validation iterations. Also, found seven molecular features, seven biochemical features, and one immunohistochemistry feature as promising biomarkers of treatment response. This comprehensive method has the potential to significantly advance personalized HCC therapy by allowing for more precise drug response estimation and assisting in the identification of effective treatment strategies.

Identification and validation of a five-necroptosis-related lncRNAs signature for prognostic prediction in hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is among the most prevalent digestive system malignancies and is associated with a poor prognosis. Necroptosis, a form of regulated death mediated by death receptors, exhibits characteristics of both necrosis and apoptosis. Long non-coding RNAs (lncRNAs) have been identified as crucial regulators in tumor necroptosis. This study aims to identify the necroptosis-related lncRNAs (np-lncRNA) in HCC and investigate their relationships with prognosis.

Method

The RNA-sequencing data, along with clinicopathological and survival information of HCC patients were sourced from The Cancer Genome Atlas (TCGA) database. The np-lncRNAs were analyzed to assess their potential in predicting HCC prognosis. Prognostic signatures related to necroptosis were constructed using stepwise multivariate Cox regression analysis. The prognosis of patients was compared using Kaplan-Meier (KM) analysis. The accuracy of the prognostic signature was evaluated using Receiver operating characteristic (ROC) analysis and decision curve analysis (DCA). Quantitative real-time polymerase chain reaction(qPCR) was employed to validate the lncRNAs expression levels of lncRNAs among samples from an independent cohort.

Results

The np-lncRNAs ZFPM2-AS1, AC099850.3, BACE1-AS, KDM4A-AS1 and MKLN1-AS were identified as potential prognostic biomarkers. The prognostic signature constructed from these np-lncRNAs achieved an Area Under the Curve (AUC) of 0.773. Based on the risk score derived from the signature, patients were divided into two groups, with the high-risk group exhibiting poorer overall survival. Gene Set Enrichment Analysis (GSEA) revealed significantly different between the low risk and high risk groups in tumor-related pathways (such as mTOR, MAPK and p53 signaling pathways) and immune-related functions (like T cell receptor signaling pathway and natural killer cell mediated cytotoxicity). The increased expression of np-lncRNAs was confirmed in another independent HCC cohort.

Conclusions

This signature offers a dependable method for forecasting the prognosis of HCC patients. Our findings indicate a subset of np-lncRNA biomarkers that could be utilized for prognosis prediction and personalized treatment strategies of HCC patients.

Non-Coding RNA as a Biomarker in Lung Cancer

INTRODUCTION

Lung cancer remains one of the most prevalent and deadly cancers globally, with high mortality rates largely due to late-stage diagnosis, aggressive progression, and frequent recurrence. Despite advancements in diagnostic techniques and therapeutic interventions, the overall prognosis for lung cancer patients continues to be dismal.

METHOD

Emerging research has identified non-coding RNAs (ncRNAs), including microRNAs, long non-coding RNAs, and circular RNAs, as critical regulators of gene expression, significantly influencing cancer biology. These ncRNAs play pivotal roles in various aspects of lung cancer pathogenesis, including tumor initiation, progression, metastasis, and resistance to therapy.

RESULTS

We provide a comprehensive analysis of the current understanding of ncRNAs in lung cancer, emphasizing their potential as biomarkers for early diagnosis, prognostication, and the prediction of the therapeutic response. We explore the biological functions of ncRNAs, their involvement in key oncogenic pathways, and the molecular mechanisms by which they modulate gene expression and cellular processes in lung cancer. Furthermore, this review highlights recent advances in ncRNA-based diagnostic tools and therapeutic strategies, such as miRNA mimics and inhibitors, lncRNA-targeted therapies, and circRNA-modulating approaches, offering promising avenues for personalized medicine.

CONCLUSION

Finally, we discuss the challenges and future directions in ncRNA research, including the need for large-scale validation studies and the development of efficient delivery systems for ncRNA-based therapies. This review underscores the potential of ncRNAs to revolutionize lung cancer management by providing novel diagnostic and therapeutic options that could improve patient outcomes.

Long noncoding RNA ZFAS1 exerts a suppressive impact on ferroptosis by modulating the miR-150/AIFM2 axis in hepatocellular carcinoma cells

ZNFX1 Antisense RNA 1 (ZFAS1) act as an oncogenic long noncoding RNA in multiple types of cancer. Ferroptosis is an iron-dependent cell death characterized by excessive iron accumulation and lipid peroxidation. However, to date, the functional role and mechanism of ZFAS1 in ferroptosis in hepatocellular carcinoma (HCC) remains largely unknown. The present study revealed that ZFAS1 was upregulated in HCC and upregulation of ZFAS1 indicated poor clinical outcome of HCC patients. Loss- and gain-of-function experiments demonstrated that knockdown of ZFAS1 inhibited HCC cell proliferation and induced ferroptosis, while overexpression of ZFAS1 exerted opposite effects. ZFAS1 enhanced cell proliferation via suppression of ferroptotic death. Mechanistically, ZFAS1 interacted with miR-150 and decreased its expression. AIFM2, the critical ferroptosis protector, was a direct target of ZFAS1/miR-150. ZFAS1 accelerated HCC proliferation and inhibited ferroptosis by the regulation of the miR-150/AIFM2 axis. These discoveries intimate an essential part of ZFAS1/miR-150/AIFM2 in governing HCC ferroptosis, which may provide a promising therapeutic strategy for HCC patients.

The potential of circulating cell-free RNA in CNS tumor diagnosis and monitoring: A liquid biopsy approach

Early detection of malignancies, through regular cancer screening, has already proven to have potential to increase survival rates. Yet current screening methods rely on invasive, expensive tissue sampling that has hampered widespread use. Liquid biopsy is noninvasive and represents a potential approach to precision oncology, based on molecular profiling of body fluids. Among these, circulating cell-free RNA (cfRNA) has gained attention due to its diverse composition and potential as a sensitive biomarker. This review provides an overview of the processes of cfRNA delivery into the bloodstream and the role of cfRNA detection in the diagnosis of central nervous system (CNS) tumors. Different types of cfRNAs such as microRNAs (miRNAs), long noncoding RNAs (lncRNAs) and circular RNAs (circRNAs) have been recognized as potential biomarkers in CNS tumors. These molecules exhibit differential expression patterns in the plasma, cerebrospinalfluid (CSF) and urine of patients with CNS tumors, providing information for diagnosing the disease, predicting outcomes, and assessing treatment effectiveness. Few clinical trials are currently exploring the use of liquid biopsy for detecting and monitoring CNS tumors. Despite obstacles like sample standardization and data analysis, cfRNA shows promise as a tool in the diagnosis and management of CNS tumors, offering opportunities for early detection, personalized therapy, and improved patient outcomes.

SNHG1: Redefining the Landscape of Hepatocellular Carcinoma through Long Noncoding RNAs

Hepatocellular carcinoma (HCC) represents a global health concern, ranking as the sixth most common malignancy worldwide and the third leading cause of cancer-related mortality. Despite advances in research, the diagnosis and prognosis of such malignancy remain challenging. Alpha-fetoprotein, the current serum biomarker used in the management of HCC, has limited sensitivity and specificity, making early detection and effective management more difficult. Thus, new management approaches in diagnosis and prognosis are needed to improve the outcome and survival of HCC patients. SNHG1 is a long noncoding RNA mainly expressed in the cell and cytoplasm of cells and is consistently upregulated in tissues and cell lines of HCC, where it acts as an important regulator of various processes: modulation of p53 activity, sponging of microRNAs with consequent upregulation of their target mRNAs, regulation of fatty acid, iron and glucose metabolism, and interaction with immune cells. The deregulation of these processes results in abnormal cell division, angiogenesis, and apoptosis, thus promoting various aspects of tumorigenesis, including proliferation, invasion, and migration of cells. Clinically, a higher expression of SNHG1 predicts poorer clinical outcomes by significantly correlating with bigger, less differentiated, and more aggressive tumors, more advanced disease stages, and lower overall survival in HCC patients. This article comprehensively summarizes the current understanding of the multifaceted roles of SNHG1 in the pathogenesis of HCC, while also highlighting its clinicopathological correlations, therefore concluding that it has potential as a biomarker in HCC diagnosis and prognosis.

