Diagnostic and prognostic value of mRNA expression of phospholipase C β family genes in hepatitis B virus‑associated hepatocellular carcinoma

LncRNA AC100826.1 regulated PLCB1 to promote progression in non-small cell lung cancer

BACKGROUND

Lung cancer is the most common malignant tumor. In the present study, we identified a long non-coding RNA (lncRNA) AC100826.1 (simplify to Lnc1), which was highly expressed in non-small cell lung cancer (NSCLC) tissues compared with the paracancerous tissues. We also observed the critical role of Lnc1 in regulating the metastasis ability of NSCLC cells.

METHODS

RNA sequencing was performed to detect differential expression levels of lncRNAs in NSCLC tissues and its paracancerous tissues. Effects of Lnc1 on cell proliferation, invasion, and migration were determined by CCK-8, transwell and scratch assays. The xenograft experiment confirmed the effect of Lnc1 on NSCLC cells proliferation and migration abilities in vivo. RT-qPCR and western blots were performed to determine the expression levels of mRNAs and proteins.

RESULTS

The expression level of Lnc1 was related to multiple pathological results, knockdown of Lnc1 can inhibit the proliferation and metastasis abilities of NSCLC cells. silencing phospholipase C, β1(PLCB1) can reverse the promoting effects of overexpression Lnc1 on NSCLC cells proliferation and migration abilities. In addition, the Rap1 signaling pathway was implicated in the regulation of Lnc1 in NSCLC metastasis.

CONCLUSION

Our results suggest that Lnc1 regulated the metastatic ability of NSCLC cells through targeting the PLCB1/Rap1 signal pathway.

In vitro suppression of porcine epidemic diarrhea virus by Panax notoginseng saponins: assessing antiviral potential

Porcine epidemic diarrhea virus (PEDV) causes severe diarrhea and high mortality in neonatal suckling piglets, leading to significant economic losses to the swine industry. Panax notoginseng saponins (PNS) are bioactive extracts derived from the P. notoginseng plant. In this study, we investigated the anti-PEDV effect of PNS by employing various methodologies to assess their impact on PEDV in Vero cells. Using a CCK-8 (Cell Counting Kit-8) assay, we found that PNS had no significant cytotoxicity below the concentration of 128 µg/mL in Vero cells. Using immunofluorescence assays (IFAs), an enzyme-linked immunosorbent assay (ELISA), and plaque formation assays, we observed a dose-dependent inhibition of PEDV infection by PNS within 24-48 hours postinfection. PNS exerts its anti-PEDV activity specifically at the genome replication stage, and mRNA-seq analysis demonstrated that treatment with PNS resulted in increased expression of various genes, including IFIT1 (interferon-induced protein with tetratricopeptide repeats 1), IFIT3 (interferon-induced protein with tetratricopeptide repeats 3), CFH (complement factor H), IGSF10 (immunoglobulin superfamily member 10), ID2 (inhibitor of DNA binding 2), SPP1 (secreted phosphoprotein 1), PLCB4 (phospholipase C beta 4), and FABP4 (fatty acid binding protein 4), but it resulted in decreased expression of IL1A (interleukin 1 alpha), TNFRSF19 (TNF receptor superfamily member 19), CDH8 (cadherin 8), DDIT3 (DNA damage inducible transcript 3), GADD45A (growth arrest and DNA damage inducible alpha), PTPRG (protein tyrosine phosphatase receptor type G), PCK2 (phosphoenolpyruvate carboxykinase 2), and ADGRA2 (adhesion G protein-coupled receptor A2). This study provides insights into the potential mechanisms underlying the antiviral effects of PNS. Taken together, the results suggest that the PNS might effectively regulate the defense response to the virus and have potential to be used in antiviral therapies.

