Classification of Long Non-Coding RNAs s Between Early and Late Stage of Liver Cancers From Non-coding RNA Profiles Using Machine-Learning Approach.

Long non-coding RNAs (lncRNAs), which are RNA sequences greater than 200 nucleotides in length, play a crucial role in regulating gene expression and biological processes associated with cancer development and progression. Liver cancer is a major cause of cancer-related mortality worldwide, notably in Thailand. Although machine learning has been extensively used in analyzing RNA-sequencing data for advanced knowledge, the identification of potential lncRNA biomarkers for cancer, particularly focusing on lncRNAs as molecular biomarkers in liver cancer, remains comparatively limited. In this study, our objective was to identify candidate lncRNAs in liver cancer. We employed an expression data set of lncRNAs from patients with liver cancer, which comprised 40 699 lncRNAs sourced from The CancerLivER database. Various feature selection methods and machine-learning approaches were used to identify these candidate lncRNAs. The results showed that the random forest algorithm could predict lncRNAs using features extracted from the database, which achieved an area under the curve (AUC) of 0.840 for classifying lncRNAs between early (stage 1) and late stages (stages 2, 3, and 4) of liver cancer. Five of 23 significant lncRNAs (WAC-AS1, MAPKAPK5-AS1, ARRDC1-AS1, AC133528.2, and RP11-1094M14.11) were differentially expressed between early and late stage of liver cancer. Based on the Gene Expression Profiling Interactive Analysis (GEPIA) database, higher expression of WAC-AS1, MAPKAPK5-AS1, and ARRDC1-AS1 was associated with shorter overall survival. In conclusion, the classification model could predict the early and late stages of liver cancer using the signature expression of lncRNA genes. The identified lncRNAs might be used as early diagnostic and prognostic biomarkers for patients with liver cancer.

Distinct alterations of gut microbiota between viral- and non-viral-related hepatocellular carcinoma.

Altered gut microbiota has been connected to hepatocellular carcinoma (HCC) occurrence and advancement. This study was conducted to identify a gut microbiota signature in differentiating between viral-related HCC (Viral-HCC) and non-hepatitis B-, non-hepatitis C-related HCC (NBNC-HCC). Fecal specimens were obtained from 16 healthy controls, 33 patients with viral-HCC (17 and 16 cases with hepatitis B virus (HBV) and hepatitis C virus (HCV) infection, respectively), and 18 patients with NBNC-HCC. Compositions of fecal microbiota were assessed by 16S rRNA sequencing. Bioinformatic analysis was performed by the DADA2 pipeline in the R program. Significantly different genera from the top 50 relative abundance were used to classify between subgroups of HCC by the Random Forest algorithm. Our data demonstrated that the HCC group had a significantly decreased alpha-diversity and changed microbial composition in comparison with healthy controls. Within the top 50 relative abundance, there were 11 genera including Faecalibacterium, Agathobacter, and Coprococcus that were significantly enhanced in Viral-HCC, while 5 genera such as Bacteroides, Streptococcus, Ruminococcus gnavus group, Parabacteroides, and Erysipelatoclostridium were enhanced in NBNC-HCC. Compared to Viral-HCC, the NBNC-HCC subgroup significantly reduced various short-chain fatty acid-producing bacteria, as well as declined fecal butyrate but elevated plasma surrogate markers of microbial translocation. Based on the machine learning algorithm, a high diagnostic accuracy to classify HCC subgroups was achieved with an area under the receiver-operating characteristic (ROC) curve (AUC) of 0.94. Collectively, these data revealed that gut dysbiosis was distinct according to etiological factors of HCC, which might play an essential role in hepatocarcinogenesis. These findings underscore the possible use of a gut microbiota signature for the diagnosis and therapeutic approaches regarding different subgroups of HCC. KEY POINTS: • Gut dysbiosis is connected to hepatocarcinogenesis and can be used as a novel biomarker. • Gut microbiota composition is significantly altered in different etiological factors of HCC. • Microbiota-based signature can accurately distinguish between Viral-HCC and NBNC-HCC.

Genome Assembly and Genome Annotation of Leishmania martiniquensis Isolated from a Leishmaniasis Patient in Thailand.