Significance of signal recognition particle 9 nuclear translocation: Implications for pancreatic cancer prognosis and functionality

Signal recognition particles (SRPs) are essential for regulating intracellular protein transport and secretion. Patients with tumors with high SRP9 expression tend to have a poorer overall survival. However, to the best of our knowledge, no reports have described the relationship between SRP9 localization and prognosis in pancreatic cancer. Thus, the present study aimed to investigate this relationship. Immunohistochemical staining for SRP9 using excised specimens from pancreatic cancer surgery cases without preoperative chemotherapy or radiotherapy showed that SRP9 was preferentially expressed in the nucleus of the cancerous regions in some cases, which was hardly detected in other cases, indicating that SRP9 was transported to the nucleus in the former cases. To compare the prognosis of patients with SRP9 nuclear translocation, patients were divided into two groups: Those with a nuclear translocation rate of >50% and those with a nuclear translocation rate of ≤50%. The nuclear translocation rate of >50% group had a significantly better recurrence‑free survival than the nuclear translocation rate of ≤50% group (P=0.037). Subsequent in vitro experiments were conducted; notably, the nuclear translocation rate of SRP9 was reduced under amino acid‑deficient conditions, suggesting that multiple factors are involved in this phenomenon. To further study the function of SRP9 nuclear translocation, in vitro experiments were performed by introducing SRP9 splicing variants (v1 and v2) and their deletion mutants lacking C‑terminal regions into MiaPaCa pancreatic cancer cells. The results demonstrated that both splicing variants showed nuclear translocation regardless of the C‑terminal deletions, suggesting the role of the N‑terminal regions. Given that SRP9 is an RNA‑binding protein, the study of RNA immunoprecipitation revealed that signaling pathways involved in cancer progression and protein translation were downregulated in nuclear‑translocated v1 and v2. Undoubtedly, further studies of the nuclear translocation of SRP9 will open an avenue to optimize the precise evaluation and therapeutic control of pancreatic cancer.

Effects of blood-processing protocols on cell-free DNA fragmentomics in plasma: Comparisons of one- and two-step centrifugations

BACKGROUND

Cell-free DNA (cfDNA) fragmentomic characteristics are promising analytes with abundant physiological signals for non-invasive disease diagnosis and monitoring. Previous studies on plasma cfDNA fragmentomics commonly employed a two-step centrifugation process for removing cell debris, involving a low-speed centrifugation followed by a high-speed centrifugation. However, the effects of centrifugation conditions on the analysis of cfDNA fragmentome remain uncertain.

METHODS

We collected blood samples from 10 healthy individuals and divided each sample into two aliquots for plasma preparation with one- and two-step centrifugation processes. We performed whole genome sequencing (WGS) of the plasma cfDNA in the two groups and comprehensively compared the cfDNA fragmentomic features. Additionally, we reanalyzed the fragmentomic features of cfDNA from 16 healthy individuals and 16 COVID-19 patients, processed through one- and two-step centrifugation in our previous study, to investigate the impact of centrifugation on disease signals.

RESULTS

Our results showed that there were no significant differences observed in the characteristics of nuclear cfDNA, including size, motif diversity score (MDS) of end motifs, and genome distribution, between plasma samples treated with one- and two-step centrifugation. The cfDNA size shortening in COVID-19 patients was observed in plasma samples with one- and two-step centrifugation methods. However, we observed a significantly higher relative abundance and longer size of cell-free mitochondrial DNA (mtDNA) in the one-step samples compared to the two-step samples. This difference in mtDNA caused by the one- and two-step centrifugation methods surpasses the pathological difference between COVID-19 patients and healthy individuals.

CONCLUSIONS

Our findings indicate that one-step low-speed centrifugation is a simple and potentially suitable method for analyzing nuclear cfDNA fragmentation characteristics. These results offer valuable guidance for cfDNA research in various clinical scenarios.

Exosomal ncRNAs: Multifunctional contributors to the immunosuppressive tumor microenvironment of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a malignant liver cancer characterized by aggressive progression, unfavorable prognosis, and an increasing global health burden. Therapies that precisely target immunological checkpoints and immune cells have gained significant attention as possible therapeutics in recent years. In truth, the efficacy of immunotherapy is heavily contingent upon the tumor microenvironment (TME). Recent studies have indicated that exosomes serve as a sophisticated means of communication among biomolecules, executing an essential part in the TME of immune suppression. Exosomal non-coding RNAs (ncRNAs) can induce the activation of tumor cells and immunosuppressive immune cells that suppress the immune system, such as cancer-associated fibroblasts (CAFs), tumor-associated macrophages (TAMs), tumor-associated neutrophils (TANs), CD+8 T cells, regulatory T cells (Tregs), and regulatory B cells (Bregs). This cell-cell crosstalk triggered by exosomal ncRNAs promotes tumor proliferation and metastasis, angiogenesis, malignant phenotype transformation, and drug resistance. Hence, it is imperative to comprehend how exosomal ncRNAs regulate tumor cells or immune cells within the TME to devise more comprehensive and productive immunotherapy programs. This study discusses the features of exosomal ncRNAs in HCC and how the activation of the exosomes redefines the tumor's immunosuppressive microenvironment, hence facilitating the advancement of HCC. Furthermore, we also explored the potential of exosomal ncRNAs as a viable biological target or natural vehicle for HCC therapy.

Value of Non-Coding RNA Expression in Biofluids to Identify Patients at Low Risk of Pathologies Associated with Pregnancy

Pregnancy-related complications (PRC) impact maternal and fetal morbidity and mortality and place a huge burden on healthcare systems. Thus, effective diagnostic screening strategies are crucial. Currently, national and international guidelines define patients at low risk of PRC exclusively based on their history, thus excluding the possibility of identifying patients with de novo risk (patients without a history of disease), which represents most women. In this setting, previous studies have underlined the potential contribution of non-coding RNAs (ncRNAs) to detect patients at risk of PRC. However, placenta biopsies or cord blood samples are required, which are not simple procedures. Our review explores the potential of ncRNAs in biofluids (fluids that are excreted, secreted, or developed because of a physiological or pathological process) as biomarkers for identifying patients with low-risk pregnancies. Beyond the regulatory roles of ncRNAs in placental development and vascular remodeling, we investigated their specific expressions in biofluids to determine favorable pregnancy outcomes as well as the most frequent pathologies of pregnant women. We report distinct ncRNA panels associated with PRC based on omics technologies and subsequently define patients at low risk. We present a comprehensive analysis of ncRNA expression in biofluids, including those using next-generation sequencing, shedding light on their predictive value in clinical practice. In conclusion, this paper underscores the emerging significance of ncRNAs in biofluids as promising biomarkers for risk stratification in PRC. The investigation of ncRNA expression patterns and their potential clinical applications is of diagnostic, prognostic, and theragnostic value and paves the way for innovative approaches to improve prenatal care and maternal and fetal outcomes.

Genome-wide repeat landscapes in cancer and cell-free DNA

Genetic changes in repetitive sequences are a hallmark of cancer and other diseases, but characterizing these has been challenging using standard sequencing approaches. We developed a de novo kmer finding approach, called ARTEMIS (Analysis of RepeaT EleMents in dISease), to identify repeat elements from whole-genome sequencing. Using this method, we analyzed 1.2 billion kmers in 2837 tissue and plasma samples from 1975 patients, including those with lung, breast, colorectal, ovarian, liver, gastric, head and neck, bladder, cervical, thyroid, or prostate cancer. We identified tumor-specific changes in these patients in 1280 repeat element types from the LINE, SINE, LTR, transposable element, and human satellite families. These included changes to known repeats and 820 elements that were not previously known to be altered in human cancer. Repeat elements were enriched in regions of driver genes, and their representation was altered by structural changes and epigenetic states. Machine learning analyses of genome-wide repeat landscapes and fragmentation profiles in cfDNA detected patients with early-stage lung or liver cancer in cross-validated and externally validated cohorts. In addition, these repeat landscapes could be used to noninvasively identify the tissue of origin of tumors. These analyses reveal widespread changes in repeat landscapes of human cancers and provide an approach for their detection and characterization that could benefit early detection and disease monitoring of patients with cancer.

Molecular profiling in the management of hepatocellular carcinoma

PURPOSE OF REVIEW

The purpose of this review is to both summarize the current knowledge of hepatocellular carcinoma molecular biology and to suggest a framework in which to prospectively translate this knowledge into patient care. This is timely as recent guidelines recommend increased use of these technologies to advance personalized liver cancer care.

RECENT FINDINGS

The main themes covered here address germline and somatic genetic alterations recently discovered in hepatocellular carcinoma, largely owing to next generation sequencing technologies, and nascent efforts to translate these into contemporary practice.

