Construction of a Necroptosis-Related miRNA Signature for Predicting the Prognosis of Patients With Hepatocellular Carcinoma

Extracellular vesicles from alveolar macrophages harboring phagocytosed methicillin-resistant Staphylococcus aureus induce necroptosis

Methicillin-resistant Staphylococcus aureus (MRSA) infection, a major cause of hospital- and community-acquired pneumonia, still has a high mortality rate. Extracellular vesicles (EVs), as crucial mediators of intercellular communication, have a significant impact on infectious diseases. However, the role of EVs from alveolar macrophages (AMs) in MRSA pneumonia remains unclear. We report that AMs phagocytose MRSA and release more EVs in mice with MRSA pneumonia. EVs from AMs harboring phagocytosed MRSA exhibit significant proinflammatory effects and induce necroptosis by delivering tumor necrosis factor α (TNF-α) and miR-146a-5p. Mechanically, the upregulated miR-146a-5p in these EVs enhances the phosphorylation of RIPK1, RIPK3, and MLKL by targeting TNF receptor-associated factor 6 (TRAF6), thereby promoting TNF-α-induced necroptosis. The combination of a TNF-α antagonist and an miR-146a-5p antagomir effectively improves the outcomes of mice with MRSA pneumonia. Overall, we reveal the pronecrotic effect of EVs from MRSA-infected AMs and provide a promising target for the prevention and treatment of MRSA pneumonia.

Developing risk models and subtypes of autophagy-associated LncRNAs for enhanced prognostic prediction and precision in therapeutic approaches for liver cancer patients

Background

Limited research has been conducted on the influence of autophagy-associated long non-coding RNAs (ARLncRNAs) on the prognosis of hepatocellular carcinoma (HCC).

Methods

We analyzed 371 HCC samples from TCGA, identifying expression networks of ARLncRNAs using autophagy-related genes. Screening for prognostically relevant ARLncRNAs involved univariate Cox regression, Lasso regression, and multivariate Cox regression. A Nomogram was further employed to assess the reliability of Riskscore, calculated from the signatures of screened ARLncRNAs, in predicting outcomes. Additionally, we compared drug sensitivities in patient groups with differing risk levels and investigated potential biological pathways through enrichment analysis, using consensus clustering to identify subgroups related to ARLncRNAs.

Results

The screening process identified 27 ARLncRNAs, with 13 being associated with HCC prognosis. Consequently, a set of signatures comprising 8 ARLncRNAs was successfully constructed as independent prognostic factors for HCC. Patients in the high-risk group showed very poor prognoses in most clinical categories. The Riskscore was closely related to immune cell scores, such as macrophages, and the DEGs between different groups were implicated in metabolism, cell cycle, and mitotic processes. Notably, high-risk group patients demonstrated a significantly lower IC50 for Paclitaxel, suggesting that Paclitaxel could be an ideal treatment for those at elevated risk for HCC. We further identified C2 as the Paclitaxel subtype, where patients exhibited higher Riskscores, reduced survival rates, and more severe clinical progression.

Conclusion

The 8 signatures based on ARLncRNAs present novel targets for prognostic prediction in HCC. The drug candidate Paclitaxel may effectively treat HCC by impacting ARLncRNAs expression. With the identification of ARLncRNAs-related isoforms, these results provide valuable insights for clinical exploration of autophagy mechanisms in HCC pathogenesis and offer potential avenues for precision medicine.

Circulating microRNAs improve bacterial infection diagnosis and overall survival prediction in acute decompensation of liver cirrhosis

Bacterial infections are the most frequent precipitating event in patients with acute decompensation of cirrhosis (AD) and are associated with high mortality. Early diagnosis is challenging due to cirrhosis-related systemic inflammation. Here we investigated the potential of circulating microRNAs to diagnose bacterial infections and predict survival in cirrhotic patients with AD. High throughput profiling of circulating microRNAs was performed using the Nanostring technology in 57 AD patients and 24 patients with compensated cirrhosis (CC). Circulating miRs profiling showed that: (a) miRs differentially detected in AD vs. CC were mostly down-regulated; (b) a composite score including absolute neutrophil count, C reactive protein and miR-362-3p could diagnose bacterial infection with an excellent performance (AUC of 0.825 [95% CI = 0.671-0.980; p < 0.001]); (c) a composite score including miR-382-5p, miR-592 and MELD-Na improved 6-month survival prediction. Circulating miRs are strongly dysregulated in patients with AD and may help to improve bacterial infection diagnosis and survival prediction.

