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

A novel approach for in vivo DNA footprinting using short double-stranded cell-free DNA from plasma

Here, we present a method for enrichment of double-stranded cfDNA with an average length of ∼40 bp from cfDNA for high-throughput DNA sequencing. This class of cfDNA is enriched at gene promoters and binding sites of transcription factors or structural DNA-binding proteins, so that a genome-wide DNA footprint is directly captured from liquid biopsies. In short double-stranded cfDNA from healthy individuals, we find significant enrichment of 203 transcription factor motifs. Additionally, short double-stranded cfDNA signals at specific genomic regions correlate negatively with DNA methylation, positively with H3K4me3 histone modifications and gene transcription. The diagnostic potential of short double-stranded cell-free DNA (cfDNA) in blood plasma has not yet been recognized. When comparing short double-stranded cfDNA from patient samples of pancreatic ductal adenocarcinoma with colorectal carcinoma or septic with postoperative controls, we identify 136 and 241 differentially enriched loci, respectively. Using these differentially enriched loci, the disease types can be clearly distinguished by principal component analysis, demonstrating the diagnostic potential of short double-stranded cfDNA signals as a new class of biomarkers for liquid biopsies.

SNORD3A Regulates STING Transcription to Promote Ferroptosis in Acute Kidney Injury

Acute kidney injury (AKI) signifies a sudden and prolonged decline in kidney function characterized by tubular cell death and interstitial inflammation. Small nucleolar RNAs (snoRNAs) play pivotal roles in oxidative stress and inflammation, and may play an important role in the AKI process, which remains elusive. an elevated expression of Snord3a is revealed in renal tubules in response to AKI and demonstrates that Snord3a deficiency alleviates renal injury in AKI mouse models. Notably, the deficiency of Snord3a exhibits a mitigating effect on the stimulator of interferon genes (STING)-associated ferroptosis phenotypes and the progression of tubular injury. Mechanistically, Snord3a is shown to regulate the STING signaling axis via promoting STING gene transcription; administration of Snord3a antisense oligonucleotides establishes a significant therapeutic advantage in AKI mouse models. Together, the findings elucidate the transcription regulation mechanism of STING and the crucial roles of the Snord3a-STING axis in ferroptosis during AKI, underscoring Snord3a as a potential prognostic and therapeutic target for AKI.

Exploring Unique Extracellular Vesicles Associated Signatures: Prognostic Insights, Immune Microenvironment Dynamics, and Therapeutic Responses in Pancreatic Adenocarcinoma

Extracellular vesicles play an important role in the progression of pancreatic adenocarcinoma (PAAD) through the transfer of proteins, mRNAs, and long noncoding RNAs (lncRNAs). However, the intricate interplay between extracellular vesicles-related lncRNAs and the tumor microenvironment (TME) remains poorly elucidated. Consequently, our investigation aimed to delineate the association between extracellular vesicles-related lncRNAs and the PAAD microenvironment. Initially, we identified differentially expressed lncRNAs (DELs) from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) project datasets. Subsequently, we validated the expression of these DELs within extracellular vesicles and assessed their prognostic implications in PAAD using the GSE133684 and TCGA datasets. Multiomics data were analyzed comprehensively, including genomic landscape, functional annotation, immune profiles, and therapeutic responses. Differential expression of selected lncRNAs in both cellular and exosomal fractions of PAAD was further confirmed through quantitative polymerase chain reaction (qPCR). Eight DELs were identified from TCGA and GTEx datasets, and two exosomal lncRNAs exhibited a significant correlation with overall survival, warranting further investigation. Specifically, elevated expression of LINC00996 correlated positively with immune infiltration and enhanced response to immunotherapy. Conversely, heightened expression of TRHED-AS1 was associated with compromised immune cell infiltration and diminished responsiveness to immunotherapy. Our study establishes a compelling link between two extracellular vesicles-related gene signatures, prognosis, and immune infiltration in PAAD. Notably, these signatures serve as robust prognostic indicators for PAAD patients, offering valuable insights for the strategic selection of immunotherapeutic interventions.

Utilizing nullomers in cell-free RNA for early cancer detection

Early detection of cancer can significantly improve patient outcomes; however, sensitive and highly specific biomarkers for cancer detection are currently missing. Nullomers are the shortest sequences that are absent from the human genome but can emerge due to somatic mutations in cancer. We examine over 10,000 whole exome sequencing matched tumor-normal samples to characterize nullomer emergence across exonic regions of the genome. We also identify nullomer emerging mutational hotspots within tumor genes. Finally, we provide evidence for the identification of nullomers in cell-free RNA from peripheral blood samples, enabling detection of multiple tumor types. We show multiple tumor classification models with an AUC greater than 0.9, including a hepatocellular carcinoma classifier with an AUC greater than 0.99.

A Comprehensive Review on Circulating cfRNA in Plasma: Implications for Disease Diagnosis and Beyond

Circulating cfRNA in plasma has emerged as a fascinating area of research with potential applications in disease diagnosis, monitoring, and personalized medicine. Circulating RNA sequencing technology allows for the non-invasive collection of important information about the expression of target genes, eliminating the need for biopsies. This comprehensive review aims to provide a detailed overview of the current knowledge and advancements in the study of plasma cfRNA, focusing on its diverse landscape and biological functions, detection methods, its diagnostic and prognostic potential in various diseases, challenges, and future perspectives.

Advances in applications of artificial intelligence algorithms for cancer-related miRNA research

MiRNAs are a class of small non-coding RNAs, which regulate gene expression post-transcriptionally by partial complementary base pairing. Aberrant miRNA expressions have been reported in tumor tissues and peripheral blood of cancer patients. In recent years, artificial intelligence algorithms such as machine learning and deep learning have been widely used in bioinformatic research. Compared to traditional bioinformatic tools, miRNA target prediction tools based on artificial intelligence algorithms have higher accuracy, and can successfully predict subcellular localization and redistribution of miRNAs to deepen our understanding. Additionally, the construction of clinical models based on artificial intelligence algorithms could significantly improve the mining efficiency of miRNA used as biomarkers. In this article, we summarize recent development of bioinformatic miRNA tools based on artificial intelligence algorithms, focusing on the potential of machine learning and deep learning in cancer-related miRNA research.

