Combined identification of three lncRNAs in serum as effective diagnostic and prognostic biomarkers for hepatitis B virus-related hepatocellular carcinoma

Subtype cluster analysis unveiled the correlation between m6A- and cuproptosis-related lncRNAs and the prognosis, immune microenvironment, and treatment sensitivity of esophageal cancer

Objective

Esophageal cancer (EC) is characterized by a high degree of malignancy and poor prognosis. N6-methyladenosine (m6A), a prominent post-transcriptional modification of mRNA in mammalian cells, plays a pivotal role in regulating various cellular and biological processes. Similarly, cuproptosis has garnered attention for its potential implications in cancer biology. This study seeks to elucidate the impact of m6A- and cuproptosis-related long non-coding RNAs (m6aCRLncs) on the prognosis of patients with EC.

Methods

The EC transcriptional data and corresponding clinical information were retrieved from The Cancer Genome Atlas (TCGA) database, comprising 11 normal samples and 159 EC samples. Data on 23 m6A regulators and 25 cuproptosis-related genes were sourced from the latest literature. The m6aCRLncs linked to EC were identified through co-expression analysis. Differentially expressed m6aCRLncs associated with EC prognosis were screened using the limma package in R and univariate Cox regression analysis. Subtype clustering was performed to classify EC patients, enabling the investigation of differences in clinical outcomes and immune microenvironment across patient clusters. A risk prognostic model was constructed using least absolute shrinkage and selection operator (LASSO) regression. Its robustness was evaluated through survival analysis, risk stratification curves, and receiver operating characteristic (ROC) curves. Additionally, the model's applicability across various clinical features and molecular subtypes of EC patients was assessed. To further explore the model's utility in predicting the immune microenvironment, single-sample gene set enrichment analysis (ssGSEA), immune cell infiltration analysis, and immune checkpoint differential expression analysis were conducted. Drug sensitivity analysis was performed to identify potential therapeutic agents for EC. Finally, the mRNA expression levels of m6aCRLncs in EC cell lines were validated using reverse transcription quantitative polymerase chain reaction (RT-qPCR).

Results

We developed a prognostic risk model based on five m6aCRLncs, namely ELF3-AS1, HNF1A-AS1, LINC00942, LINC01389, and MIR181A2HG, to predict survival outcomes and characterize the immune microenvironment in EC patients. Analysis of molecular subtypes and clinical features revealed significant differences in cluster distribution, disease stage, and N stage between high- and low-risk groups. Immune profiling further identified distinct immune cell populations and functional pathways associated with risk scores, including positive correlations with naive B cells, resting CD4+ T cells, and plasma cells, and negative correlations with macrophages M0 and M1. Additionally, we identified key immune checkpoint-related genes with significant differential expression between risk groups, including TNFRSF14, TNFSF15, TNFRSF18, LGALS9, CD44, HHLA2, and CD40. Furthermore, nine candidate drugs with potential therapeutic efficacy in EC were identified: Bleomycin, Cisplatin, Cyclopamine, PLX4720, Erlotinib, Gefitinib, RO.3306, XMD8.85, and WH.4.023. Finally, RT-qPCR validation of the mRNA expression levels of m6aCRLncs in EC cell lines demonstrated that ELF3-AS1 expression was significantly upregulated in the EC cell lines KYSE-30 and KYSE-180 compared to normal esophageal epithelial cells.

Conclusion

This study elucidates the role of m6aCRLncs in shaping the prognostic outcomes and immune microenvironment of EC. Furthermore, it identifies potential therapeutic agents with efficacy against EC. These findings hold significant promise for enhancing the survival of EC patients and provide valuable insights to inform clinical decision-making in the management of this disease.