SUMMARY

Early efforts of translating molecular profiling to hepatocellular carcinoma care demonstrate a growing number of potentially actionable alterations. Still lacking are a consensus on what biomarkers and technologies to adopt, at what scale and cost, and how to integrate them most effectively into care.

Unveiling the Genetic Complexity of Teratozoospermia: Integrated Genomic Analysis Reveals Novel Insights into lncRNAs' Role in Male Infertility

Male infertility is a global health issue, affecting over 20 million men worldwide. Genetic factors are crucial in various male infertility forms, including teratozoospermia. Nonetheless, the genetic causes of male infertility remain largely unexplored. In this study, we employed whole-genome sequencing and RNA expression analysis to detect differentially expressed (DE) long-noncoding RNAs (lncRNAs) in teratozoospermia, along with mutations that are exclusive to teratozoospermic individuals within these DE lncRNAs regions. Bioinformatic tools were used to assess variants' impact on lncRNA structure, function, and lncRNA-miRNA interactions. Our analysis identified 1166 unique mutations in teratozoospermic men within DE lncRNAs, distinguishing them from normozoospermic men. Among these, 64 variants in 23 lncRNAs showed potential regulatory roles, 7 variants affected 4 lncRNA structures, while 37 variants in 17 lncRNAs caused miRNA target loss or gain. Pathway Enrichment and Gene Ontology analyses of the genes targeted by the affected miRNAs revealed dysregulated pathways in teratozoospermia and a link between male infertility and cancer. This study lists novel variants and lncRNAs associated for the first time with teratozoospermia. These findings pave the way for future studies aiming to enhance diagnosis and therapy in the field of male infertility.

LncRNA SNHG1: role in tumorigenesis of multiple human cancers

Small nucleolar RNA host gene 1 (SNHG1) is an important member of the SNHG family. This family is composed of a group of host genes that can be processed into small nucleolar RNAs and play important biological functions. In an oncogenic role, the SNHG1 expression is increased in various cancers, which has immense application prospects in the diagnosis, treatment, and prognosis of malignant tumors. In this review, we have summarized the role and molecular mechanism of SNHG1 in the development of various cancers. In addition, we have emphasized the clinical significance of SNHG1 in cancers in our article. This molecule is expected to be a new marker for potential usage in the diagnosis, prognosis, and treatment of cancer.

Emerging role of exosome-shuttled noncoding RNAs in gastrointestinal cancers: From intercellular crosstalk to clinical utility

Gastrointestinal cancer remains a significant global health burden. The pursuit of advancing the comprehension of tumorigenesis, along with the identification of reliable biomarkers and the development of precise therapeutic strategies, represents imperative objectives in this field. Exosomes, small membranous vesicles released by most cells, commonly carry functional biomolecules, including noncoding RNAs (ncRNAs), which are specifically sorted and encapsulated by exosomes. Exosome-mediated communication involves the release of exosomes from tumor or stromal cells and the uptake by nearby or remote recipient cells. The bioactive cargoes contained within these exosomes exert profound effects on the recipient cells, resulting in significant modifications in the tumor microenvironment (TME) and distinct alterations in gastrointestinal tumor behaviors. Due to the feasibility of isolating exosomes from various bodily fluids, exosomal ncRNAs have shown great potential as liquid biopsy-based indicators for different gastrointestinal cancers, using blood, ascites, saliva, or bile samples. Moreover, exosomes are increasingly recognized as natural delivery vehicles for ncRNA-based therapeutic interventions. In this review, we elucidate the processes of ncRNA-enriched exosome biogenesis and uptake, examine the regulatory and functional roles of exosomal ncRNA-mediated intercellular crosstalk in gastrointestinal TME and tumor behaviors, and explore their potential clinical utility in diagnostics, prognostics, and therapeutics.

Cell-free RNA for the liquid biopsy of gastrointestinal cancer

Gastrointestinal (GI) cancer includes many cancer types, such as esophageal, liver, gastric, pancreatic, and colorectal cancer. As the cornerstone of personalized medicine for GI cancer, liquid biopsy based on noninvasive biomarkers provides promising opportunities for early diagnosis and dynamic treatment management. Recently, a growing number of studies have demonstrated the potential of cell-free RNA (cfRNA) as a new type of noninvasive biomarker in body fluids, such as blood, saliva, and urine. Meanwhile, transcriptomes based on high-throughput RNA detection technologies keep discovering new cfRNA biomarkers. In this review, we introduce the origins and applications of cfRNA, describe its detection and qualification methods in liquid biopsy, and summarize a comprehensive list of cfRNA biomarkers in different GI cancer types. Moreover, we also discuss perspective studies of cfRNA to overcome its current limitations in clinical applications. This article is categorized under: RNA in Disease and Development > RNA in Disease.

A comprehensive evaluation of full-spectrum cell-free RNAs highlights cell-free RNA fragments for early-stage hepatocellular carcinoma detection

BACKGROUND

Various studies have reported cell-free RNAs (cfRNAs) as noninvasive biomarkers for detecting hepatocellular carcinoma (HCC). However, they have not been independently validated, and some results are contradictory. We provided a comprehensive evaluation of various types of cfRNA biomarkers and a full mining of the biomarker potential of new features of cfRNA.

METHODS

We first systematically reviewed reported cfRNA biomarkers and calculated dysregulated post-transcriptional events and cfRNA fragments. In 3 independent multicentre cohorts, we further selected 6 cfRNAs using RT-qPCR, built a panel called HCCMDP with AFP using machine learning, and internally and externally validated HCCMDP's performance.

FINDINGS

We identified 23 cfRNA biomarker candidates from a systematic review and analysis of 5 cfRNA-seq datasets. Notably, we defined the cfRNA domain to describe cfRNA fragments systematically. In the verification cohort (n = 183), cfRNA fragments were more likely to be verified, while circRNA and chimeric RNA candidates were neither abundant nor stable as qPCR-based biomarkers. In the algorithm development cohort (n = 287), we build and test the panel HCCMDP with 6 cfRNA markers and AFP. In the independent validation cohort (n = 171), HCCMDP can distinguish HCC patients from control groups (all: AUC = 0.925; CHB: AUC = 0.909; LC: AUC = 0.916), and performs well in distinguishing early-stage HCC patients (all: AUC = 0.936; CHB: AUC = 0.917; LC: AUC = 0.928).

INTERPRETATION

This study comprehensively evaluated full-spectrum cfRNA biomarker types for HCC detection, highlighted the cfRNA fragment as a promising biomarker type in HCC detection, and provided a panel HCCMDP.

FUNDING

National Natural Science Foundation of China, and The National Key Basic Research Program (973 program).

Circulating biomarkers for diagnosis and management of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common primary liver cancer, but the prognosis of HCC patients is poor due to the difficulty of early diagnosis and high recurrence rate. Therefore, it is particularly important to seek effective methods for early diagnosis and early recurrence monitoring after treatment. Circulating biomarkers play an important role in the diagnosis, progression monitoring, and prognosis evaluation of HCC. In recent years, with the discovery of a variety of new biomarkers, the development of biomarkers-related models, and the emergence of liquid biopsy technology, the diagnosis and treatment of HCC have been greatly improved. This article reviews the latest research advances of biomarkers in the diagnosis and treatment of HCC, aiming to provide new ideas for improving the prognosis of HCC patients.

An exosome-related lncRNA signature correlates with prognosis, immune microenvironment, and therapeutic responses in hepatocellular carcinoma

BACKGROUND

Exosomes act as essential modulators of cancer development and progression in hepatocellular carcinoma. However, little is known about the potential prognostic value and underlying molecular features of exosome-related long non-coding RNAs.

METHODS

Genes associated with exosome biogenesis, exosome secretion, and exosome biomarkers were collected. Exosome-related lncRNA modules were identified using PCA and WGCNA analysis. A prognostic model based on data from the TCGA, GEO, NODE, and ArrayExpress was developed and validated. A comprehensive analysis of the genomic landscape, functional annotation, immune profile, and therapeutic responses underlying the prognostic signature was performed on multi-omics data, and bioinformatics methods were also applied to predict potential drugs for patients with high risk scores. qRT-PCR was used to validate the differentially expressed lncRNAs in normal and cancer cell lines.