Extracellular vesicles as a new frontier of diagnostic biomarkers in osteosarcoma diseases: a bibliometric and visualized study

The use of liquid biopsy in cancer research has grown exponentially, offering potential for early detection, treatment stratification, and monitoring residual disease and recurrence. Exosomes, released by cancer cells, contain tumor-derived materials and are stable in biofluids, making them valuable biomarkers for clinical evaluation. Bibliometric research on osteosarcoma (OS) and exosome-derived diagnostic biomarkers is scarce. Therefore, we aimed to conduct a bibliometric evaluation of studies on OS and exosome-derived biomarkers. Using the Web of Science Core Collection database, Microsoft Excel, the R "Bibliometrix" package, CiteSpace, and VOSviewer software, quantitative analyses of the country, author, annual publications, journals, institutions, and keywords of studies on exosome-derived biomarkers for OS from 1995 to 2023 were performed. High-quality records (average citation rate ≥ 10/year) were filtered. The corresponding authors were mainly from China, the USA, Australia, and Canada. The University of Kansas Medical Center, National Cancer Center, Japan, and University of Kansas were major institutions, with limited cooperation reported by the University of Kansas Medical Center. Keyword analysis revealed a shift from cancer progression to mesenchymal stem cells, exosome expression, biogenesis, and prognostic biomarkers. Qualitative analysis highlighted exosome cargo, including miRNAs, circRNAs, lncRNAs, and proteins, as potential diagnostic OS biomarkers. This research emphasizes the rapid enhancement of exosomes as a diagnostic frontier, offering guidance for the clinical application of exosome-based liquid biopsy in OS, contributing to the evolving landscape of cancer diagnosis.

Uncovering the link between human endogenous retroviruses, inflammatory pathways, and gastric cancer development

BACKGROUND

Endogenous retroviruses, previously deemed "junk" DNA, have gained attention in recent scientific studies. These inherited genomic elements are now recognized for their potential roles in diseases, especially cancer, highlighting their value as potential diagnostic or therapeutic targets.

OBJECTIVE

This research aims to explore the association between human endogenous retroviruses (HERV) and gastric cancer, focusing on discerning HERV expression patterns and understanding their implications in gastric cancer pathology.

METHODS

A quantitative analysis of HERV expression was conducted, employing Support Vector Machine (SVM) and AdaBoost algorithms to identify discriminative HERVs. The co-regulation network between protein-coding genes and HERVs was constructed using the Weighted Gene Co-expression Network Analysis (WGCNA).

RESULTS

Three distinct HERVs (LTR16A|72|451, LTR91|636|874, LTR27D|87|222) were identified as significantly different. Strong correlations were found between HERVs, and gene sets enriched in the inflammatory pathway.

CONCLUSIONS

HERVs appear to influence abnormal inflammatory responses, suggesting a pivotal role in gastric cancer development.

Cell-free multi-omics analysis reveals potential biomarkers in gastrointestinal cancer patients' blood

During cancer progression, tumorigenic and immune signals are spread through circulating molecules, such as cell-free DNA (cfDNA) and cell-free RNA (cfRNA) in the blood. So far, they have not been comprehensively investigated in gastrointestinal cancers. Here, we profile 4 categories of cell-free omics data from patients with colorectal cancer and patients with stomach adenocarcinoma and then assay 15 types of genomic, epigenomic, and transcriptomic variations. We find that multi-omics data are more appropriate for detection of cancer genes compared with single-omics data. In particular, cfRNAs are more sensitive and informative than cfDNAs in terms of detection rate, enriched functional pathways, etc. Moreover, we identify several peripheral immune signatures that are suppressed in patients with cancer. Specifically, we establish a γδ-T cell score and a cancer-associated-fibroblast (CAF) score, providing insights into clinical statuses like cancer stage and survival. Overall, we reveal a cell-free multi-molecular landscape that is useful for blood monitoring in personalized cancer treatment.

Identification of urinary extracellular vesicles differentially expressed RNAs in diabetic nephropathy via whole-transcriptome integrated analysis

BACKGROUND

Diabetic nephropathy (DN) is a common systemic microvascular complication of diabetes and a leading cause of chronic kidney disease worldwide. Urinary extracellular vesicles (uEVs), which are natural nanoscale vesicles that protect RNA from degradation, have the potential to serve as an invasive diagnostic biomarker for DN.

METHODS

We enrolled 24 participants, including twelve with renal biopsy-proven T2DN and twelve with T2DM, and isolated uEVs using ultracentrifugation. We performed microarrays for mRNAs, lncRNAs, and circRNAs in parallel, and Next-Generation Sequencing for miRNAs. Differentially expressed RNAs (DE-RNAs) were subjected to CIBERSORTx, ssGSEA analysis, GO enrichment, PPI network analysis, and construction of the lncRNA/circRNA-miRNA-mRNA regulatory network. Candidate genes and potential biomarker RNAs were validated using databases and machine learning models.

RESULTS

A total of 1684 mRNAs, 126 lncRNAs, 123 circRNAs and 66 miRNAs were found in uEVs in T2DN samples compared with T2DM. CIBERSORTx revealed the involvement of uEVs in immune activity and ssGSEA explored possible cell or tissue sources of uEVs. A ceRNA co-expression and regulation relationship network was constructed. Candidate genes MYO1C and SP100 mRNA were confirmed to be expressed in the kidney using Nephroseq database, scRNA-seq dataset, and Human Protein Atlas database. We further selected 2 circRNAs, 2 miRNAs, and 2 lncRNAs from WGCNAs and ceRNAs and demonstrated their efficacy as potential diagnostic biomarkers for T2DN using machine learning algorithms.

CONCLUSIONS

This study reported, for the first time, the whole-transcriptome genetic resources found in urine extracellular vesicles of T2DN patients. The results provide additional support for the possible interactions, and regulators between RNAs from uEVs themselves and as potential biomarkers in DN.

