Elevated Neddylation Pathway Promotes Th2 Cells Infiltration by Transactivating STAT5A in Hepatocellular Carcinoma

Uncovering essential anesthetics-induced exosomal miRNAs related to hepatocellular carcinoma progression: a bioinformatic investigation

BACKGROUND

Anesthetic drugs may alter exosomal microRNA (miRNA) contents and mediate cancer progression and tumor microenvironment remodeling. Our study aims to explore how the anesthetics (sevoflurane and propofol) impact the miRNA makeup within exosomes in hepatocellular carcinoma (HCC), alongside the interconnected signaling pathways linked to the tumor immune microenvironment.

METHODS

In this prospective study, we collected plasma exosomes from two groups of HCC patients (n = 5 each) treated with either propofol or sevoflurane, both before anesthesia and after hepatectomy. Exosomal miRNA profiles were assessed using next-generation sequencing (NGS). Furthermore, the expression data from The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) was used to pinpoint the differentially expressed exosomal miRNAs (DEmiRNAs) attributed to the influence of propofol or sevoflurane in the context of HCC. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were used to dissect the signaling pathways and biological activities associated with the identified DEmiRNAs and their corresponding target genes.

RESULTS

A total of 35 distinct DEmiRNAs were exclusively regulated by either propofol (n = 9) or sevoflurane (n = 26). Through TCGA-LIHC database analysis, 8 DEmiRNAs were associated with HCC. These included propofol-triggered miR-452-5p and let-7c-5p, as well as sevoflurane-induced miR-24-1-5p, miR-122-5p, miR-200a-3p, miR-4686, miR-214-3p, and miR-511-5p. Analyses revealed that among these 8 DEmiRNAs, the upregulation of miR-24-1-5p consistently demonstrated a significant association with lower histological grades (p < 0.0001), early-stage tumors (p < 0.05) and higher survival (p = 0.029). Further analyses using GSEA and GSVA indicated that miR-24-1-5p, along with its target genes, were involved in governing the tumor immune microenvironment and potentially inhibiting tumor progression in HCC.

CONCLUSIONS

This study provided bioinformatics evidence suggesting that sevoflurane-induced plasma exosomal miRNAs may have a potential impact on the immune microenvironment of HCC. These findings established a foundation for future research into mechanistic outcomes in cancer patients.

Prognosis, Immune Microenvironment Infiltration and Immunotherapy Response in Clear Cell Renal Cell Carcinoma Based on Cuproptosis-related Immune Checkpoint Gene Signature

Background: Immune checkpoint genes (ICGs), which are the cornerstone of immunotherapy, influence the incidence and progression of clear cell renal cell carcinoma (ccRCC). It is important to note that there is not much data in the literature to determine how cuproptosis and antitumor immunity are related. Methods: On the basis of The Cancer Genome Atlas ccRCC dataset (n=526), cuproptosis-related ICGs (CICGs) were used to identify distinct molecular subtypes. Using the Cox regression method, a risk signature was constructed and externally validated using the ICGC (n=91) and primary ccRCC subgroups of GSE22541 (n=24). The molecular and immune characteristics and efficacy of immunotherapy in the subgroups defined by the risk score were investigated. Four risk CICGs were verified through in vitro experiment. Results: We identified two unique molecular subgroups with substantial prognostic differences based on 17 CICGs. The two subtypes clearly differ in terms of the tumor immune microenvironment (TME). A predictive risk signature (CD276, HLA-E, LGALS9, and TNFRSF18) was created and externally confirmed, and their expressions were validated by realtime PCR. The multivariate Cox regression analysis demonstrated that this signature could independently predict survival. Thus, a credible nomogram incorporating the signature, age, stage, and grade was constructed, and discrimination was confirmed using the C-index, calibration curve, and decision curve analyses. The underlying implications for immune checkpoint inhibitors, the landscape of the TME, and single-cell level localization are depicted. In addition, its accuracy in forecasting actual immunotherapeutic results has been proven (CheckMate025 and TCGA-SKCM cohorts). The sensitivity of the two risk groups to various drug-targeted therapy methods was analyzed. Conclusions: The data provided here provide the groundwork for creating customized therapeutic options for individuals with ccRCC. The findings also suggested that researching the cuproptosis-based pathway might improve ccRCC patient better prognosis, development of anti-tumor immunity, and therapeutic strategies for immunotherapy.

Discovery of neddylation E2s inhibitors with therapeutic activity

Neddylation is the writing of monomers or polymers of neural precursor cells expressed developmentally down-regulated 8 (NEDD8) to substrate. For neddylation to occur, three enzymes are required: activators (E1), conjugators (E2), and ligators (E3). However, the central role is played by the ubiquitin-conjugating enzymes E2M (UBE2M) and E2F (UBE2F), which are part of the E2 enzyme family. Recent understanding of the structure and mechanism of these two proteins provides insight into their physiological effects on apoptosis, cell cycle arrest and genome stability. To treat cancer, it is therefore appealing to develop novel inhibitors against UBE2M or UBE2F interactions with either E1 or E3. In this evaluation, we summarized the existing understanding of E2 interaction with E1 and E3 and reviewed the prospective of using neddylation E2 as a pharmacological target for evolving new anti-cancer remedies.