Machine-learning algorithms based on personalized pathways for a novel predictive model for the diagnosis of hepatocellular carcinoma

BACKGROUND

At present, the diagnostic ability of hepatocellular carcinoma (HCC) based on serum alpha-fetoprotein level is limited. Finding markers that can effectively distinguish cancer and non-cancerous tissues is important for improving the diagnostic efficiency of HCC.

RESULTS

In this study, we developed a predictive model for HCC diagnosis using personalized biological pathways combined with a machine learning algorithm based on regularized regression and carry out relevant examinations. In two training sets, the overall cross-study-validated area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve and the Brier score of the diagnostic model were 0.987 [95%confidence interval (CI): 0.979-0.996], 0.981 and 0.091, respectively. Besides, the model showed good transferability in external validation set. In TCGA-LIHC cohort, the AUROC, AURPC and Brier score were 0.992 (95%CI: 0.985-0.998), 0.967 and 0.112, respectively. The diagnostic model has accomplished very impressive performance in distinguishing HCC from non-cancerous liver tissues. Moreover, we further analyzed the extracted biological pathways to explore molecular features and prognostic factors. The risk score generated from a 12-gene signature extracted from the characteristic pathways was correlated with some immune related pathways and served as an independent prognostic factor for HCC.

CONCLUSION

We used personalized biological pathways analysis and machine learning algorithm to construct a highly accurate HCC diagnostic model. The excellent interpretable performance and good transferability of this model enables it with great potential for personalized medicine, which can assist clinicians in diagnosis for HCC patients.

Integrative analysis of multi-omics data to detect the underlying molecular mechanisms for obesity in vivo in humans

BACKGROUND

Obesity is a complex, multifactorial condition in which genetic play an important role. Most of the systematic studies currently focuses on individual omics aspect and provide insightful yet limited knowledge about the comprehensive and complex crosstalk between various omics levels.

SUBJECTS AND METHODS

Therefore, we performed a most comprehensive trans-omics study with various omics data from 104 subjects, to identify interactions/networks and particularly causal regulatory relationships within and especially those between omic molecules with the purpose to discover molecular genetic mechanisms underlying obesity etiology in vivo in humans.

RESULTS

By applying differentially analysis, we identified 8 differentially expressed hub genes (DEHGs), 14 differentially methylated regions (DMRs) and 12 differentially accumulated metabolites (DAMs) for obesity individually. By integrating those multi-omics biomarkers using Mendelian Randomization (MR) and network MR analyses, we identified 18 causal pathways with mediation effect. For the 20 biomarkers involved in those 18 pairs, 17 biomarkers were implicated in the pathophysiology of obesity or related diseases.

CONCLUSIONS

The integration of trans-omics and MR analyses may provide us a holistic understanding of the underlying functional mechanisms, molecular regulatory information flow and the interactive molecular systems among different omic molecules for obesity risk and other complex diseases/traits.

Ebola Virus Encodes Two microRNAs in Huh7-Infected Cells

MicroRNAs (miRNAs) are important gene regulatory molecules involved in a broad range of cellular activities. Although the existence and functions of miRNAs are clearly defined and well established in eukaryotes, this is not always the case for those of viral origin. Indeed, the existence of viral miRNAs is the subject of intense controversy, especially those of RNA viruses. Here, we characterized the miRNA transcriptome of cultured human liver cells infected or not with either of the two Ebola virus (EBOV) variants: Mayinga or Makona; or with Reston virus (RESTV). Bioinformatic analyses revealed the presence of two EBOV-encoded miRNAs, miR-MAY-251 and miR-MAK-403, originating from the EBOV Mayinga and Makona variants, respectively. From the miRDB database, miR-MAY-251 and miR-MAK-403 displayed on average more than 700 potential human host target candidates, 25% of which had a confidence score higher than 80%. By RT-qPCR and dual luciferase assays, we assessed the potential regulatory effect of these two EBOV miRNAs on selected host mRNA targets. Further analysis of Panther pathways unveiled that these two EBOV miRNAs, in addition to general regulatory functions, can potentially target genes involved in the hemorrhagic phenotype, regulation of viral replication and modulation of host immune defense.