Role of Non-Coding RNAs in Hepatocellular Carcinoma Progression: From Classic to Novel Clinicopathogenetic Implications

Hepatocellular carcinoma (HCC) is a predominant malignancy with increasing incidences and mortalities worldwide. In Western countries, the progressive affirmation of Non-alcoholic Fatty Liver Disease (NAFLD) as the main chronic liver disorder in which HCC occurrence is appreciable even in non-cirrhotic stages, constitutes a real health emergency. In light of this, a further comprehension of molecular pathways supporting HCC onset and progression represents a current research challenge to achieve more tailored prognostic models and appropriate therapeutic approaches. RNA non-coding transcripts (ncRNAs) are involved in the regulation of several cancer-related processes, including HCC. When dysregulated, these molecules, conventionally classified as "small ncRNAs" (sncRNAs) and "long ncRNAs" (lncRNAs) have been reported to markedly influence HCC-related progression mechanisms. In this review, we describe the main dysregulated ncRNAs and the relative molecular pathways involved in HCC progression, analyzing their implications in certain etiologically related contexts, and their applicability in clinical practice as novel diagnostic, prognostic, and therapeutic tools. Finally, given the growing evidence supporting the immune system response, the oxidative stress-regulated mechanisms, and the gut microbiota composition as relevant emerging elements mutually influencing liver-cancerogenesis processes, we investigate the relationship of ncRNAs with this triad, shedding light on novel pathogenetic frontiers of HCC progression.

Decoding cell-type contributions to the cfRNA transcriptomic landscape of liver cancer

BACKGROUND

Liquid biopsy, particularly cell-free RNA (cfRNA), has emerged as a promising non-invasive diagnostic tool for various diseases, including cancer, due to its accessibility and the wealth of information it provides. A key area of interest is the composition and cellular origin of cfRNA in the blood and the alterations in the cfRNA transcriptomic landscape during carcinogenesis. Investigating these changes can offer insights into the manifestations of tissue alterations in the blood, potentially leading to more effective diagnostic strategies. However, the consistency of these findings across different studies and their clinical utility remains to be fully elucidated, highlighting the need for further research in this area.

RESULTS

In this study, we analyzed over 350 blood samples from four distinct studies, investigating the cell type contributions to the cfRNA transcriptomic landscape in liver cancer. We found that an increase in hepatocyte proportions in the blood is a consistent feature across most studies and can be effectively utilized for classifying cancer and healthy samples. Moreover, our analysis revealed that in addition to hepatocytes, liver endothelial cell signatures are also prominent in the observed changes. By comparing the classification performance of cellular proportions to established markers, we demonstrated that cellular proportions could distinguish cancer from healthy samples as effectively as existing markers and can even enhance classification when used in combination with these markers.

CONCLUSIONS

Our comprehensive analysis of liver cell-type composition changes in blood revealed robust effects that help classify cancer from healthy samples. This is especially noteworthy, considering the heterogeneous nature of datasets and the etiological distinctions of samples. Furthermore, the observed differences in results across studies underscore the importance of integrative and comparative approaches in the future research to determine the consistency and robustness of findings. This study contributes to the understanding of cfRNA composition in liver cancer and highlights the potential of cellular deconvolution in liquid biopsy.

lhCLIP reveals the in vivo RNA-RNA interactions recognized by hnRNPK

RNA-RNA interactions play a crucial role in regulating gene expression and various biological processes, but identifying these interactions on a transcriptomic scale remains a challenge. To address this, we have developed a new biochemical technique called pCp-biotin labelled RNA hybrid and ultraviolet crosslinking and immunoprecipitation (lhCLIP) that enables the transcriptome-wide identification of intra- and intermolecular RNA-RNA interactions mediated by a specific RNA-binding protein (RBP). Using lhCLIP, we have uncovered a diverse landscape of intermolecular RNA interactions recognized by hnRNPK in human cells, involving all major classes of noncoding RNAs (ncRNAs) and mRNA. Notably, hnRNPK selectively binds with snRNA U4, U11, and U12, and shapes the secondary structure of these snRNAs, which may impact RNA splicing. Our study demonstrates the potential of lhCLIP as a user-friendly and widely applicable method for discovering RNA-RNA interactions mediated by a particular protein of interest and provides a valuable tool for further investigating the role of RBPs in gene expression and biological processes.

U3 snoRNA-mediated degradation of ZBTB7A regulates aerobic glycolysis in isocitrate dehydrogenase 1 wild-type glioblastoma cells

AIMS

The isocitrate dehydrogenase (IDH) phenotype is associated with reprogrammed energy metabolism in glioblastoma (GBM) cells. Small nucleolar RNAs (snoRNAs) are known to exert an important regulatory role in the energy metabolism of tumor cells. The purpose of this study was to investigate the role of C/D box snoRNA U3 and transcription factor zinc finger and BTB domain-containing 7A (ZBTB7A) in the regulation of aerobic glycolysis and the proliferative capacity of IDH1 wild-type (IDH1WT ) GBM cells.

METHODS

Quantitative reverse transcription PCR and western blot assays were utilized to detect snoRNA U3 and ZBTB7A expression. U3 promoter methylation status was analyzed via bisulfite sequencing and methylation-specific PCR. Seahorse XF glycolysis stress assays, lactate production and glucose consumption measurement assays, and cell viability assays were utilized to detect glycolysis and proliferation of IDH1WT GBM cells.

RESULTS

We found that hypomethylation of the CpG island in the promoter region of U3 led to the upregulation of U3 expression in IDH1WT GBM cells, and the knockdown of U3 suppressed aerobic glycolysis and the proliferation ability of IDH1WT GBM cells. We found that small nucleolar-derived RNA (sdRNA) U3-miR, a small fragment produced by U3, was able to bind to the ZBTB4 3'UTR region and reduce ZBTB7A mRNA stability, thereby downregulating ZBTB7A protein expression. Furthermore, ZBTB7A transcriptionally inhibited the expression of hexokinase 2 (HK2) and lactate dehydrogenase A (LDHA), which are key enzymes of aerobic glycolysis, by directly binding to the HK2 and LDHA promoter regions, thereby forming the U3/ZBTB7A/HK2 LDHA pathway that regulates aerobic glycolysis and proliferation of IDH1WT GBM cells.

CONCLUSION

U3 enhances aerobic glycolysis and proliferation in IDH1WT GBM cells via the U3/ZBTB7A/HK2 LDHA axis.

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.