Whole transcriptome and proteome analyses identify ncRNAs and mRNAs to predict competing endogenous RNA networks in hepatitis B virus-induced hepatocellular carcinoma

The presence of the Hepatitis B virus (HBV) is considered as a valuable risk factor of hepatocellular carcinoma (HCC). To more deeply comprehend the molecular mechanism and transcriptome of HBV-induced HCC, we utilized tandem mass tagging (TMT)-based quantitative proteomics analysis and whole-transcriptome sequencing to analyze three sets of matched HepG2 hepatoma cells and HBV-positive HepAD38 cells. The differentially expressed (DE) proteins (1596), mRNAs (5263), miRNAs (581), lncRNAs (2672) and circRNAs (222) were subjected to differential expression and enrichment analyses in order to thoroughly assess the gene-regulatory circuits of HBV-induced HCC. Subsequently, the amounts of 321 DEproteins-DEmRNAs with common alterations were confirmed. According to functional pathway analysis, the DEproteins-DEmRNAs were primarily linked to signaling pathways, amino acid metabolism, and cellular function. Furthermore, the viability and significance of the ceRNA regulatory networks, LOC105377730/miR-4726-5p/FHL2 and hsa_circ_0001098/miR-2110/IGF2BP1, were randomly chosen and confirmed. Our work provides a valuable asset in terms of understanding regulatory activities at the RNA level, and might reveal fresh information about the fundamental mechanism and potential therapeutic targets of HBV-induced HCC.

LncRNAs in serum-derived extracellular vesicles are potential biomarker and correlated with immune infiltration in gastric cancer

Background

Long non-coding RNAs (lncRNAs) in extracellular vesicles (EVs) have been confirmed as effective non-invasive biomarkers for multiple diseases. However, their expression and clinical value in gastric cancer (GC) remain poorly understood.

Materials and methods

Serum EV RNA was extracted from four patients with GC and four healthy controls, followed by high-throughput RNA sequencing. LncRNAs were further validated in training and validation sets using quantitative real-time reverse transcription polymerase chain reaction.

Results

A total of 37,684 lncRNAs were obtained, and 10 lncRNAs were selected based on the criteria (P < 0.05 and |log2FoldChange| ≥1). Serum EV lncRNA RMRP, RPPH1, and linc-ROR were significantly higher in patients with GC than in those with chronic gastritis, atypical hyperplasia, or healthy control (all P < 0.05). Three lncRNAs were also significantly correlated with tumor diameter, lymphatic metastasis, distal metastasis, and TNM stage (all P < 0.05). The area under the curve (AUC) values for lncRNA RMRP, RPPH1, and linc-ROR were 0.727, 0.774, and 0.811, respectively. Corresponding sensitivity and specificity were 63.4% and 85.4%, 50.7% and 89.6%, and 78.5% and 66.7%. The combination of these three lncRNAs with carcinoembryonic antigen (CEA) yielded an AUC of 0.909, with a sensitivity and specificity of 83.3% each. Furthermore, high EV linc-ROR and RMRP expression levels were associated with worse disease-free survival and overall survival (OS). Univariate and multivariate Cox regression analyses confirmed that linc-ROR was the only independent prognostic factor for GC. Finally, the lncRNA-miRNA-mRNA network showed that three lncRNAs were predicted to interact with 15 miRNAs and 69 mRNAs. In addition, lncRNA RMRP and linc-ROR were correlated with immune cell infiltration, including neutrophils, central memory CD4 T cells, macrophage, and natural kill T cells.

Conclusion

EV lncRNAs are prospective biomarker and correlated with immune cell infiltration in GC. It provides a foundation for the development of serum EV-targeted novel biomarkers and immunotherapy targets of GC.

RNA-seq profiling identified a three-lncRNA panel in serum as potential biomarker for muscle-invasive bladder cancer

Background

Preoperative determination of muscular infiltration is crucial for appropriate treatment planning in patients with muscle-invasive bladder cancer (MIBC). We aimed to explore early diagnostic biomarkers in serum for MIBC in this study.

Methods

The expression profiles of long noncoding RNA (lncRNA) were initially screened by high-throughput sequencing and evaluation of potential lncRNAs were conducted by two phases of RT-qPCR assays using serum samples from 190 patients with MIBC and 190 non-muscle-invasive BC (NMIBC) patients. Multivariate logistic regression analysis was applied to establish a diagnostic signature with high accuracy and Fagan's nomogram was plotted to promote clinical application. Bioinformatics analysis was used to determine the potential miRNA-mRNA binding of candidate lncRNAs.

Results

We identified three differentially expressed lncRNAs (LINC00565, LINC00592 and NDUFA6-AS1) and established a 3-lncRNA panel which demonstrated high diagnostic accuracy for MIBC with an AUC of 0.903 (95% CI: 0.850-0.942) and 0.875 (95% CI: 0.802-0.928) in the training and validation set. Moreover, construction and assessment of Fagan'nomogram demonstrated that the 3-lncRNA panel could exhibit practical and helpful values for clinical use. Finally, a network map based on LINC00565 was constructed and we found that the expression of miR-143-5p and miR-4516 were significantly correlated with LINC00565 in MIBC.

