Analysis of Immune and Prognostic-Related lncRNA PRKCQ-AS1 for Predicting Prognosis and Regulating Effect in Sepsis.

Background: Sepsis was a high mortality and great harm systemic inflammatory response syndrome caused by infection. lncRNAs were potential prognostic marker and therapeutic target. Therefore, we expect to screen and analyze lncRNAs with potential prognostic markers in sepsis. Methods: Transcriptome sequencing and limma was used to screen dysregulated RNAs. Key RNAs were screened by correlation analysis, lncRNA-mRNA co-expression and weighted gene co-expression network analysis. Immune infiltration, gene set enrichment analysis and gene set variation analysis were used to analyze the immune correlation. Kaplan-Meier curve, receiver operator characteristic curve, Cox regression analysis and nomogram were used to analyze the correlation between key RNAs and prognosis. Sepsis model was established by lipopolysaccharide-induced HUVECs injury, and then cell viability and migration ability were detected by cell counting kit-8 and wound healing assay. The levels of apoptosis-related proteins and inflammatory cytokines were detected by RT-qPCR and Western blot. Reactive Oxygen Species and superoxide dismutase were detected by commercial kit. Results: Fourteen key differentially expressed lncRNAs and 663 key differentially expressed genes were obtained. And these lncRNAs were closely related to immune cells, especially T cell activation, immune response and inflammation. Subsequently, Subsequently, lncRNA PRKCQ-AS1 was identified as the regulator for further investigation in sepsis. RT-qPCR results showed that PRKCQ-AS1 expression was up-regulated in clinical samples and sepsis model cells, which was an independent prognostic factor in sepsis patients. Immune correlation analysis showed that PRKCQ-AS1 was involved in the immune response and inflammatory process of sepsis. Cell function tests confirmed that PRKCQ-AS1 could inhibit sepsis model cells viability and promote cell apoptosis, inflammatory damage and oxidative stress. Conclusion: We constructed immune-related lncRNA-mRNA regulatory networks in the progression of sepsis and confirmed that PRKCQ-AS1 is an important prognostic factor affecting the progression of sepsis and is involved in immune response.

A Pilot Study on Human Circulating System Indicated That Regenerating Islet-Derived Protein 3 Gamma (REG3A) is a Potential Prognostic Biomarker for Sepsis.

It is critical to find fast and robust biomarkers for sepsis to reduce the patient's risk for morbidity and mortality. In this work, we compared serum protein expression levels of regenerating islet-derived protein 3 gamma (REG3A) between patients with sepsis and healthy controls and found that serum REG3A protein was significantly elevated in patients with sepsis. In addition, expression level of serum REG3A protein was markedly correlated with the Sequential Organ Failure Assessment score, Acute Physiology and Chronic Health Evaluation II score, and C-reactive protein levels of patients with sepsis. Serum REG3A protein expression level was also confirmed to have good diagnostic value to differentiate patients with sepsis from healthy controls. Finally, serum REG3A protein expression level was found to have good prognostic value to predict the 28-day survival rate of patients with sepsis. In conclusion, our work indicated that serum REG3A may be a novel biomarker for sepsis.

Identification of a Two-lncRNA Signature with Prognostic and Diagnostic Value for Hepatocellular Carcinoma.

Background: Accumulating evidence has revealed the important role of long noncoding RNAs (lncRNA) in tumorigenesis and progression of hepatocellular carcinoma (HCC). This study aimed to identify potential lncRNAs that can serve as diagnostic and prognostic signatures for HCC. Methods: Expression profiling analysis was performed to identify differentially expressed lncRNAs (DElncRNA) between HCC and matched normal samples by integrating two independent microarray datasets. Functional Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were explored by Gene Set Variation Analysis. The prognostic and diagnostic models were developed based on two DElncRNAs. Real-time PCR was used to quantify the relative expressions of candidate lncRNAs. Results: Two robust DElncRNAs were identified and verified by quantitative PCR between HCC and matched normal samples. Function enrichment analysis revealed that they were associated with the wound healing process. The two lncRNAs were subsequently used to construct a prognostic risk model for HCC. Patients with high-risk scores estimated by the model showed a shorter survival time than low-risk patients (P < 0.001). Besides, the two lncRNA-based HCC diagnostic models exhibited good performance in discriminating HCC from normal samples on both training and test sets. The values of area under the curve (AUC) for early (I-II) and late (III-IV) HCC detection were 0.88 and 0.93, respectively. Conclusions: The two wound healing-related DElncRNAs showed robust performance for HCC prognostic prediction and detection, implying their potential role as diagnostic and prognostic markers for HCC.