A miRNome analysis at the early postmortem interval

The postmortem interval (PMI) is the time elapsing since the death of an individual until the body is examined. Different molecules have been analyzed to better estimate the PMI with variable results. The miRNAs draw attention in the forensic field to estimate the PMI as they can better support degradation. In the present work, we analyzed the miRNome at early PMI in rats' skeletal muscle using the Affymetrix GeneChip™ miRNA 4.0 microarrays. We found 156 dysregulated miRNAs in rats' skeletal muscle at 24 h of PMI, out of which 84 were downregulated, and 72 upregulated. The miRNA most significantly downregulated was miR-139-5p (FC = -160, p = 9.97 × 10^-11), while the most upregulated was rno-miR-92b-5p (FC = 241.18, p = 2.39 × 10^-6). Regarding the targets of these dysregulated miRNAs, the rno-miR-125b-5p and rno-miR-138-5p were the miRNAs with more mRNA targets. The mRNA targets that we found in the present study participate in several biological processes such as interleukin secretion regulation, translation regulation, cell growth, or low oxygen response. In addition, we found a downregulation of SIRT1 mRNA and an upregulation of TGFBR2 mRNA at 24 h of PMI. These results suggest there is an active participation of miRNAs at early PMI which could be further explored to identify potential biomarkers for PMI estimation.

Identification and Characterization of an Ageing-Associated 13-lncRNA Signature That Predicts Prognosis and Immunotherapy in Hepatocellular Carcinoma

Background

In cancer pathology, cell senescence not only alters cell function but also reshapes the immune microenvironments in tumours. However, the association between cell senescence, tumour microenvironment, and disease progression of hepatocellular carcinoma (HCC) is yet to be fully understood. Therefore, the role of cell senescence-related genes and long noncoding RNAs (lncRNAs) in evaluating the clinical prognosis and immune cell infiltration (ICI) of HCC patients requires further investigation.

Methods

The limma R package was utilised to investigate differentially expressed genes according to the multiomics data. The CIBERSORT R package was utilised to assess ICI, and unsupervised cluster analysis was conducted using the R software's ConsensusClusterPlus package. A polygenic prognostic model of lncRNAs was constructed by conducting univariate and least absolute shrinkage and selection operator (Lasso) cox proportional-hazards regression analyses. The time-dependent receiver operating characteristic (ROC) curves were used for validation. We utilised the survminer R package to evaluate the tumour mutational burden (TMB). Moreover, the gene set enrichment analysis (GSEA) helped in pathway enrichment analysis, and the immune infiltration level of the model was evaluated using the IMvigor210 cohort.

Results

The identification of 36 prognosis-related genes was achieved based on their differential expression between healthy and liver cancer tissues. Liver cancer individuals were categorised into 3 independent senescence subtypes using the gene list, revealing considerable survival differences (variations). We observed that the prognosis of patients in the ARG-ST2 subtype was substantially better as compared to that in the ARG-ST3 subtype. Differences were observed in gene expression profiles among the three subtypes, with the differentially expressed genes predominantly associated with cell cycle control. The enrichment of upregulated genes in the ARG-ST3 subtype was observed in pathways related to biological processes, for instance, organelle fission, nuclear division, and chromosome recombination. ICI in the ARG-ST1 and ARG-ST2 subtypes, with relatively better prognosis, was substantially higher as compared to the ARG-ST3 subtype. Furthermore, a risk-score model, which can be employed as a reliable prognostic factor in an independent manner for individuals suffering from liver cancer, was constructed based on 13 cell senescence-related lncRNAs (MIR99AHG, LINC01224, LINC01138, SLC25A30AS1, AC006369.2, SOCS2AS1, LINC01063, AC006037.2, USP2AS1, FGF14AS2, LINC01116, KIF25AS1, and AC002511.2). The individuals with higher risk scores had noticeably poor prognoses in contrast with those having low-risk scores. Moreover, increased levels of TMB and ICI were observed in individuals with low-risk scores and gaining more benefit from immune checkpoint therapy.