RESULTS

Twenty-six hub lncRNAs were identified as highly correlated with exosomes and overall survival and were used for prognosis modeling. Three cohorts consistently showed higher scores in the high-risk group, with an AUC greater than 0.7 over time. These higher scores implied poorer overall survival, higher genomic instability, higher tumor purity, higher tumor stemness, pro-tumor pathway activation, lower anti-tumor immune cell and tertiary lymphoid structure infiltration, and poor responses to immune checkpoint blockade therapy and transarterial chemoembolization therapy.

CONCLUSION

Through developing an exosome-related lncRNA predictor for HCC patients, we revealed the clinical relevance of exosome-related lncRNAs and their potential as prognostic biomarkers and therapeutic response predictors.

Evaluation and Application of Drug Resistance by Biomarkers in the Clinical Treatment of Liver Cancer

Liver cancer is one of the most lethal cancers in the world, mainly owing to the lack of effective means for early monitoring and treatment. Accordingly, there is considerable research interest in various clinically applicable methods for addressing these unmet needs. At present, the most commonly used biomarker for the early diagnosis of liver cancer is alpha-fetoprotein (AFP), but AFP is sensitive to interference from other factors and cannot really be used as the basis for determining liver cancer. Treatment options in addition to liver surgery (resection, transplantation) include radiation therapy, chemotherapy, and targeted therapy. However, even more expensive targeted drug therapies have a limited impact on the clinical outcome of liver cancer. One of the big reasons is the rapid emergence of drug resistance. Therefore, in addition to finding effective biomarkers for early diagnosis, an important focus of current discussions is on how to effectively adjust and select drug strategies and guidelines for the treatment of liver cancer patients. In this review, we bring this thought process to the drug resistance problem faced by different treatment strategies, approaching it from the perspective of gene expression and molecular biology and the possibility of finding effective solutions.

MiRNAs and snoRNAs in Bone Metastasis: Functional Roles and Clinical Potential

Bone is a frequent site of metastasis. Bone metastasis is associated with a short-term prognosis in cancer patients, and current treatments aim to slow its growth, but are rarely curative. Thus, revealing molecular mechanisms that explain why metastatic cells are attracted to the bone micro-environment, and how they successfully settle in the bone marrow-taking advantage over bone resident cells-and grow into macro-metastasis, is essential to propose new therapeutic approaches. MicroRNAs and snoRNAs are two classes of small non-coding RNAs that post-transcriptionally regulate gene expression. Recently, microRNAs and snoRNAs have been pointed out as important players in bone metastasis by (i) preparing the pre-metastatic niche, directly and indirectly affecting the activities of osteoclasts and osteoblasts, (ii) promoting metastatic properties within cancer cells, and (iii) acting as mediators within cells to support cancer cell growth in bone. This review aims to highlight the importance of microRNAs and snoRNAs in metastasis, specifically in bone, and how their roles can be linked together. We then discuss how microRNAs and snoRNAs are secreted by cancer cells and be found as extracellular vesicle cargo. Finally, we provide evidence of how microRNAs and snoRNAs can be potential therapeutic targets, at least in pre-clinical settings, and how their detection in liquid biopsies can be a useful diagnostic and/or prognostic biomarker to predict the risk of relapse in cancer patients.

Exosomal long non-coding RNAs: novel molecules in gastrointestinal cancers' progression and diagnosis

Gastrointestinal (GI) cancers arise in the GI tract and accessory organs, including the mouth, esophagus, stomach, liver, biliary tract, pancreas, small intestine, large intestine, and rectum. GI cancers are a major cause of cancer-related morbidity and mortality worldwide. Exosomes act as mediators of cell-to-cell communication, with pleiotropic activity in the regulation of homeostasis, and can be markers for diseases. Non-coding RNAs (ncRNAs), such as long non-coding RNAs (lncRNAs), can be transported by exosomes derived from tumor cells or non-tumor cells. They can be taken by recipient cells to alter their function or remodel the tumor microenvironment. Moreover, due to their uniquely low immunogenicity and excellent stability, exosomes can be used as natural carriers for therapeutic ncRNAs in vivo. Exosomal lncRNAs have a crucial role in regulating several cancer processes, including angiogenesis, proliferation, drug resistance, metastasis, and immunomodulation. Exosomal lncRNA levels frequently alter according to the onset and progression of cancer. Exosomal lncRNAs can therefore be employed as biomarkers for the diagnosis and prognosis of cancer. Exosomal lncRNAs can also monitor the patient's response to chemotherapy while also serving as potential targets for cancer treatment. Here, we discuss the role of exosomal lncRNAs in the biology and possible future treatment of GI cancer.

The Role of Long Noncoding RNAs on Male Infertility: A Systematic Review and In Silico Analysis

Male infertility is a complex disorder affecting many couples worldwide. Long noncoding RNAs (lncRNAs) regulate important cellular processes; however, a comprehensive understanding of their role in male infertility is limited. This systematic review investigates the differential expressions of lncRNAs in male infertility or variations in lncRNA regions associated with it. The PRISMA guidelines were used to search Pubmed and Web of Science (1 June 2022). Inclusion criteria were human participants, patients diagnosed with male infertility, and English language speakers. We also performed an in silico analysis investigating lncRNAs that are reported in many subtypes of male infertility. A total of 625 articles were found, and after the screening and eligibility stages, 20 studies were included in the final sample. Many lncRNAs are deregulated in male infertility, and interactions between lncRNAs and miRNAs play an important role. However, there is a knowledge gap regarding the impact of variants found in lncRNA regions. Furthermore, eight lncRNAs were identified as differentially expressed in many subtypes of male infertility. After in silico analysis, gene ontology (GO) and KEGG enrichment analysis of the genes targeted by them revealed their association with bladder and prostate cancer. However, pathways involved in general in tumorigenesis and cancer development of all types, such as p53 pathways, apoptosis, and cell death, were also enriched, indicating a link between cancer and male infertility. This evidence, however, is preliminary. Future research is needed to explore the exact mechanism of action of the identified lncRNAs and investigate the association between male infertility and cancer.

Circulating biomarkers in the diagnosis and management of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most prevalent and lethal causes of cancer-related death worldwide. The treatment of HCC remains challenging and is largely predicated on early diagnosis. Surveillance of high-risk groups using abdominal ultrasonography, with or without serum analysis of α-fetoprotein (AFP), can permit detection of early, potentially curable tumours, but is limited by its insensitivity. Reviewed here are two current approaches that aim to address this limitation. The first is to use old re-emerged empirically derived biomarkers such as AFP, now applied within statistical models. The second is to use circulating nucleic acid biomarkers, which include cell-free DNA (for example, circulating tumour DNA, cell-free mitochondrial DNA and cell-free viral DNA) and cell-free RNA, applying modern molecular biology-based technologies and machine learning techniques closely allied to the underlying biology of cancer. Taken together, these approaches are likely to be complementary. Both hold considerable promise for achieving earlier diagnosis as well as offering additional functionalities including improved monitoring of therapy and prediction of response thereto.

Small Nucleolar RNAs and Their Comprehensive Biological Functions in Hepatocellular Carcinoma

Small nucleolar RNAs (snoRNAs) are a class of highly conserved, stable non-coding RNAs involved in both post-transcriptional modification of RNA and in ribosome biogenesis. Recent research shows that the dysfunction of snoRNAs plays a pivotal role in hepatocellular carcinoma (HCC) and related etiologies, such as hepatitis B virus (HBV), hepatitis C virus (HCV), and non-alcoholic fatty liver disease (NAFLD). Growing evidence suggests that snoRNAs act as oncogenes or tumor suppressors in hepatocellular carcinoma (HCC) through multiple mechanisms. Furthermore, snoRNAs are characterized by their stability in body fluids and their clinical relevance and represent promising tools as diagnostic and prognostic biomarkers. SnoRNAs represent an emerging area of cancer research. In this review, we summarize the classification, biogenesis, activity, and functions of snoRNAs, as well as highlight the mechanism and roles of snoRNAs in HCC and related diseases. Our findings will aid in the understanding of complex processes of tumor occurrence and development, as well as suggest potential diagnostic markers and treatment targets. Furthermore, we discuss several limitations and suggest future research and application directions.