Conclusion

Our findings indicated that the constructed 3-lncRNA panel in serum showed favorable diagnostic capacity and might serve as promising non-invasive biomarkers in the early diagnosis of MIBC.

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.

The Potential of the lncRNAs ADAMTSL4-AS1, AC067931 and SOCS2-AS1 in Peripheral Blood Mononuclear Cells as Novel Diagnostic Biomarkers for Hepatitis B Virus-Associated Hepatocellular Carcinoma

Purpose

Long noncoding RNAs (lncRNAs) might be closely associated with hepatocellular carcinoma (HCC) progression and could serve as diagnostic and prognostic markers. This study aimed to investigate lncRNA-based diagnostic biomarkers for hepatitis B virus (HBV)-associated HCC.

Materials and Methods

High-throughput transcriptome sequencing was conducted on the liver tissues of 15 patients with HBV-associated liver diseases (5 with chronic hepatitis B [CHB], 5 with liver cirrhosis [LC], and 5 with HCC). Quantitative real-time polymerase chain reaction (qRT-PCR) was used to analyze lncRNA expressions. Potential diagnostic performance for HBV-associated HCC screening was evaluated.

Results

Through trend analysis and functional analysis, we found that 8 lncRNAs were gradually upregulated and 1 lncRNA was progressively downregulated by regulation of target mRNAs and downstream HCC-associated signaling pathways. The validation of dysregulated lncRNAs in peripheral blood mononuclear cells (PBMCs) and HCC tissues by qRT-PCR revealed that ADAMTSL4-AS1, SOCS2-AS1, and AC067931 were significantly increased in HCC compared with CHB and cirrhosis. Moreover, differentially expressed lncRNAs were aberrantly elevated in Huh7, Hep3B, HepG2, and HepG2.215 cells compared with LX2 cells. Furthermore, ADAMTSL4-AS1, SOCS2-AS1, and AC067931 were identified as novel biomarkers for HBV-associated HCC. For distinguishing HCC from CHB, ADAMTSL4-AS1, AC067931, and SOCS2-AS1 combined with alpha-fetoprotein (AFP) had an area under the curve (AUC) of 0.945 (sensitivity, 83.9%; specificity, 89.8%). Similarly, for distinguishing HCC from LC, this combination had an AUC of 0.871 (sensitivity, 91.1%; specificity, 68.2%). Furthermore, this combination showed the highest diagnostic ability to distinguish HCC from CHB and LC (AUC, 0.905; sensitivity, 91.1%; specificity, 75.3%). In particular, this combination identified AFP-negative (AFP < 20 ng/mL) (AUC = 0.814), small (AUC = 0.909), and early stage (AUC = 0.863) tumors.

Conclusion

ADAMTSL4-AS1, SOCS2-AS1, and AC067931 combined with AFP in PBMCs may serve as a noninvasive diagnostic biomarker for HBV-associated HCC, especially AFP-negative, small, and early stage HCC.

Battle of the biopsies: Role of tissue and liquid biopsy in hepatocellular carcinoma

The diagnosis and management of hepatocellular carcinoma (HCC) have improved significantly in recent years. With the introduction of immunotherapy-based combination therapy, there has been a notable expansion in treatment options for patients with unresectable HCC. Simultaneously, innovative molecular tests for early detection and management of HCC are emerging. This progress prompts a key question: as liquid biopsy techniques rise in prominence, will they replace traditional tissue biopsies, or will both techniques remain relevant? Given the ongoing challenges of early HCC detection, including issues with ultrasound sensitivity, accessibility, and patient adherence to surveillance, the evolution of diagnostic techniques is more relevant than ever. Furthermore, the accurate stratification of HCC is limited by the absence of reliable biomarkers which can predict response to therapies. While the advantages of molecular diagnostics are evident, their potential has not yet been fully harnessed, largely because tissue biopsies are not routinely performed for HCC. Liquid biopsies, analysing components such as circulating tumour cells, DNA, and extracellular vesicles, provide a promising alternative, though they are still associated with challenges related to sensitivity, cost, and accessibility. The early results from multi-analyte liquid biopsy panels are promising and suggest they could play a transformative role in HCC detection and management; however, comprehensive clinical validation is still ongoing. In this review, we explore the challenges and potential of both tissue and liquid biopsy, highlighting that these diagnostic methods, while distinct in their approaches, are set to jointly reshape the future of HCC management.