Conclusion

Cell senescence is an essential factor in HCC onset and progression. We identified 13 senescence-related lncRNAs as HCC prognostic markers, which can help understand their function in the onset and progression of HCC and guide clinical diagnosis and treatment.

Qualification of Necroptosis-Related lncRNA to Forecast the Treatment Outcome, Immune Response, and Therapeutic Effect of Kidney Renal Clear Cell Carcinoma

Background

Kidney renal clear cell carcinoma (KIRC) is considered as a highly immune infiltrative tumor. Necroptosis is an inflammatory programmed cell death associated with a wide range of diseases. Long noncoding RNAs (lncRNAs) play important roles in gene regulation and immune function. lncRNA associated with necroptosis could systematically explore the prognostic value, regulate tumor microenvironment (TME), etc.

Method

The patients' data was collected from TCGA datasets. We used the univariate Cox regression (UCR) to select prediction lncRNAs that are related to necroptosis. Meanwhile, risk models were constructed using LASSO Cox regression (LCR). Kaplan–Meier (KM) analysis, accompanied with receiver operating characteristic (ROC) curves, was performed to assess the independent risk factors of different clinical characteristics. The evaluated factors are age, gender, disease staging, grade, and their related risk score. Databases such as Gene Ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG), and Gene set enrichment analysis (GSEA) were used to search the probable biological characteristics that could influence the risk groups, containing signaling pathway and immue-related pathways. The single-sample gene set enrichment analysis (ssGSEA) was chosen to perform gene set variation analysis (GSVA), and the GSEABase package was selected to detect the immune and inflammatory infiltration profiles. The TIDE and IC₅₀ evaluation were used to estimate the effectiveness of clinical treatment on KIRC.

Results

Based on the above analysis, we have got a conclusion that patients who show high risk had higher immune infiltration, immune checkpoint expression, and poorer prognosis. We identified 19 novel prognostic necroptosis-related lncRNAs, which could offer opinions for a deeper study of KIRC.

Conclusion

The risk model we constructed makes it possible to predict the prognosis of KIRC patients and offers directions for further research on the prognostication and treatment strategies for KIRC.

miR-200c-3p, miR-222-5p, and miR-512-3p Constitute a Biomarker Signature of Sorafenib Effectiveness in Advanced Hepatocellular Carcinoma

Background: Sorafenib constitutes a suitable treatment alternative for patients with advanced hepatocellular carcinoma (HCC) in whom atezolizumab + bevacizumab therapy is contraindicated. The aim of the study was the identification of a miRNA signature in liquid biopsy related to sorafenib response. Methods: miRNAs were profiled in hepatoblastoma HepG2 cells and tested in animal models, extracellular vesicles (EVs), and plasma from HCC patients. Results: Sorafenib altered the expression of 11 miRNAs in HepG2 cells. miR-200c-3p and miR-27a-3p exerted an anti-tumoral activity by decreasing cell migration and invasion, whereas miR-122-5p, miR-148b-3p, miR-194-5p, miR-222-5p, and miR-512-3p exerted pro-tumoral properties by increasing cell proliferation, migration, or invasion, or decreasing apoptosis. Sorafenib induced a change in EVs population with an increased number of larger EVs, and promoted an accumulation of miR-27a-3p, miR-122-5p, miR-148b-3p, miR-193b-3p, miR-194-5p, miR-200c-3p, and miR-375 into exosomes. In HCC patients, circulating miR-200c-3p baseline levels were associated with increased survival, whereas high levels of miR-222-5p and miR-512-3p after 1 month of sorafenib treatment were related to poor prognosis. The RNA sequencing revealed that miR-200c-3p was related to the regulation of cell growth and death, whereas miR-222-5p and miR-512-3p were related to metabolic control. Conclusions: The study showed that Sorafenib regulates a specific miRNA signature in which miR-200c-3p, miR-222-5p, and miR-512-3p bear prognostic value and contribute to treatment response.