Differentially expressed non-coding RNAs and their regulatory networks in liver cancer

The vast majority of human transcriptome is represented by various types of small RNAs with little or no protein-coding capability referred to as non-coding RNAs (ncRNAs). Functional ncRNAs include microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs), which are expressed at very low, but stable and reproducible levels in a variety of cell types. ncRNAs regulate gene expression due to miRNA capability of complementary base pairing with mRNAs, whereas lncRNAs and circRNAs can sponge miRNAs off their target mRNAs to act as competitive endogenous RNAs (ceRNAs). Each miRNA can target multiple mRNAs and a single mRNA can interact with several miRNAs, thereby creating miRNA-mRNA, lncRNA-miRNA-mRNA, and circRNA-miRNA-mRNA regulatory networks. Over the past few years, a variety of differentially expressed miRNAs, lncRNAs, and circRNAs (DEMs, DELs, and DECs, respectively) have been linked to cancer pathogenesis. They can exert both oncogenic and tumor suppressor roles. In this review, we discuss the recent advancements in uncovering the roles of DEMs, DELs, and DECs and their networks in aberrant cell signaling, cell cycle, transcription, angiogenesis, and apoptosis, as well as tumor microenvironment remodeling and metabolic reprogramming during hepatocarcinogenesis. We highlight the potential and challenges in the use of differentially expressed ncRNAs as biomarkers for liver cancer diagnosis and prognosis.

Machine Learning Identifies a Signature of Nine Exosomal RNAs That Predicts Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide. Although alpha fetoprotein (AFP) remains a commonly used serological marker of HCC, the sensitivity and specificity of AFP in detecting HCC is often limited. Exosomal RNA has emerged as a promising diagnostic tool for various cancers, but its use in HCC detection has yet to be fully explored. Here, we employed Machine Learning on 114,602 exosomal RNAs to identify a signature that can predict HCC. The exosomal expression data of 118 HCC patients and 112 healthy individuals were stratified split into Training, Validation and Unseen Test datasets. Feature selection was then performed on the initial training dataset using permutation importance, and the predictive performance of the selected features were tested on the validation dataset using Support Vector Machine (SVM) Classifier. A minimum of nine features were identified to be predictive of HCC and these nine features were then evaluated across six different models in an unseen test set. These features, mainly in the immune, platelet/neutrophil and cytoskeletal pathways, exhibited good predictive performance with ROC-AUC from 0.79-0.88 in the unseen test set. Hence, these nine exosomal RNAs have potential to be clinically useful minimally invasive biomarkers for HCC.

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).

Circular RNAs in ferroptosis: regulation mechanism and potential clinical application in disease

Ferroptosis, an iron-dependent non-apoptotic form of cell death, is reportedly involved in the pathogenesis of various diseases, particularly tumors, organ injury, and degenerative pathologies. Several signaling molecules and pathways have been found to be involved in the regulation of ferroptosis, including polyunsaturated fatty acid peroxidation, glutathione/glutathione peroxidase 4, the cysteine/glutamate antiporter system Xc-, ferroptosis suppressor protein 1/ubiquinone, and iron metabolism. An increasing amount of evidence suggests that circular RNAs (circRNAs), which have a stable circular structure, play important regulatory roles in the ferroptosis pathways that contribute to disease progression. Hence, ferroptosis-inhibiting and ferroptosis-stimulating circRNAs have potential as novel diagnostic markers or therapeutic targets for cancers, infarctions, organ injuries, and diabetes complications linked to ferroptosis. In this review, we summarize the roles that circRNAs play in the molecular mechanisms and regulatory networks of ferroptosis and their potential clinical applications in ferroptosis-related diseases. This review furthers our understanding of the roles of ferroptosis-related circRNAs and provides new perspectives on ferroptosis regulation and new directions for the diagnosis, treatment, and prognosis of ferroptosis-related diseases.

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.

Advances of multi-omics applications in hepatic precancerous lesions and hepatocellular carcinoma: The role of extracellular vesicles

Due to the lack of distinct early symptoms and specific biomarkers, most patients with hepatocellular carcinoma (HCC) are usually diagnosed at advanced stages, rendering the treatment ineffective and useless. Therefore, recognition of the malady at precancerous lesions and early stages is particularly important for improving patient outcomes. The interest in extracellular vesicles (EVs) has been growing in recent years with the accumulating knowledge of their multiple cargoes and related multipotent roles in the modulation of immune response and tumor progression. By virtue of the rapid advancement of high-throughput techniques, multiple omics, including genomics/transcriptomics, proteomics, and metabolomics/lipidomics, have been widely integrated to analyze the role of EVs. Comprehensive analysis of multi-omics data will provide useful insights for discovery of new biomarkers and identification of therapeutic targets. Here, we review the attainment of multi-omics analysis to the finding of the potential role of EVs in early diagnosis and the immunotherapy in HCC.

A novel cuproptosis-related lncRNA signature predicts prognosis and therapeutic response in bladder cancer

Bladder cancer (BC) ranks the tenth in the incidence of global tumor epidemiology. LncRNAs and cuproptosis were discovered to regulate the cell death. Herein, we downloaded transcriptome profiling, mutational data, and clinical data on patients from The Cancer Genome Atlas (TCGA). High- and low-risk BC patients were categorized. Three CRLs (AL590428.1, AL138756.1 and GUSBP11) were taken into prognostic signature through least absolute shrinkage and selection operator (LASSO) Cox regression. Worse OS and PFS were shown in high-risk group (p < 0.05). ROC, independent prognostic analyses, nomogram and C-index were predicted via CRLs. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis indicated IncRNAs play a biological role in BC progression. Immune-related functions showed the high-risk group received more benefit from immunotherapy and had stronger immune responses, and the overall survival was better (p < 0.05). Finally, a more effective outcome (p < 0.05) was found from clinical immunotherapy via the TIDE algorithm and many potential anti-tumor drugs were identified. In our study, the cuproptosis-related signature provided a novel tool to predict the prognosis in BC patients accurately and provided a novel strategy for clinical immunotherapy and clinical applications.

