Determining the Prognostic Value of Spliceosome-Related Genes in Hepatocellular Carcinoma Patients

Transcriptional genes of lysosome-associated membrane protein 2A in sciatic nerve injuries by bioinformatics

Recent studies have shown that autophagy is activated in response to nerve damage and occurs simultaneously with the initial stages of Schwann cell-mediated demyelination. Although several studies have reported that macroautophagy is involved in the peripheral nerve, the role of chaperone-mediated autophagy (CMA) has not yet been investigated in peripheral nerve injury. The present study investigates the role of CMA in the sciatic nerve. Using a mouse model of sciatic nerve injury, the authors employed immunofluorescence analysis to observe the expression of LAMP2A, a critical marker for CMA. RNA sequencing was performed to observe the transcriptional profile of Lamp2a in Schwann cells. Bioinformatics analysis was carried out to observe the hub genes associated with Lamp2a. Expression of Lamp2a, a key gene in CMA, increased following sciatic nerve injury, based on an immunofluorescence assay. To identify differentially expressed genes using Lamp2a, RNA sequence analysis was conducted using rat Schwann cells overexpressing Lamp2a. The nine hub genes (Snrpf, Polr1d, Snip1, Aqr, Polr2h, Ssbp1, Mterf3, Adcy6, and Sbds) were identified using the CytoHubba plugin of Cytoscape. Functional analysis revealed that Lamp2a overexpression affected the transcription levels of genes associated with mitotic spindle organization and mRNA splicing via the spliceosome. In addition, Polr1d and Snrpf1 were downregulated throughout postnatal development but elevated following sciatic nerve injury, according to a bioinformatics study. CMA may be an integral pathway in sciatic nerve injury via mRNA splicing.

The mechanism of LSM2 in the progression of live hepatocellular carcinoma was analyzed based on bioinformatics

Comprehensive analysis of the expression and probable function of LSM2 in Live hepatocellular carcinoma (LIHC), and validation via in vitro experiments. Integrated use of database resources to examine the differential expression, survival prognosis, clinicopathological characteristics, and functional enrichment of LSM2 in LIHC. The expression level of LSM2 in LIHC tissues and adjacent tissues was proven via immunohistochemical staining. The biological function of LSM2 in LIHC was detected by cell proliferation, cell cloning, cell scratch, cell migration, and invasion experiments in vitro. TIMER 2.0 and GEPIA indicated that LSM2 was highly expressed in cancers and was strongly associated with survival rates in LIHC, cholangiocarcinoma, breast cancer, and renal clear cell carcinoma. LSM2 was highly expressed in LIHC, which was closely associated to the clinicopathological characteristics of patients, and the overall survival rate and disease-free survival rate of patients with high expression of LSM2 were lower than those with low expression of LSM2. Functional enrichment results revealed that LSM2 was involved to ribosome formation, DNA replication, cell cycle, metabolic processes, JAK-STAT signaling pathways, and FoxO signaling pathways. Knockdown of LSM2 inhibited the proliferation, migration, and invasion of LIHC cells in vitro experiments. LSM2 was highly expressed in LIHC and was related to a poor prognosis. Knockdown of LSM2 could inhibit the proliferation, migration, and invasion of LIHC cells.

Identification of dynamic gene expression profiles during sequential vaccination with ChAdOx1/BNT162b2 using machine learning methods

To date, COVID-19 remains a serious global public health problem. Vaccination against SARS-CoV-2 has been adopted by many countries as an effective coping strategy. The strength of the body’s immune response in the face of viral infection correlates with the number of vaccinations and the duration of vaccination. In this study, we aimed to identify specific genes that may trigger and control the immune response to COVID-19 under different vaccination scenarios. A machine learning-based approach was designed to analyze the blood transcriptomes of 161 individuals who were classified into six groups according to the dose and timing of inoculations, including I-D0, I-D2-4, I-D7 (day 0, days 2–4, and day 7 after the first dose of ChAdOx1, respectively) and II-D0, II-D1-4, II-D7-10 (day 0, days 1–4, and days 7–10 after the second dose of BNT162b2, respectively). Each sample was represented by the expression levels of 26,364 genes. The first dose was ChAdOx1, whereas the second dose was mainly BNT162b2 (Only four individuals received a second dose of ChAdOx1). The groups were deemed as labels and genes were considered as features. Several machine learning algorithms were employed to analyze such classification problem. In detail, five feature ranking algorithms (Lasso, LightGBM, MCFS, mRMR, and PFI) were first applied to evaluate the importance of each gene feature, resulting in five feature lists. Then, the lists were put into incremental feature selection method with four classification algorithms to extract essential genes, classification rules and build optimal classifiers. The essential genes, namely, NRF2, RPRD1B, NEU3, SMC5, and TPX2, have been previously associated with immune response. This study also summarized expression rules that describe different vaccination scenarios to help determine the molecular mechanism of vaccine-induced antiviral immunity.

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.