Leishmaniasis is a parasitic disease caused by Leishmania spp. with worldwide distribution. Autochthonous leishmaniasis has been reported to result from the infection by Leishmania martiniquensis in Thailand. This species was isolated in culture and subjected to high-throughput whole-genome sequencing. A total of 30.8 Mb in 36 chromosomes of the whole genome was assembled, annotated, and characterized. The L. martiniquensis under study was shown to segregate into the same clade and thus closely related to the previously identified L. martiniquensis (LU_Lmar_1.0), as determined by phylogenetic analysis of their genomic sequences along with those of representative kinetoplastid species. The total number of open reading frames genomewide predicts 8,209 protein-coding genes, of which 359 are putative virulence factors, including two previously known, e.g., cysteine proteinase C and superoxide dismutase B1. The results obtained from this study will be useful for further annotation and comparison with other Leishmania martiniquensis in the future.

Identification of genes associated with Kikuchi-Fujimoto disease using RNA and exome sequencing.

Kikuchi-Fujimoto disease (KFD) is an extremely rare disease, and although it is reported to have a worldwide distribution, young Asian women are most likely to be affected. Although this disease is generally benign and self-limiting, distinguishing it from other diseases that cause lymphadenopathy (e.g., leukemia, lymphoma, and infectious diseases) is challenging. A lymph node biopsy is a definitive diagnostic technique for KFD and only requires skillful pathologists. There are no specific symptoms or laboratory tests for KFD, and more than 50% of KFD patients have suffered from being misdiagnosed with lymphoma, which leads to improper treatment. In this study, lymph node tissue samples from KFD patients were used to reveal their exomes and transcriptomes using a high-throughput nucleotide sequencer. Fourteen single nucleotide polymorphisms (SNPs) were identified as candidate KFD markers and were compared with a healthy lymph node exome dataset. The mutation of these genes caused disruptive impact in the proteins. Several SNPs associated with KFD involve genes related to human cancers, olfaction, and osteoblast differentiation. According to the transcriptome data, there were 238 up-regulated and 1,519 down-regulated genes. RANBP2-like and ribosomal protein L13 were the most up-regulated and down-regulated genes in KFD patients, respectively. The altered gene expression involved in the human immune system, chromatin remodeling, and gene transcription. A comparison of KFD and healthy datasets of exomes and transcriptomes may allow further insights into the KFD phenotype. The results may also facilitate future KFD diagnosis and treatment.

Hepatitis B Virus-Encoded MicroRNA (HBV-miR-3) Regulates Host Gene PPM1A Related to Hepatocellular Carcinoma.

BACKGROUND: Hepatitis B is a liver infection disease caused by the Hepatitis B Virus (HBV) that can become chronic and develop into hepatocellular carcinoma. HBV was classified as a double-stranded DNA virus. Currently, there is a report showing that HBV virus-encoded miRNA called HBV-miR-3 controls the replication of HBV. However, the regulation of HBV-miR-3 in host cells remains unclear. OBJECTIVE: This study aimed to investigate the regulation of HBV-miR-3 in host gene target which is related to chronic HBV infection and HCC process. METHODS: In this study, we analyzed the read count of HBV-miR-3 from next-generation sequencing of chronic hepatitis patients in Pegylated interferon alpha-2a (PEG-IFN-α-2a) treatment. To understand the regulation of HBV-miR-3 in host cells, the HBV-miR-3 recognition sites were predicted in host target genes using miRDB. The effect of HBV-miR-3 in host cells was examined using qPCR and 3' UTR dual luciferase assay. RESULTS: The read count of HBV-miR-3 was found in chronic hepatitis patients before treatment. Moreover, the decrease of HBV-miR-3 was correlated with response group of chronic hepatitis patients after treatment. On the other hand, the abundance of HBV-miR-3 showed no difference in nonresponse group of chronic patients after PEG-IFN-α-2a treatment. To study the role of HBV-miR-3 in patients, four HBV-miR-3 target regions from Protein phosphatase 1A (PPM1A) and DIX domain containing 1 (DIXDC1) were identified in the human genome using miRDB. Interestingly, we found that HBV-miR-3 hybridized with PPM1A mRNA. The mRNA expression from RT-qPCR showed no difference between HepG2 transfected with pSilencer_scramble or pSilencer_HBV-miR-3. However, the reporter assay showed that PPM1A mRNA was suppressed by HBV-miR-3. The protein expression of PPM1A showed a decrease in cells overexpressing HBV-miR-3. Finally, the HBV-miR-3 can promote cell proliferation in cells overexpressing HBV-miR-3. CONCLUSION: This study is the first report showed the HBV encoded miRNA can regulate host gene expression. HBV-miR-3 silenced PPM1A by inhibiting the translation process of PPM1A. The downregulation of PPM1A promotes cell proliferation related to HCC development.