Identification of a Novel N7-Methylguanosine-Related LncRNA Signature Predicts the Prognosis of Hepatocellular Carcinoma and Experiment Verification

(1) Background: It is well-known that long non-coding RNAs (lncRNAs) and N7-methylguanosine (m7G) contribute to hepatocellular carcinoma (HCC) progression. However, it remains unclear whether lncRNAs regulating m7G modification could predict HCC prognosis. Thus, we sought to explore the prognostic implications of m7G-related lncRNAs in HCC patients. (2) Methods: Prognostic M7G-related lncRNAs obtained from The Cancer Genome Atlas (TCGA) database were screened by co-expression analysis and univariate Cox regression analysis. Next, the m7G-related lncRNA signature (m7GRLSig) was conducted by Least absolute shrinkage and selection operator (LASSO) Cox regression and multivariate Cox regression analysis. Kaplan-Meier analysis and time-dependent receiver operating characteristics (ROC) assessed the prognostic abilities of our signature. Univariate and multivariate Cox regression, nomogram, and principal component analysis (PCA) were conducted to evaluate our signature. Subsequently, we investigated the role of m7GRLSig on the immune landscape and sensitivity to drugs in HCC patients. The potential function of lncRNAs obtained from the prognostic signature was explored by in vitro experiments. (3) Results: A novel m7GRLSig was identified using seven meaningful lncRNA (ZFPM2-AS1, AC092171.2, PIK3CD-AS2, NRAV, CASC19, HPN-AS1, AC022613.1). The m7GLPSig exhibited worse survival in the high-risk group and served as an independent prognostic factor. The m7GRLSig stratification was sensitive in assessing the immune landscape and sensitivity to drugs between the high-risk and low-risk groups. Finally, in vitro experiments confirmed that the knockdown of NRAV was accompanied by the downregulation of METTL1 during HCC progression. (4) Conclusions: The m7G-related signature is a potential predictor of HCC prognosis and contributes to individualize the effective drug treatment of HCC.

Network analysis of hepatocellular carcinoma liquid biopsies augmented by single-cell sequencing data

Liquid biopsy, the analysis of body fluids, represents a promising approach for disease diagnosis and prognosis with minimal intervention. Sequencing cell-free RNA derived from liquid biopsies has been very promising for the diagnosis of several diseases. Cancer research, in particular, has emerged as a prominent candidate since early diagnosis has been shown to be a critical determinant of disease prognosis. Although high-throughput analysis of liquid biopsies has uncovered many differentially expressed genes in the context of cancer, the functional connection between these genes is not investigated in depth. An important approach to remedy this issue is the construction of gene networks which describes the correlation patterns between different genes, thereby allowing to infer their functional organization. In this study, we aimed at characterizing extracellular transcriptome gene networks of hepatocellular carcinoma patients compared to healthy controls. Our analysis revealed a number of genes previously associated with hepatocellular carcinoma and uncovered their association network in the blood. Our study thus demonstrates the feasibility of performing gene co-expression network analysis from cell-free RNA data and its utility in studying hepatocellular carcinoma. Furthermore, we augmented cell-free RNA network analysis with single-cell RNA sequencing data which enables the contextualization of the identified network modules with cell-type specific transcriptomes from the liver.

Big Data Analysis and Application of Liver Cancer Gene Sequence Based on Second-Generation Sequencing Technology

In big data analysis with the rapid improvement of computer storage capacity and the rapid development of complex algorithms, the exponential growth of massive data has also made science and technology progress with each passing day. Based on omics data such as mRNA data, microRNA data, or DNA methylation data, this study uses traditional clustering methods such as kmeans, K-nearest neighbors, hierarchical clustering, affinity propagation, and nonnegative matrix decomposition to classify samples into categories, obtained: (1) The assumption that the attributes are independent of each other reduces the classification effect of the algorithm to a certain extent. According to the idea of multilevel grid, there is a one-to-one mapping from high-dimensional space to one-dimensional. The complexity is greatly simplified by encoding the one-dimensional grid of the hierarchical grid. The logic of the algorithm is relatively simple, and it also has a very stable classification efficiency. (2) Convert the two-dimensional representation of the data into the one-dimensional representation of the binary, realize the dimensionality reduction processing of the data, and improve the organization and storage efficiency of the data. The grid coding expresses the spatial position of the data, maintains the original organization method of the data, and does not make the abstract expression of the data object. (3) The data processing of nondiscrete and missing values provides a new opportunity for the identification of protein targets of small molecule therapy and obtains a better classification effect. (4) The comparison of the three models shows that Naive Bayes is the optimal model. Each iteration is composed of alternately expected steps and maximal steps and then identified and quantified by MS.

Multi-omics analysis revealed the role of extracellular vesicles in hepatobiliary & pancreatic tumor

Liquid biopsy is rapidly growing into a hot research field due to its unique advantages of minimal invasiveness, and extracellular vesicle (EVs) are also expected to become an important pillar in the diagnostic technology system as a newly discovered active substance carrier. More and more research has highlighted the important contribution of EVs in the progress of tumor. Molecular changes during disease progression could be detected in EVs. However, the diagnostic applications of EVs are not generally understood. Combined with the characteristics of hepatobiliary and pancreatic tumor, we summarized the recent developments in various omics analysis of EVs. Furtherly, we explored the role of EVs in the early diagnosis of hepatobiliary and pancreatic tumors by multi-omics analysis.

Iron-Based Nanoparticles Applied to Evaluate MRI Diagnosis and Treatment of Liver Cancer Treated with Apatinib

Objective: To investigate the value of iron-based nanoparticles in evaluating the magnetic resonance imaging (MRI) diagnosis and treatment of liver cancer treated with apatinib. Methods: Apatinib treatment and MRI were performed in patients with primary liver cancer. The characteristics of liver tissue sections and biodistribution in mice after injection of Fe2O3-PEG and iron oxide nanoparticles (Fe2O3-pep) were analyzed, and the MRI characteristics and magnetic resonance signals of Fe2O3-PEG and Fe2O3-pep nanoparticles were compared. Results: Fe2O3-PEG and Fe2O3-pep had little effect on the activity of human normal hepatocytes. There was no significant difference in liver tissue sections between mice injected with Fe2O3-PEG and Fe2O3-pep nanoparticles. The Fe2O3-PEG and Fe2O3-pep in the liver organs of mice were 11.3 and 9.7, which were significantly higher than those in other organs. At 12 hours and 24 hours after injection of Fe2O3-pep and Fe2O3-PEG nanoparticles, the signal at the tumor site decreased on T2WI images, the maximum contrast of magnetic resonance images was enhanced at 12 hours after injection, and the signal decrease was more significant in the group injected with Fe2O3-pep nanoparticles. Conclusion: Fe2O3-pep has higher tumor targeting and has positive application value in evaluating MRI diagnosis and treatment of liver cancer.