Combinatorial Gene Expression Profiling of Serum HULC, HOTAIR, and UCA1 lncRNAs to Differentiate Hepatocellular Carcinoma from Liver Diseases: A Systematic Review and Meta-Analysis

Hepatocellular carcinoma (HCC) presents a significant global health challenge due to limited early detection methods, primarily relying on conventional approaches like imaging and alpha-fetoprotein (AFP). Although non-coding RNAs (ncRNAs) show promise as potential biomarkers in HCC, their true utility remains uncertain. We conducted a comprehensive review of 76 articles, analyzing 88 circulating lncRNAs in 6426 HCC patients. However, the lack of a standardized workflow protocol has hampered holistic comparisons across the literature. Consequently, we herein confined our meta-analysis to only a subset of these lncRNAs. The combined analysis of serum highly upregulated in liver cancer (HULC) gene expression with homeobox transcript antisense intergenic RNA (HOTAIR) and urothelial carcinoma-associated 1 (UCA1) demonstrated markedly enhanced sensitivity and specificity in diagnostic capability compared to traditional biomarkers or other ncRNAs. These findings could have substantial implications for the early diagnosis and tailored treatment of HCC.

LINC00665: A Promising Biomarker in Gastrointestinal Tumors

An increasing volume of studies has reported that long non-codingRNAs (lncRNAs) are involved in the carcinogenesis of many different cancers. Especially in gastrointestinal tumors, lncRNAs are found to participate in various physiological and pathological processes. LncRNAs can regulate gene expression at multiple levels, including transcriptional, post-transcription, translational, and post-translational levels. Long intergenic non-protein coding RNA 665(LINC00665), a novel cancer-related lncRNA, is frequently dysregulated in multiple gastrointestinal tumors, including gastric and colorectal cancers, hepatocellular carcinoma, and so on. In this review, we analyzed the expression and prognostic value of LINC00665 in human gastrointestinal tumors, systematically summarized the current literature about the clinical significance of this lncRNA, and explored the regulatory mechanisms of LINC00665 as a competing endogenous RNA (ceRNA) in tumor progression. Consequently, we concluded that LINC00665 might act as a prognostic biomarker and a potential target for gastrointestinal tumor diagnosis and treatment.

Long non coding RNA, C8orf49, a novel diagnostic and prognostic biomarker, enhances PTEN/FZD4-mediated cell growth and metastasis by sponging miR-1323 in endometriosis

Lack of sensitive biomarkers in the early stages of endometriosis (EMs) results in delayed diagnosis and intervention. Long non-coding RNAs (lncRNAs) have prognostic and diagnostic values in various diseases. However, the prognostic and diagnostic effects of lncRNAs on EMs have rarely been discussed in EMs. In this study, we found that lncRNA C8orf49 was stably overexpressed in EMs tissues/plasma, and its expression greatly influenced dysmenorrhea (p = 2.2605E-9) and the revised American Society for Reproductive Medicine stage (p = 0.040765) of EMs. Multivariate logistic regression results revealed that C8orf49 expression was an independent risk factor for EMs [p = 6.4997E-17, 95% confidence interval (CI) = 0.000559-0.023853]. In primary endometrial stromal cells (ESCs), inhibition of C8orf49 could impede the proliferation and metastasis of ESCs. C8orf49 influenced the expression of PTEN/FZD4 by absorbing miR-1323, thus controlling ESCs activity. The results of a subcutaneous endometriosis animal model showed that the inhibition of C8orf49 restrained endometrial growth. Overall, C8orf49 functioned as an activator of EMs pathogenesis via the C8orf49/miR-1323/PTEN/FZD4 axis.