A systematic review of EV-piRNA in human body fluid and its role in disease progression

The state of host cells is reflected in the cargo carried by their extracellular vesicles (EVs). This makes EV a potential source of biomarkers for human diseases. Piwi-interacting RNA (piRNA) regulates gene expression through epigenetic regulation and post-transcriptional gene silencing. Thus, piRNA profiles in EVs derived from human clinical samples could identify markers that characterize disease stages, and unveil their roles in disease pathology. This review aimed to report the expression profiles of EV-derived piRNA (EV-piRNA) in various human samples, as well as their role in each pathology. A systematic review was conducted to collate the findings of human EV-piRNA from original research articles published in indexed scientific journals up to 16th Feb 2022. Article searches were performed in PubMed, Web of Science, and Scopus databases, using a combination of keywords including 'EV' and 'piRNA'. A total of 775 non-redundant original articles were identified. After subjecting articles to inclusion and exclusion criteria, 34 articles were accepted for this review. The piRNA expression levels among the small RNA profiles of human-derived EVs range from 0.09% to 43.84%, with the lowest expression level reported in urine-derived EVs and the highest percentage in plasma-derived EVs. Differentially expressed EV-piRNAs have been identified in patients with specific disease conditions compared to their counterparts (healthy control), suggesting an association between piRNA and progression in various diseases. Seven articles identified piRNA putative target genes and/or the pathway enrichment of piRNA target genes, and one study demonstrated a direct tole of piRNA candidates in disease pathology. In conclusion, EV-piRNA has been isolated successfully from various human body fluids. EV-piRNA is a new research niche in human disease pathology. The expression profiles of EV-piRNA in various tissue types and disease conditions remain largely unexplored. Furthermore, there is currently a lack of guidelines on piRNA bioinformatics analysis, which could lead to inconsistent results and thus hinder the progression of piRNA discoveries. Lastly, the lack of published scientific evidence on the role of EV-piRNA supports the need for future research to focus on the functional analysis of EV-piRNA as part of the route in piRNA discoveries.

The protective effects of lncRNA ZFAS1/miR-421-3p/MEF2C axis on cerebral ischemia-reperfusion injury

LncRNA ZNFX1 antisense RNA 1 (ZFAS1) could improve neuronal damage and inhibit inflammation and apoptosis. We conducted an in-depth exploration on the protective mechanism of ZFAS1 in cerebral ischemia-reperfusion injury. Overexpressed or silenced plasmids of ZFAS1 were transfected into the cells to analyze the effects of oxygen-glucose deprivation/reperfusion (OGD/R) treatment on the viability, apoptosis and related gene expressions of Neuro-2a cell by performing MTT assay, flow cytometry, qRT-PCR, and Western blot. Bioinformatic analysis, qRT-PCR, dual-luciferase reporter assay and RNA immunoprecipitation were used to screen and verify the miRNA(s) which could competitively bind with ZFAS1 and downstream mRNA(s) targeted by the miRNA(s). The effects of ZFAS1 and the above target miRNA(s) or gene(s) on the apoptosis of OGD/R-injured cells, apoptosis-related proteins, inflammatory factors and p65/IκBα pathway were further verified via the rescue test. The results from the middle cerebral artery occlusion (MCAO) mouse model in vivo were consistent with those from the cellular experiments. The expression of lncRNA ZFAS1 in OGD/R-injured cells was inhibited, and the up-regulation of ZFAS1 protected Neuro-2a cells. MiR-421-3p was predicted to be the target miRNA of ZFAS1 and could offset the protective effect of ZFAS1 overexpression on OGD/R-injured cells following its up-regulation. MEF2C, which was the downstream target gene of miR-421-3p, reversed the OGD/R-induced enhanced cell damage caused by miR-421-3p mimic when MEF2C was overexpressed. In in vivo studies, ZFAS1 overexpression reduced brain tissue infarction, apoptosis and gene regulation caused by MCAO, while miR-421-3p mimic had the opposite effect. Collectively, the regulation of lncRNA ZFAS1/miR-421-3p/MEF2C axis showed protective effects on cerebral ischemia-reperfusion injury.

Key LncRNAs Associated With Oxidative Stress Were Identified by GEO Database Data and Whole Blood Analysis of Intervertebral Disc Degeneration Patients

Background: Intervertebral disc degeneration (IDD) is a major cause of low back pain, but the onset and progression of IDD are unknown. Long non-coding RNA (lncRNA) has been validated to play a critical role in IDD, while an increasing number of studies have linked oxidative stress (OS) to the initiation and progression of IDD. We aim to investigate key lncRNAs in IDD through a comprehensive network of competing endogenous RNA (ceRNA) and to identify possible underlying mechanisms.

Methods: We downloaded IDD-related gene expression data from the Gene Expression Omnibus (GEO) database and obtained differentially expressed-lncRNAs (DE-lncRNA), -microRNAs (DE-miRNA), and -messenger RNAs (DE-mRNA) by bioinformatics analysis. The OS-related lncRNA-miRNA-mRNA ceRNA interaction axis was constructed and key lncRNAs were identified based on ceRNA theory. We performed gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses on mRNAs regulated by lncRNAs in the ceRNA network. Single sample gene set enrichment analysis (ssGSEA) was used to reveal the immune landscape. Expression of key lncRNAs in IDD was assessed by quantitative reverse transcription-polymerase chain reaction (qRT-PCR).

Results: In this study, 111 DE-mRNAs, 20 DE-lncRNAs, and 502 DE-miRNAs were identified between IDD patients and controls, and 16 OS-related DE-lncRNAs were also identified. The resulting lncRNA-miRNA-mRNA network consisted of eight OS-related DE-lncRNA nodes, 24 DE-miRNA nodes, 70 DE-mRNA nodes, and 183 edges. Functional enrichment analysis suggested that the ceRNA network may be involved in regulating biological processes related to cytokine secretion, lipid, and angiogenesis. We also identified four key lncRNAs, namely lncRNA GNAS-AS1, lncRNA MIR100HG, lncRNA LINC01359, and lncRNA LUCAT1, which were also found to be significantly associated with immune cells.

Conclusion: These results provide novel insights into the potential applications of OS-related lncRNAs in patients with IDD.

Cancer type classification using plasma cell-free RNAs derived from human and microbes

The utility of cell-free nucleic acids in monitoring cancer has been recognized by both scientists and clinicians. In addition to human transcripts, a fraction of cell-free nucleic acids in human plasma were proven to be derived from microbes and reported to have relevance to cancer. To obtain a better understanding of plasma cell-free RNAs (cfRNAs) in cancer patients, we profiled cfRNAs in ~300 plasma samples of 5 cancer types (colorectal cancer, stomach cancer, liver cancer, lung cancer, and esophageal cancer) and healthy donors (HDs) with RNA-seq. Microbe-derived cfRNAs were consistently detected by different computational methods when potential contaminations were carefully filtered. Clinically relevant signals were identified from human and microbial reads, and enriched Kyoto Encyclopedia of Genes and Genomes pathways of downregulated human genes and higher prevalence torque teno viruses both suggest that a fraction of cancer patients were immunosuppressed. Our data support the diagnostic value of human and microbe-derived plasma cfRNAs for cancer detection, as an area under the ROC curve of approximately 0.9 for distinguishing cancer patients from HDs was achieved. Moreover, human and microbial cfRNAs both have cancer type specificity, and combining two types of features could distinguish tumors of five different primary locations with an average recall of 60.4%. Compared to using human features alone, adding microbial features improved the average recall by approximately 8%. In summary, this work provides evidence for the clinical relevance of human and microbe-derived plasma cfRNAs and their potential utilities in cancer detection as well as the determination of tumor sites.

Identification of Two Exosomal miRNAs in Circulating Blood of Cancer Patients by Using Integrative Transcriptome and Network Analysis

Exosomes carry molecules of great biological and clinical interest, such as miRNAs. The contents of exosomes vary between healthy controls and cancer patients. Therefore, miRNAs and other molecules transported in exosomes are considered a potential source of diagnostic and prognostic biomarkers in cancer. Many miRNAs have been detected in recent years. Consequently, a substantial amount of miRNA-related data comparing patients and healthy individuals is available, which contributes to a better understanding of the initiation, development, malignancy, and metastasis of cancer using non-invasive sampling procedures. However, a re-analysis of available ncRNA data is rare. This study used available data about miRNAs in exosomes comparing healthy individuals and cancer patients to identify possible global changes related to the presence of cancer. A robust transcriptomic analysis identified two common miRNAs (miR-495-3p and miR-543) deregulated in five cancer datasets. They had already been implicated in different cancers but not reported in exosomes circulating in blood. The study also examined their target genes and the implications of these genes for functional processes.

Comprehensive analysis to identify noncoding RNAs mediated upregulation of maternal embryonic leucine zipper kinase (MELK) correlated with poor prognosis in hepatocellular

Object: Maternal embryonic leucine zipper kinase (MELK) is involved in the development and progression of various cancers. This work investigated the usefulness of MELK in the prediction of hepatocellular carcinoma (HCC) prognosis.