Effect of Transdermal Fentanyl Patch Combined with Enhanced Recovery after Surgery on the Curative Effect and Analgesic Effect of Liver Cancer

Its goal was to see how a transdermal fentanyl patch combined with accelerated recovery after surgery (ERAS) affected the treatment efficacy and analgesic effect of liver cancer, as well as to help patients with liver cancer choose the right analgesic treatment and nursing mode. 150 patients with liver cancer were divided into group A (transdermal fentanyl patch), group B (ERAS), and group C (transdermal fentanyl patch combined with ERAS). Patients in the three groups were compared in terms of pain, survival, psychological status, adverse responses, postoperative recovery, and patient satisfaction. The results showed that under different treatment and nursing methods, the number of patients with mild cancer pain in the three groups was increased, especially the number of patients with mild cancer pain in group C (P < 0.05). Besides, the quality of life score of patients in each group was decreased. Patients who received the combination analgesia had a significantly higher quality of life than those who received simply a transdermal fentanyl patch or ERAS (P < 0.05). The scores of both the Hamilton anxiety scale (HAMA) and Hamilton depression rating scale (HAMD) of patients with the combined analgesia were decreased signally (P < 0.05). There were few patients with combined analgesia who had adverse reactions (P < 0.05). After surgery, the time of the first anal exhaust, first defecation, and first ambulation in group C were shorter than those in the other two groups (P < 0.05). To summarize, combining the two techniques aided in the recovery of gastrointestinal function as well as the physical recovery of patients following surgery. Furthermore, combining the two approaches produced a clear analgesic impact, which could improve patients' quality of life while also having a favorable clinical adoption effect.

Construction of a Necroptosis-Associated Long Non-Coding RNA Signature to Predict Prognosis and Immune Response in Hepatocellular Carcinoma

Background: Necroptosis is a form of programmed cell death, and studies have shown that long non-coding RNA molecules (lncRNAs) can regulate the process of necroptosis in various cancers. We sought to screen lncRNAs associated with necroptosis to predict prognosis and tumor immune infiltration status in patients with hepatocellular carcinoma (HCC).

Methods: Transcriptomic data from HCC tumor samples and normal tissues were extracted from The Cancer Genome Atlas database. Necroptosis-associated lncRNAs were obtained by co-expression analysis. Necroptosis-associated lncRNAs were then screened by Cox regression and least absolute shrinkage and selection operator methods to construct a risk model for HCC. The models were also validated and evaluated by Kaplan-Meier analysis, univariate and multivariate Cox regression, and time-dependent receiver operating characteristic (ROC) curves. In addition, Gene Ontology, Kyoto Encyclopedia of Genes and Genomes enrichment, gene set enrichment, principal component, immune correlation, and drug sensitivity analyses were applied to assess model risk groups. To further differentiate the immune microenvironment of different HCC subtypes, the entire dataset was divided into three clusters, based on necroptosis-associated lncRNAs, and a series of analyses performed.

Results: We constructed a model comprising four necroptosis-associated lncRNAs: POLH-AS1, DUXAP8, AC131009.1, and TMCC1-AS1. Overall survival (OS) duration was significantly longer in patients classified as low-risk than those who were high-risk, according to our model. Univariate and multivariate Cox regression analyses further confirmed risk score stability. The analyzed models had area under the ROC curve values of 0.786, 0.713, and 0.639 for prediction of 1-, 3-, and 5-year OS, respectively, and risk score was significantly associated with immune cell infiltration and ESTIMATE score. In addition, differences between high and low-risk groups in predicted half-maximal inhibitory concentration values for some targeted and chemical drugs, providing a potential basis for selection of treatment approach. Finally, cluster analysis facilitated more refined differentiation of the immune microenvironment in patients with HCC and may allow prediction of the effectiveness of immune checkpoint inhibitors.

Conclusions: This study contributes to understanding of the function of necroptosis-related lncRNAs in predicting the prognosis and immune infiltration status of HCC. The risk model constructed and cluster analysis provide a basis for predicting the prognosis of patients with HCC and to inform the selection of immunotherapeutic strategies.

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.

Polyadenylation ligation-mediated sequencing (PALM-Seq) characterizes cell-free coding and non-coding RNAs in human biofluids

BACKGROUND

Cell-free messenger RNA (cf-mRNA) and long non-coding RNA (cf-lncRNA) are becoming increasingly important in liquid biopsy by providing biomarkers for disease prediction, diagnosis and prognosis, but the simultaneous characterization of coding and non-coding RNAs in human biofluids remains challenging.

METHODS

Here, we developed polyadenylation ligation-mediated sequencing (PALM-Seq), an RNA sequencing strategy employing treatment of RNA with T4 polynucleotide kinase to generate cell-free RNA (cfRNA) fragments with 5' phosphate and 3' hydroxyl and RNase H to deplete abundant RNAs, achieving simultaneous quantification and characterization of cfRNAs.

RESULTS

Using PALM-Seq, we successfully identified well-known differentially abundant mRNA, lncRNA and microRNA in the blood plasma of pregnant women. We further characterized cfRNAs in blood plasma, saliva, urine, seminal plasma and amniotic fluid and found that the detected numbers of different RNA biotypes varied with body fluids. The profiles of cf-mRNA reflected the function of originated tissues, and immune cells significantly contributed RNA to blood plasma and saliva. Short fragments (<50 nt) of mRNA and lncRNA were major in biofluids, whereas seminal plasma and amniotic fluid tended to retain long RNA. Body fluids showed distinct preferences of pyrimidine at the 3' end and adenine at the 5' end of cf-mRNA and cf-lncRNA, which were correlated with the proportions of short fragments.

CONCLUSION

Together, PALM-Seq enables a simultaneous characterization of cf-mRNA and cf-lncRNA, contributing to elucidating the biology and promoting the application of cfRNAs.