Decoding hepatocarcinogenesis from a noncoding RNAs perspective

Being a leading lethal malignancy worldwide, the pathophysiology of hepatocellular carcinoma (HCC) has gained a lot of interest. Yet, underlying mechanistic basis of the liver tumorigenesis is poorly understood. The role of some coding genes and their respective translated proteins, then later on, some noncoding RNAs (ncRNAs) such as microRNAs have been extensively studied in context of HCC pathophysiology; however, the implication of long noncoding RNAs (lncRNAs) and circular RNAs (circRNAs) in HCC is indeed less investigated. As a subclass of the ncRNAs which has been elusive for long time ago, lncRNAs was found to be involved in plentiful cellular functions such as DNA, RNA, and proteins regulation. Hence, it is undisputed that lncRNAs dysregulation profoundly contributes to HCC via diverse etiologies. Accordingly, lncRNAs represent a hot research topic that requires prime focus in HCC. In this review, the authors discuss breakthrough discoveries involving lncRNAs and circRNAs dysregulation that have contributed to the contemporary concepts of HCC pathophysiology and how these concepts could be leveraged as potential novel diagnostic and prognostic HCC biomarkers. Further, this review article sheds light on future trends, thereby discussing the pathological roles of lncRNAs and circRNAs in HCC proliferation, migration, and epithelial-to-mesenchymal transition. Along this line of reasoning, future recommendations of how these targets could be exploited to achieve effective HCC-related drug development is highlighted.

Research progress of circulating non-coding RNA in diagnosis and treatment of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a highly malignant tumor that carries a significant risk of morbidity and mortality. This type of cancer is prevalent in Asia due to the widespread presence of risk factors. Unfortunately, HCC often goes undetected until it has reached an advanced stage, making early detection and treatment critical for better outcomes. Alpha-fetoprotein (AFP) is commonly used in clinical practice for diagnosing HCC, but its sensitivity and specificity are limited. While surgery and liver transplantation are the main radical treatments, drug therapy and local interventions are better options for patients with advanced HCC. Accurately assessing treatment efficacy and adjusting plans in a timely manner can significantly improve the prognosis of HCC. Non-coding RNA gene transcription products cannot participate in protein production, but they can regulate gene expression and protein function through the regulation of transcription and translation processes. These non-coding RNAs have been found to be associated with tumor development in various types of tumors. Noncoding RNA released by tumor or blood cells can circulate in the blood and serve as a biomarker for diagnosis, prognosis, and efficacy assessment. This article explores the unique role of circulating noncoding RNA in HCC from various perspectives.

Cuproptosis-related lncRNA signatures predict prognosis and immune relevance of kidney renal papillary cell carcinoma

Kidney renal papillary cell carcinoma (KIRP) has a high mortality rate and a poor prognosis. Cu concentrations differed significantly between renal cancer tissues and adjacent normal tissues. Cuproptosis is a newly identified cell death. Long non-coding RNAs (lncRNAs) play a crucial role in the progression of KIRP. In this study, we focused on constructing and validating cuproptosis-related lncRNA signatures to predict the prognosis of KIRP patients and their immune correlation. We created prognosis models using Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) algorithm. We found that patients in the high-risk group had poorer overall survival (OS) and progression-free survival (PFS) and higher mortality. Risk score and stage are prognosis factors independent of other clinical features. Kaplan-Meier analysis, receiver operating characteristic (ROC) curves, and C-index curves showed that cuproptosis-related lncRNA signatures could more accurately predict the prognosis of patients. Functional enrichment analysis suggests that the function of differentially expressed genes (DEGs) is associated with KIRP development and immunity. In immune-related function analysis, we found a significant difference in parainflammation responses between high-risk and low-risk groups. The mutation frequencies of TTN, MET, KMT2C, PKHD1, SETD2, and KMT2D genes in the high-risk group were higher than those in the low-risk group, but the mutation frequencies of MUC16, KIAA109, CUBN, USH2A, DNAH8 and HERC2 genes were significantly lower than those in the low-risk group. Survival analysis of tumor mutation burden (TMB) and combined TMB-risk showed better OS in patients with high TMB. Immune infiltration and immune checkpoint analysis assessed the immune association of six high mutation frequency genes (TTN, MET, KMT2C, PKHD1, SETD2, and KMT2D) with KIRP. Finally, we performed a drug sensitivity analysis and screened 15 potential drugs that differed between high-risk and low-risk patients. In this study, we constructed and validated cuproptosis-related lncRNA signatures that can more accurately predict the prognosis of KIRP patients and provide new potential therapeutic targets and prognosis markers for KIRP patients.