Methods: Information on MELK expression was obtained by pan-cancer analysis using The Cancer Genome Atlas (TCGA) database. The TCGA-liver hepatic cancer (TCGA-LIHC), Oncomine datasets, International Cancer Genome Consortium (ICGC) datasets were used to investigate MELK expression in HCC. The prognostic roles of MELK in HCC were assessed by univariate and multivariate survival analyses. The underlying mechanism for noncoding RNAs (ncRNAs) involved in MELK expression was investigated by in silico studies, correlation, methylation, and survival analyses. The relationships between MELK expression and immune cells, immune markers, and checkpoint markers were also analyzed.

Results: (1) MELK was identified as an independent predictor of overall survival (OS) in HCC patients (MELK high vs. low expression, HR 2.469; 95% CI 1.217–5.008; p = 0.012) in a multivariate Cox analysis, with a concordance index (C-index) value of 0.727 (95% CI 0.750–0.704). (2) The noncoding RNA miR3142HG and the LINC00265/has-miR-101-3p axis were found to regulate MELK expression in HCC tissue. (3) MELK levels were linked to various immune functions, including tumor infiltration and the expression of immune checkpoints and biomarkers in HCC.

Conclusion: MELK may have an oncogenic function in HCC and was found to be up-regulated by ncRNAs and associated with immune cell infiltration and unfavorable prognosis.

Functional Peptides Encoded by Long Non-Coding RNAs in Gastrointestinal Cancer

Gastrointestinal cancer is by far the most common malignancy and the most common cause of cancer-related deaths worldwide. Recent studies have shown that long non-coding RNAs (lncRNAs) play an important role in the epigenetic regulation of cancer cells and regulate tumor progression by affecting chromatin modifications, gene transcription, translation, and sponge to miRNAs. In particular, lncRNA has recently been found to possess open reading frame (ORF), which can encode functional small peptides or proteins. These peptides interact with its targets to regulate transcription or the signal axis, thus promoting or inhibiting the occurrence and development of tumors. In this review, we summarize the involvement of lncRNAs and the function of lncRNAs encoded small peptides in gastrointestinal cancer.

The lncRNA B3GALT5-AS1 Functions as an HCC Suppressor by Regulating the miR-934/UFM1 Axis

Accumulating evidence has demonstrated that long noncoding RNA (lncRNA) is importantly related to the occurrence and development of cancer. According to reports, the expression of B3GALT5-AS1 in hepatocellular carcinoma (HCC) is downregulated; however, the role of B3GALT5-AS1 in HCC is not yet clear. In this study, our purpose is to explore the biological function of B3GALT5-AS1 in HCC and its coupling mechanism with miR-934 and ubiquitin-fold modifier 1 (UFM1). We found that the B3GALT5-AS1 expression level was of significant reduction in both HCC tissues and cell lines; B3GALT5-AS1 overexpression (ov) may inhibit the malignant features of HCC. In addition, we demonstrated that miR-934 mimics could reverse the effect of B3GALT5-AS1 ov, which proved miR-934 was the downstream regulator of B3GALT5-AS1. Furthermore, si-UFM1 could reverse the effect of miR-934 inhibitor, which revealed the connection between them. Moreover, we found that B3GALT5-AS1 could keep down the PI3K/AKT pathway through UFM1. Our results demonstrated that B3GALT5-AS1 was an excellent HCC suppressant by regulating miR-934 and UFM1 to achieve negative regulation of HCC cell proliferation, invasion, and metastasis, indicating that B3GALT5-AS1 is a promising potential therapeutic target for HCC treatment.

LPI-deepGBDT: a multiple-layer deep framework based on gradient boosting decision trees for lncRNA-protein interaction identification

Background

Long noncoding RNAs (lncRNAs) play important roles in various biological and pathological processes. Discovery of lncRNA–protein interactions (LPIs) contributes to understand the biological functions and mechanisms of lncRNAs. Although wet experiments find a few interactions between lncRNAs and proteins, experimental techniques are costly and time-consuming. Therefore, computational methods are increasingly exploited to uncover the possible associations. However, existing computational methods have several limitations. First, majority of them were measured based on one simple dataset, which may result in the prediction bias. Second, few of them are applied to identify relevant data for new lncRNAs (or proteins). Finally, they failed to utilize diverse biological information of lncRNAs and proteins.

Results

Under the feed-forward deep architecture based on gradient boosting decision trees (LPI-deepGBDT), this work focuses on classify unobserved LPIs. First, three human LPI datasets and two plant LPI datasets are arranged. Second, the biological features of lncRNAs and proteins are extracted by Pyfeat and BioProt, respectively. Thirdly, the features are dimensionally reduced and concatenated as a vector to represent an lncRNA–protein pair. Finally, a deep architecture composed of forward mappings and inverse mappings is developed to predict underlying linkages between lncRNAs and proteins. LPI-deepGBDT is compared with five classical LPI prediction models (LPI-BLS, LPI-CatBoost, PLIPCOM, LPI-SKF, and LPI-HNM) under three cross validations on lncRNAs, proteins, lncRNA–protein pairs, respectively. It obtains the best average AUC and AUPR values under the majority of situations, significantly outperforming other five LPI identification methods. That is, AUCs computed by LPI-deepGBDT are 0.8321, 0.6815, and 0.9073, respectively and AUPRs are 0.8095, 0.6771, and 0.8849, respectively. The results demonstrate the powerful classification ability of LPI-deepGBDT. Case study analyses show that there may be interactions between GAS5 and Q15717, RAB30-AS1 and O00425, and LINC-01572 and P35637.

Conclusions

Integrating ensemble learning and hierarchical distributed representations and building a multiple-layered deep architecture, this work improves LPI prediction performance as well as effectively probes interaction data for new lncRNAs/proteins.

Do combined assays of serum AFP, AFP-L3, DCP, GP73, and DKK-1 efficiently improve the clinical values of biomarkers in decision-making for hepatocellular carcinoma? A meta-analysis

Objectives: Serum biomarkers are valuable for clinical decision-making for patients with hepatocellular carcinoma (HCC), among which the most promising are AFP, AFP-L3, DCP, DKK-1, and GP73; however, the efficacy of using combined biomarkers remains controversial. This meta-analysis provides insights regarding this topic.Methods: After systematically surveying the literature available in PubMed, Embase, and Cochrane Library, we identified 28 qualified articles published since January 2015. A random-effects model was used to assess pooled sensitivity, specificity, positive and negative likelihood ratios (PLRs and NLPs), and diagnostic odds ratio (DOR).Results: Values under the summary receiver operating characteristic (SROC) curve varied in different panels of the five biomarkers. Importantly, the sum of sensitivity and specificity of AFP+GP73 was 1.76 (P= 0.0001), which was the best among all the panels. The sum of the triple biomarker panel of AFP, AFP-L3, and DCP was larger (1.64, P= 0.0001) than those of any double biomarker panels of AFP, AFP-L3, and DCP.Conclusions: To the best of our knowledge, this is the first meta-analysis to focus solely on combination assays of multiple biomarkers in HCC. The combined assay of AFP and GP73 conferred the best outcome among all panels. The triple combined panel of AFP, AFP-L3, and DCP showed higher diagnostic potential than individual random double combinations of the three biomarkers. Multiple-biomarker combined assays will be clinically important for decision-making processes for HCC.

Identifying novel transcript biomarkers for hepatocellular carcinoma (HCC) using RNA-Seq datasets and machine learning

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer death in the world owing to limitations in its prognosis. The current prognosis approaches include radiological examination and detection of serum biomarkers, however, both have limited efficiency and are ineffective in early prognosis. Due to such limitations, we propose to use RNA-Seq data for evaluating putative higher accuracy biomarkers at the transcript level that could help in early prognosis.

METHODS

To identify such potential transcript biomarkers, RNA-Seq data for healthy liver and various HCC cell models were subjected to five different machine learning algorithms: random forest, K-nearest neighbor, Naïve Bayes, support vector machine, and neural networks. Various metrics, namely sensitivity, specificity, MCC, informedness, and AUC-ROC (except for support vector machine) were evaluated. The algorithms that produced the highest values for all metrics were chosen to extract the top features that were subjected to recursive feature elimination. Through recursive feature elimination, the least number of features were obtained to differentiate between the healthy and HCC cell models.