The development and validation of a novel senescence-related long-chain non-coding RNA (lncRNA) signature that predicts prognosis and the tumor microenvironment of patients with hepatocellular carcinoma

Background

The epigenetic regulators of cellular senescence, especially long non-coding RNAs (lncRNAs), remain unclear. The expression levels of lncRNA were previously known to be prognostic indicators for tumors. We hypothesized that lncRNAs regulating cellular senescence could also predict prognosis in patients with hepatocellular carcinoma (HCC) and developed a novel lncRNA predictive signature.

Methods

Using RNA sequencing data from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) database, a co-expression network of senescence-related messenger RNAs (mRNAs) and lncRNAs was constructed. Using univariate Cox regression analysis and a stepwise multiple Cox regression analysis, we constructed a prognostic HCC senescence-related lncRNA signature (HCCSenLncSig). Kaplan-Meier analysis was used to compare the overall survival (OS) of high- and low-risk groups stratified by the HCCSenLncSig. Furthermore, the HCCSenLncSig risk score and other clinical characteristics were included to develop an HCC prognostic nomogram. The accuracy of the model was evaluated by the time dependent receiver operating characteristic (ROC) and calibration curves, respectively.

Results

We obtained a prognostic risk model consisting of 8 senescence-related lncRNAs: AL117336.3, AC103760.1, FOXD2-AS1, AC009283.1, AC026401.3, AC021491.4, AC124067.4, and RHPN1-AS1. The HCCSenLncSig high-risk group was associated with poor OS [hazard ratio (HR) =1.125, 95% confidence interval (CI): 1.082-1.169; P<0.001]. The accuracy of the model was further supported by ROC curves (the area under the curve is 0.783, sensitivity of 0.600, and specificity of 0.896 at the cut-off value of 1.447). The HCCSenLncSig was found to be an independent prognostic factor from other clinical factors in both univariate and multivariate Cox regression analyses. The prognostic nomogram shows HCCSenLncSig has a good prognostic effect for survival risk stratification. Finally, we found that a higher number of immunosuppressed Treg cells infiltrate in high-risk patients (P<0.001 compared to low-risk patients), possibly explaining why these patients have a poor prognosis. On the other hand, the expression of immunotherapy markers, such as CD276, PDCD1, and CTLA4, was also up-regulated in the high-risk patients, indicating potential immunotherapy response in these patients.

Conclusions

The development of HCCSenLncSig allows us to better predict HCC patients' survival outcomes and disease risk, as well as contribute to the development of novel HCC anti-cancer therapeutic strategies.

Ferroptosis-related long non-coding RNA signature predicts the prognosis of bladder cancer

Background

Ferroptosis is an iron-dependent programmed cell death modality that may have a tumor-suppressive function. Therefore, regulating ferroptosis in tumor cells could serve as a novel therapeutic approach. This article focuses on ferroptosis-associated long non-coding RNAs (lncRNAs) and their potential application as a prognostic predictor for bladder cancer (BCa).

Methods

We retrieved BCa-related transcriptome information and clinical information from the TCGA database and ferroptosis-related gene sets from the FerrDb database. Least absolute shrinkage and selection operator regression (LASSO) and Cox regression models were used to identify and develop predictive models and validate the model accuracy. Finally, we explored the inter-regulatory relationships between ferroptosis-related genes and immune cell infiltration, immune checkpoints, and m⁶A methylation genes.

Results

Kaplan–Meier analyses screened 11 differentially expressed lncRNAs associated with poor BCa prognosis. The signature (AUC = 0.720) could be utilized to predict BCa prognosis. Additionally, GSEA revealed immune and tumor-related pathways in the low-risk group. TCGA showed that the p53 signaling pathway, ferroptosis, Kaposi sarcoma − associated herpesvirus infection, IL − 17 signaling pathway, MicroRNAs in cancer, TNF signaling pathway, PI3K − Akt signaling pathway and HIF − 1 signaling pathway were significantly different from those in the high-risk group. Immune checkpoints, such as PDCD-1 (PD-1), CTLA4, and LAG3, were differentially expressed between the two risk groups. m⁶A methylation-related genes were significantly differentially expressed between the two risk groups.

Conclusion

A new ferroptosis-associated lncRNAs signature developed for predicting the prognosis of BCa patients will improve the treatment and management of BCa patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09805-9.

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

MOTIVATION

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

RESULTS

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

AVAILABILITY AND IMPLEMENTATION

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

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Circ-ZEB1 promotes PIK3CA expression by silencing miR-199a-3p and affects the proliferation and apoptosis of hepatocellular carcinoma

BACKGROUND

Although the prognostic outcomes of liver cancer (LC) cases have improved with the advancement in diagnostic technology and treatment methods, the transferability and recurrence of HCC and the 5-year and 10-year survival rates of patients have remained unsatisfactory. As a result, there is a need for more accurate diagnostic indicators that can detect liver cancer early, effectively improving the prognosis of patients. Whole-genome sequencing (WGS) revealed that circ-ZEB1 and PIK3CA are highly expressed in HCC tissues, whereas miR-199a-3p is significantly downregulated in HCC. Multiple databases search and biological analysis revealed that elevated expression of circ-ZEB1 and PIK3CA was related to poor prognosis of HCC. In vitro and in vivo studies revealed that upregulated levels of PIK3CA and circ-ZEB1 were closely associated with HCC proliferation and apoptosis. Based on these results, we believe that circ-ZEB1 and PIK3CA could be used as biomarkers to diagnose and treat patients with HCC. More importantly, circ-ZEB1 can promotes the expression of PIK3CA by silencing miR-199a-3p and affecting the progression of HCC.

METHODS AND RESULTS

Postoperative specimens from 56 patients with HCC who had not undergone chemotherapy from 2015 to 2018 were collected from the Department of Hepatobiliary Surgery, Second Affiliated Hospital of Nanchang University. WGS revealed differential expression of genes in HCC. Furthermore, RT-qPCR detected the expression of circ-ZEB1, miR-199a-3p, and PIK3CA in HCC tissues. MTT, EdU, and plate cloning experiments were conducted to detect cell proliferation, whereas flow cytometry analysis was used to detect apoptosis. FISH was used to co-localize circ-ZEB1 and miR-199a-3p, and biotin-coupled probe pull-down assay was used to detect the specific binding of circ-ZEB1 and miR-199a-3p. The dual-luciferase report assay detected the association of miR-199a-3p with PIK3CA. Western blotting was used to study the expression of PIK3CA protein. Circ-ZEB1 and PIK3CA were upregulated in HCC and predicted a poor prognosis. MiR-199a-3p showed low expression in HCC, whereas downregulation of circ-ZEB1 reduced HCC cell proliferation and promoted cell apoptosis. MiR-199a-3p blocked the effect of circ-ZEB1 on HCC. Circ-ZEB1 served as a biomarker of HCC. Circ-ZEB1 promoted the expression of PIK3CA by silencing miR-199a-3p to affect the progress of HCC.