The prognostic values of serum markers in hepatocellular carcinoma after invasive therapies based on real-world data

BACKGROUND AND AIMS

Hepatocellular carcinoma (HCC) is one of the most common malignancy with poor prognosis, and the mortality rate remains high. More than 70% of HCC patients have recurrence within 5 years after treatment. The purpose of this study is to evaluate the prognostic values of serum markers with retrospective data.

METHODS

We applied real-world data (RWD) to analyze the prognostic values of six serum markers for HCC patients after treatment, including α-fetoprotein (AFP), α-fetoprotein-L3 (AFP-L3), Golgi protein73 (GP73), alanine aminotransferase (ALT), albumin (ALB), and total bilirubin (TBil). A total of 268 cases were enrolled to analyze recurrence-free survival (RFS), and 104 cases were used to analyze overall survival (OS).

RESULTS

Our results demonstrated that patients with higher AFP and AFP-L3 had shorter RFS (p = 0.016 and 0.004), while higher GP73, ALT, and TBil experienced longer RFS (p = 0.000, 0.020, and 0.019). Patients with high-level GP73, ALT, TBil, and low-level ALB had significantly higher mortality rate (p=0.035, 0.008, 0.010, and 0.005). Multivariate analysis revealed that GP73 (HR = 1.548, p = 0.001) and ALT (HR = 1.316, p = 0.046) were identified as independent prognostic factors for RFS, ALB (HR = 0.127, p = 0.007), and ALT (HR = 0.237, p = 0.01) were identified as independent prognostic factors for OS. Subgroups analysis showed that GP73 had better prognostic values than other serum markers in early-stage HCC (p = 0.023).

CONCLUSIONS

Our study demonstrates that AFP, AFP-L3, and GP73 can be used as prognostic indicators for predicting the recurrence of HCC, while liver function tests have better survival prediction values. GP73 can act as a promising prognostic marker for early-stage HCC.

Analysis of clinical significance and prospective molecular mechanism of main elements of the JAK/STAT pathway in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one the most common malignancies and has poor prognosis in patients. The aim of the present study is to explore the clinical significance of the main genes involved in the Janus kinase (JAK)-signal transducer and activator of transcription (STAT) pathway in HCC. GSE14520, a training cohort containing 212 hepatitis B virus-infected HCC patients from the Gene Expression Omnibus database, and data from The Cancer Genome Atlas as a validation cohort containing 370 HCC patients, were used to analyze the diagnostic and prognostic significance for HCC. Joint-effect analyses were performed to determine diagnostic and prognostic significance. Nomograms and risk score models were constructed to predict HCC prognosis using the two cohorts. Additionally, molecular mechanism analysis was performed for the two cohorts. Prognosis-associated genes in the two cohorts were further validated for differential expression using reverse transcription-quantitative polymerase chain reaction of 21 pairs of hepatitis B virus-infected HCC samples. JAK2, TYK2, STAT3, STAT4 and STAT5B had diagnostic significance in the two cohorts (all area under curves >0.5; P≤0.05). In addition, JAK2, STAT5A, STAT6 exhibited prognostic significance in both cohorts (all adjusted P≤0.05). Furthermore, joint-effect analysis had advantages over using one gene alone. Molecular mechanism analyses confirmed that STAT6 was enriched in pathways and terms associated with the cell cycle, cell division and lipid metabolism. Nomograms and risk score models had advantages for HCC prognosis prediction. When validated in 21 pairs of HCC and non-tumor tissue, STAT6 was differentially expressed, whereas JAK2 was not differentially expressed. In conclusion, JAK2, STAT5A and STAT6 may be potential prognostic biomarkers for HCC. JAK2, TYK2, STAT3, STAT4 and STAT5B may be potential diagnostic biomarkers for HCC. STAT6 has a role in HCC that may be mediated via effects on the cell cycle, cell division and lipid metabolism.