RESULTS

From the metrics used, it is demonstrated that the efficiency of the known protein biomarkers for HCC is comparatively lower than complete transcriptomics data. Among the different machine learning algorithms, random forest and support vector machine demonstrated the best performance. Using recursive feature elimination on top features of random forest and support vector machine three transcripts were selected that had an accuracy of 0.97 and kappa of 0.93. Of the three transcripts, two were protein coding (PARP2-202 and SPON2-203) and one was a non-coding transcript (CYREN-211). Lastly, we demonstrated that these three selected transcripts outperformed randomly taken three transcripts (15,000 combinations), hence were not chance findings, and could then be an interesting candidate for new HCC biomarker development.

CONCLUSION

Using RNA-Seq data combined with machine learning approaches can aid in finding novel transcript biomarkers. The three biomarkers identified: PARP2-202, SPON2-203, and CYREN-211, presented the highest accuracy among all other transcripts in differentiating the healthy and HCC cell models. The machine learning pipeline developed in this study can be used for any RNA-Seq dataset to find novel transcript biomarkers. Code: www.github.com/rajinder4489/ML_biomarkers.

The Role of Extracellular Vesicles in Disease Progression and Detection of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the most common primary liver malignancy and one of the leading causes of cancer-related death worldwide. Despite the improvements in surveillance and treatment, the prognosis of HCC remains poor. Extracellular vesicles (EVs) are a heterogeneous group of phospholipid bilayer-enclosed particles circulating in the bloodstream and mediating intercellular communication. Emerging studies have shown that EVs play a crucial role in regulating the proliferation, immune escape, and metastasis of HCC. In addition, because EVs are present in the circulation at relatively early stages of disease, they are getting attention as an attractive biomarker for HCC detection. Over the past decade, dedicated efforts have been made to isolate EVs more efficiently and make them useful tools in different clinical settings. In this review article, we provide an overview of the EVs isolation methods and highlight the role of EVs as mediators in the pathogenesis and progression of HCC. Lastly, we summarize the potential applications of EVs in early-stage HCC detection.

PERK/ATF4-Dependent ZFAS1 Upregulation Is Associated with Sorafenib Resistance in Hepatocellular Carcinoma Cells

Sorafenib, a multi-kinase inhibitor, is the first-line treatment for advanced hepatocellular carcinoma (HCC) patients. However, this drug only provides a short improvement of patients' overall survival, and drug resistance is commonly developed. Thus, the identification of resistant factor(s) or biomarker(s) is needed to develop more efficient therapeutic strategies. Long, non-coding RNAs (lncRNAs) have recently been viewed as attractive cancer biomarkers and drive many important cancer phenotypes. A lncRNA, ZFAS1 (ZNFX1 antisense RNA 1) has been found to promote HCC metastasis. This study found that sorafenib induced ZFAS1 expression specifically in sorafenib-resistant HCC cells. Although ZFAS1 knockdown did not restore the sensitivity of HCC cells to sorafenib, its expression may act as a resistant biomarker for sorafenib therapy. Bioinformatics analysis predicted that sorafenib tended to induce pathways related to endoplasmic reticulum (ER) stress and the unfolded protein response (UPR) in sorafenib-resistant HCC cells. In vitro experimental evidence suggested that sorafenib induced protein kinase RNA-like ER kinase (PERK)/activating transcription factor 4 (ATF4)-dependent ZFAS1 expression, and sorafenib resistance could be overcome by PERK/ATF inhibitors. Therefore, PERK/ATF4/ZFAS1 signaling axis might be an attractive therapeutic and prognostic biomarker for sorafenib therapy in HCC.

Exosomal Non-Coding RNAs: Regulatory and Therapeutic Target of Hepatocellular Carcinoma

Exosomes are small extracellular vesicles secreted by most somatic cells, which can carry a variety of biologically active substances to participate in intercellular communication and regulate the pathophysiological process of recipient cells. Recent studies have confirmed that non-coding RNAs (ncRNAs) carried by tumor cell/non-tumor cell-derived exosomes have the function of regulating the cancerous derivation of target cells and remodeling the tumor microenvironment (TME). In addition, due to the unique low immunogenicity and high stability, exosomes can be used as natural vehicles for the delivery of therapeutic ncRNAs in vivo. This article aims to review the potential regulatory mechanism and the therapeutic value of exosomal ncRNAs in hepatocellular carcinoma (HCC), in order to provide promising targets for early diagnosis and precise therapy of HCC.

The Role of Liquid Biopsy in Hepatocellular Carcinoma Prognostication

Simple Summary

Hepatocellular carcinoma (HCC) is one of the deadliest cancer. Clinical guidelines for the management of HCC endorse algorithms deriving from clinical variables whose performances to prognosticate HCC is limited. Liquid biopsy is the molecular analysis of tumor by-products released into the bloodstream. It offers minimally-invasive access to circulating analytes like DNA, RNA, exosomes and cells. This technology demonstrated promising results for various applications in cancers, including prognostication. This review aimed to provide a comprehensive overview of the contribution of liquid biopsy in HCC prognostication. The results suggested that liquid biopsy may be a polyvalent and valuable tool to prognosticate HCC.

Abstract

Showing a steadily increasing cancer-related mortality, the epidemiological evolution of hepatocellular carcinoma (HCC) is concerning. Numerous strategies have attempted to prognosticate HCC but their performance is modest; this is partially due to the heterogeneous biology of this cancer. Current clinical guidelines endorse classifications and scores that use clinical variables, such as the Barcelona Clinic Liver Cancer (BCLC) classification. These algorithms are unlikely to fully recapitulate the genomic complexity of HCC. Integrating molecular readouts on a patient-basis, following a precision-medicine perspective, might be an option to refine prognostic systems. The limited access to HCC tissue samples is an important limitation to these approaches but it could be partially circumvented by using liquid biopsy. This concept consists of the molecular analysis of products derived from a solid tumor and released into biological fluids, mostly into the bloodstream. It offers an easy and minimally-invasive access to DNA, RNA, extracellular vesicles and cells that can be analyzed with next-generation sequencing (NGS) technologies. This review aims to investigate the potential contributions of liquid biopsy in HCC prognostication. The results identified prognostic values for each of the components of liquid biopsy, suggesting that this technology may help refine HCC prognostication.

SNHG1 knockdown upregulates miR-376a and downregulates FOXK1/Snail axis to prevent tumor growth and metastasis in HCC

Long non-coding RNAs (lncRNAs), microRNAs (miRNAs or miRs), and genes are emerging players in cancer progression. In the present study, we explored the roles and interactions of oncogenic lncRNA small nucleolar RNA host gene 1 (SNHG1), miR-376, forkhead box protein K1 (FOXK1), and Snail in hepatocellular carcinoma (HCC). Expression of SNHG1, miR-376, and FOXK1 in HCC was characterized in clinical HCC tissues of 75 patients with HCC. The interactions between SNHG1 and miR-376 and between miR-376 and FOXK1 were predicted and confirmed by dual-luciferase reporter gene and RNA immunoprecipitation assays. Overexpression and knockdown experiments were performed in HCC cells to examine the effects of the SNHG1/miR-376/FOXK1/Snail axis on viability, apoptosis, invasiveness, and migrating abilities. Their effects on tumor growth and metastasis were validated in nude mouse models. SNHG1 and FOXK1 were upregulated, and miR-376a was downregulated in HCC. SNHG1 knockdown contributed to suppression of HCC cell viability, invasion, and migration properties and promotion of apoptosis. SNHG1 could competitively bind to miR-376a to upregulate its target gene FOXK1, which upregulated Snail. SNHG1 knockdown delayed cancer progression both in vitro and in vivo by upregulating miR-376a and downregulating FOXK1 and Snail. SNHG1 knockdown exerts anti-tumor activity in HCC, suggesting a therapeutic target.

Circulating Long and Circular Noncoding RNA as Non-Invasive Diagnostic Tools of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most common causes of cancer-related death worldwide, partially due to late diagnosis of the disease. Growing evidence in the field of biomarker discovery has shown the promising use of nucleic acid in the early detection of many cancers, including HCC. Here, we review data on how various long noncoding RNAs (lncRNAs) and circular RNAs (circRNAs) could be used as a diagnostic tool for HCC being differentially expressed in HCC compared to non-HCC patients. These non-coding RNAs (ncRNAs) showed high stability in the blood being present as free-circulating molecules or encapsulated into exosomes. This review reports some recent evidence on the use of lncRNAs and circRNAs as possible diagnostic biomarkers for HCC. Further, their pathophysiological mechanism in liver carcinogenesis was also described, elucidating the complex regulatory networks making these ncRNAs of particular relevance for the study of liver malignancy cancer.