CONCLUSIONS

Circ-ZEB1 promoted the expression of PIK3CA by depleting miR-199a-3p, thereby affecting HCC proliferation and apoptosis.

Exosomes as a new frontier of cancer liquid biopsy

Liquid biopsy, characterized by minimally invasive detection through biofluids such as blood, saliva, and urine, has emerged as a revolutionary strategy for cancer diagnosis and prognosis prediction. Exosomes are a subset of extracellular vesicles (EVs) that shuttle molecular cargoes from donor cells to recipient cells and play a crucial role in mediating intercellular communication. Increasing studies suggest that exosomes have a great promise to serve as novel biomarkers in liquid biopsy, since large quantities of exosomes are enriched in body fluids and are involved in numerous physiological and pathological processes. However, the further clinical application of exosomes has been greatly restrained by the lack of high-quality separation and component analysis methods. This review aims to provide a comprehensive overview on the conventional and novel technologies for exosome isolation, characterization and content detection. Additionally, the roles of exosomes serving as potential biomarkers in liquid biopsy for the diagnosis, treatment monitoring, and prognosis prediction of cancer are summarized. Finally, the prospects and challenges of applying exosome-based liquid biopsy to precision medicine are evaluated.

A novel ResNet101 model based on dense dilated convolution for image classification

Image classification plays an important role in computer vision. The existing convolutional neural network methods have some problems during image classification process, such as low accuracy of tumor classification and poor ability of feature expression and feature extraction. Therefore, we propose a novel ResNet101 model based on dense dilated convolution for medical liver tumors classification. The multi-scale feature extraction module is used to extract multi-scale features of images, and the receptive field of the network is increased. The depth feature extraction module is used to reduce background noise information and focus on effective features of the focal region. To obtain broader and deeper semantic information, a dense dilated convolution module is deployed in the network. This module combines the advantages of Inception, residual structure, and multi-scale dilated convolution to obtain a deeper level of feature information without causing gradient explosion and gradient disappearance. To solve the common feature loss problems in the classification network, the up- down-sampling module in the network is improved, and multiple convolution kernels with different scales are cascaded to widen the network, which can effectively avoid feature loss. Finally, experiments are carried out on the proposed method. Compared with the existing mainstream classification networks, the proposed method can improve the classification performance, and finally achieve accurate classification of liver tumors. The effectiveness of the proposed method is further verified by ablation experiments.

Highlights

(In)Distinctive Role of Long Non-Coding RNAs in Common and Rare Ovarian Cancers

Rare ovarian cancers (ROCs) are OCs with an annual incidence of fewer than 6 cases per 100,000 women. They affect women of all ages, but due to their low incidence and the potential clinical inexperience in management, there can be a delay in diagnosis, leading to a poor prognosis. The underlying causes for these tumors are varied, but generally, the tumors arise due to alterations in gene/protein expression in cellular processes that regulate normal proliferation and its checkpoints. Dysregulation of the cellular processes that lead to cancer includes gene mutations, epimutations, non-coding RNA (ncRNA) regulation, posttranscriptional and posttranslational modifications. Long non-coding RNA (lncRNA) are defined as transcribed RNA molecules, more than 200 nucleotides in length which are not translated into proteins. They regulate gene expression through several mechanisms and therefore add another level of complexity to the regulatory mechanisms affecting tumor development. Since few studies have been performed on ROCs, in this review we summarize the mechanisms of action of lncRNA in OC, with an emphasis on ROCs.

Construction of a Ferroptosis-Related Nine-lncRNA Signature for Predicting Prognosis and Immune Response in Hepatocellular Carcinoma

Ferroptosis is an iron-dependent cell death process that plays important regulatory roles in the occurrence and development of cancers, including hepatocellular carcinoma (HCC). Moreover, the molecular events surrounding aberrantly expressed long non-coding RNAs (lncRNAs) that drive HCC initiation and progression have attracted increasing attention. However, research on ferroptosis-related lncRNA prognostic signature in patients with HCC is still lacking. In this study, the association between differentially expressed lncRNAs and ferroptosis-related genes, in 374 HCC and 50 normal hepatic samples obtained from The Cancer Genome Atlas (TCGA), was evaluated using Pearson's test, thereby identifying 24 ferroptosis-related differentially expressed lncRNAs. The least absolute shrinkage and selection operator (LASSO) algorithm and Cox regression model were used to construct and validate a prognostic risk score model from both TCGA training dataset and GEO testing dataset (GSE40144). A nine-lncRNA-based signature (CTD-2033A16.3, CTD-2116N20.1, CTD-2510F5.4, DDX11-AS1, LINC00942, LINC01224, LINC01231, LINC01508, and ZFPM2-AS1) was identified as the ferroptosis-related prognostic model for HCC, independent of multiple clinicopathological parameters. In addition, the HCC patients were divided into high-risk and low-risk groups according to the nine-lncRNA prognostic signature. The gene set enrichment analysis enrichment analysis revealed that the lncRNA-based signature might regulate the HCC immune microenvironment by interfering with tumor necrosis factor α/nuclear factor kappa-B, interleukin 2/signal transducers and activators of transcription 5, and cytokine/cytokine receptor signaling pathways. The infiltrating immune cell subtypes, such as resting memory CD4(+) T cells, follicular helper T cells, regulatory T cells, and M0 macrophages, were all significantly different between the high-risk group and the low-risk group as indicated in Spearman's correlation analysis. Moreover, a substantial increase in the expression of B7H3 immune checkpoint molecule was found in the high-risk group. Our findings provided a promising insight into ferroptosis-related lncRNAs in HCC and a personalized prediction tool for prognosis and immune responses in patients.