Integrative analysis of long extracellular RNAs reveals a detection panel of noncoding RNAs for liver cancer

Rationale: Long extracellular RNAs (exRNAs) in plasma can be profiled by new sequencing technologies, even with low abundance. However, cancer-related exRNAs and their variations remain understudied. Methods: We investigated different variations (i.e. differential expression, alternative splicing, alternative polyadenylation, and differential editing) in diverse long exRNA species (e.g. long noncoding RNAs and circular RNAs) using 79 plasma exosomal RNA-seq (exoRNA-seq) datasets of multiple cancer types. We then integrated 53 exoRNA-seq datasets and 65 self-profiled cell-free RNA-seq (cfRNA-seq) datasets to identify recurrent variations in liver cancer patients. We further combined TCGA tissue RNA-seq datasets and validated biomarker candidates by RT-qPCR in an individual cohort of more than 100 plasma samples. Finally, we used machine learning models to identify a signature of 3 noncoding RNAs for the detection of liver cancer. Results: We found that different types of RNA variations identified from exoRNA-seq data were enriched in pathways related to tumorigenesis and metastasis, immune, and metabolism, suggesting that cancer signals can be detected from long exRNAs. Subsequently, we identified more than 100 recurrent variations in plasma from liver cancer patients by integrating exoRNA-seq and cfRNA-seq datasets. From these datasets, 5 significantly up-regulated long exRNAs were confirmed by TCGA data and validated by RT-qPCR in an independent cohort. When using machine learning models to combine two of these validated circular and structured RNAs (SNORD3B-1, circ-0080695) with a miRNA (miR-122) as a panel to classify liver cancer patients from healthy donors, the average AUROC of the cross-validation was 89.4%. The selected 3-RNA panel successfully detected 79.2% AFP-negative samples and 77.1% early-stage liver cancer samples in the testing and validation sets. Conclusions: Our study revealed that different types of RNA variations related to cancer can be detected in plasma and identified a 3-RNA detection panel for liver cancer, especially for AFP-negative and early-stage patients.

BTEB2-Activated lncRNA TSPEAR-AS2 Drives GC Progression through Suppressing GJA1 Expression and Upregulating CLDN4 Expression

Long non-coding RNAs (lncRNAs) are characterized as key layers of the genome in various cancers. TSPEAR-AS2 was highlighted to be a candidate lncRNA potentially involved in gastric cancer (GC) progression. However, the clinical significance and mechanism of TSPEAR-AS2 in GC required clarification. The clinical significance of TSPEAR-AS2 was elucidated through Kaplan-Meier Plotter. The mechanism of TSPEAR-AS2 in GC was clarified in vitro and in vivo using luciferase reporter, chromatin immunoprecipitation, RNA immunoprecipitation assays, and animal models. TSPEAR-AS2 elevation was closely correlated with overall survival of GC patients. A basic transcription element-binding protein 2 (BTEB2)-activated TSPEAR-AS2 model was first explored in this study. TSPEAR-AS2 silencing substantially reduced tumorigenic capacities of GC cells, while TSPEAR-AS2 elevation had the opposite effect. Mechanistically, TSPEAR-AS2 bound with both polycomb repressive complex 2 (PRC2) and argonaute 2 (Ago2). TSPEAR-AS2 knockdown significantly decreased H3K27me3 levels at promoter regions of gap junction protein alpha 1 (GJA1). Ago2 was recruited by TSPEAR-AS2, which was defined to sponge miR-1207-5p, contributing to the repression of claudin 4 (CLDN4) translation. The axis of EZH2/GJA1 and miR-1207-5p/CLDN4 mediated by BTEB2-activated-TSPEAR-AS2 plays an important role in GC progression, suggesting a new therapeutic direction in GC treatment.

Expression of NOTCH receptors and ligands and prognosis of hepatocellular carcinoma

Aim: To elucidate potential prognostic significance of NOTCH receptor and ligand expression in hepatocellular carcinoma. Materials & methods: NOTCH receptors and ligands were divided into increased and decreased expression groups by X-tile program. The association between NOTCH receptors/ligands and prognosis was analyzed by Kaplan-Meier method and log-rank test. Gene set enrichment analysis was performed to explore NOTCH receptors/ligands-related pathways via gsea-3.0. Results: DLL3 and DLL4 were independent prognostic factors for overall survival. Further studies showed that only DLL3 was significantly associated with tumor, node, metastasis stage. Gene set enrichment analysis analysis demonstrated that retinol metabolism, drug metabolism cytochrome P450 and tryptophan metabolism were significantly enriched in DLL3 expression phenotype. Conclusion: We demonstrate that DLL3 may be a prognostic biomarker in hepatocellular carcinoma.

Multi-Omics Data Integration in Extracellular Vesicle Biology-Utopia or Future Reality?

Extracellular vesicles (EVs) are membranous structures derived from the endosomal system or generated by plasma membrane shedding. Due to their composition of DNA, RNA, proteins, and lipids, EVs have garnered a lot of attention as an essential mechanism of cell-to-cell communication, with various implications in physiological and pathological processes. EVs are not only a highly heterogeneous population by means of size and biogenesis, but they are also a source of diverse, functionally rich biomolecules. Recent advances in high-throughput processing of biological samples have facilitated the development of databases comprised of characteristic genomic, transcriptomic, proteomic, metabolomic, and lipidomic profiles for EV cargo. Despite the in-depth approach used to map functional molecules in EV-mediated cellular cross-talk, few integrative methods have been applied to analyze the molecular interplay in these targeted delivery systems. New perspectives arise from the field of systems biology, where accounting for heterogeneity may lead to finding patterns in an apparently random pool of data. In this review, we map the biological and methodological causes of heterogeneity in EV multi-omics data and present current applications or possible statistical methods for integrating such data while keeping track of the current bottlenecks in the field.

Exosomal noncoding RNAs in cholangiocarcinoma: Laboratory noise or hope?

Currently, extracellular vesicles and particularly exosomes have gained a lot of research interest due to their unique roles in several biological processes. Noncoding RNAs (microRNAs, long noncoding RNAs and circular RNAs) represent a class of functional RNA with distinct regulatory roles in tumorigenesis and cancer progression. Cholangiocarcinoma is a rare but highly aggressive type of malignancy that is very challenging to diagnose, especially in early stages; surgical resection still represents the sole potentially curative treatment option. Hence, there is an urgent need for the discovery of novel diagnostic and prognostic biomarkers. Hereby, we provide a comprehensive review of the most recent discoveries that focus on exosomal noncoding RNAs in cholangio-carcinoma with the aim to identify new molecular players that could be used as biomarkers and therapeutic targets.

Panel of potential lncRNA biomarkers can distinguish various types of liver malignant and benign tumors

PURPOSE

Liver cancers are among the deadliest malignancies due to a limited efficacy of early diagnostics, the lack of appropriate biomarkers and insufficient discrimination of different types of tumors by classic and molecular methods. In this study, we searched for novel long non-coding RNA (lncRNA) as well as validated several known candidates suitable as probable biomarkers for primary liver tumors of various etiology.

METHODS

We described a novel lncRNA HELIS (aka "HEalthy LIver Specific") and estimated its expression by RT-qPCR in 82 paired tissue samples from patients with hepatocellular carcinoma (HCC), cholangiocarcinoma (CCA), combined HCC-CCA, pediatric hepatoblastoma (HBL) and non-malignant hepatocellular adenoma (HCA) and focal nodular hyperplasia (FNH). Additionally, we examined expression of cancer-associated lncRNAs HULC, MALAT1, UCA1, CYTOR, LINC01093 and H19, which were previously studied mainly in HCC.

RESULTS

We demonstrated that down-regulation of HELIS strongly correlates with carcinogenesis; whereas in tumors with non-hepatocyte origin (HBL, CCA) or in a number of poorly differentiated HCC, this lncRNA is not expressed. We showed that recently discovered LINC01093 is dramatically down-regulated in all malignant liver cancers; while in benign tumors LINC01093 expression is just twice decreased in comparison to adjacent samples.

CONCLUSION

Our study revealed that among all measured biomarkers only down-regulated HELIS and LINC01093, up-regulated CYTOR and dysregulated HULC are perspective for differential diagnostics of liver cancers; whereas others demonstrated discordant results and cannot be considered as potential universal biomarkers for this purpose.