An Inflammation-Associated Prognosis Model for Hepatocellular Carcinoma Based on Adenylate Uridylate- (AU-) Rich Element Genes

Hepatocellular carcinoma (HCC) is a typical inflammation-driven cancer and ranks sixth in the incidence rate worldwide. The role of adenylate uridylate- (AU-) rich element genes (AREGs) in HCC remains unclear. HCC-related datasets were acquired from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database. Differentially expressed AREGs (DE-AREGs) between HCC samples and healthy controls were identified. The univariate Cox and LASSO analyses were performed to determine the prognostic genes. Furthermore, a signature and corresponding nomogram were configured for the clinical prediction of HCC. The potential signature-related biological significance was explored using functional and pathway enrichment analysis. Additionally, immune infiltration analysis was also performed. Finally, the expression of prognostic genes was verified using real-time quantitative polymerase chain reaction (RT-qPCR). A total of 189 DE-AREGs between normal and HCC samples were identified, wherein CENPA, TXNRD1, RABIF, UGT2B15, and SERPINE1 were selected to generate an AREG-related signature. Moreover, the prognostic accuracy of the AREG-related signature was also confirmed. Functional analysis indicated that the high-risk score was related to various functions and pathways. Inflammation and immune-related analyses indicated that the difference of T cell and B cell receptor abundance, microvascular endothelial cells (MVE), lymphatic endothelial cells (lye), pericytes, stromal cells, and the six immune checkpoints was statistically significant between the different risk groups. Similarly, RT-qPCR outcomes of these signature genes were also significant. In conclusion, an inflammation-associated signature based on five DE-AREGs was constructed, which could act as a prognostic indicator of patients with HCC.

NEDD8-conjugating enzyme E2s: critical targets for cancer therapy

NEDD8-conjugating enzymes, E2s, include the well-studied ubiquitin-conjugating enzyme E2 M (UBE2M) and the poorly characterized ubiquitin-conjugating enzyme E2 F (UBE2F). UBE2M and UBE2F have distinct and prominent roles in catalyzing the neddylation of Cullin or non-Cullin substrates. These enzymes are overexpressed in various malignancies, conferring a worse overall survival. Targeting UBE2M to influence tumor growth by either modulating several biological responses of tumor cells (such as DNA-damage response, apoptosis, or senescence) or regulating the anti-tumor immunity holds strong therapeutic potential. Multiple inhibitors that target the interaction between UBE2M and defective cullin neddylation protein 1 (DCN1), a co-E3 for neddylation, exhibit promising anti-tumor effects. By contrast, the potential benefits of targeting UBE2F are still to be explored. It is currently reported to inhibit apoptosis and then induce cell growth; hence, targeting UBE2F serves as an effective chemo-/radiosensitizing strategy by triggering apoptosis. This review highlights the most recent advances in the roles of UBE2M and UBE2F in tumor progression, indicating these E2s as two promising anti-tumor targets.

Cuproptosis-related immune checkpoint gene signature: Prediction of prognosis and immune response for hepatocellular carcinoma

Immune checkpoint genes (ICGs), the foundation of immunotherapy, are involved in the incidence and progression of hepatocellular carcinoma (HCC). Cuproptosis is characterized by copper-induced cell death, and this novel cell death pathway has piqued the interest of researchers in recent years. It is worth noting that there is little information available in the literature to determine the relationship between cuproptosis and anti-tumor immunity. We identified 39 cuproptosis-related ICGs using ICGs co-expressed with cuproptosis-related genes. A prognostic risk signature was constructed using the Cox regression and the least absolute shrinkage and selection operator analysis methods. The signature was built using the Cancer Genome Atlas (TCGA)-Liver Hepatocellular Carcinoma database. The TCGA and International Cancer Genome Consortium cohorts were classified into two groups; the low- and high-risk groups were determined using a prognostic signature comprised of five genes. The multivariate Cox regression analysis revealed that the signature could independently predict overall survival. Furthermore, the level of immune infiltration analysis revealed the robustness of the prognostic signature-immune cell infiltration relationship observed for Tregs, macrophages, helper T cells, and naive B cells. Both groups showed significant differences in immune checkpoint expression levels. The gene enrichment analysis was used for characterization, and the results revealed that enriching various pathways such as PI3K-AKT-mTOR signaling, glycolysis, Wnt/beta-catenin signaling, and unfolded protein response could potentially influence the prognosis of patients with HCC and the level of immune infiltration. The sensitivity of the two groups of patients to various drug-targeted therapy methods and immunotherapy was analyzed. In conclusion, the findings presented here lay the foundation for developing individualized treatment methods for HCC patients. The findings also revealed that studying the cuproptosis-based pathway can aid in the prognosis of HCC patients. It is also possible that cuproptosis contributes to developing anti-tumor immunity in patients.

Neddylation pattern indicates tumor microenvironment characterization and predicts prognosis in lung adenocarcinoma

Background: Lung adenocarcinoma (LUAD) is the most common type of lung cancer with a complex tumor microenvironment. Neddylation, as a type of post-translational modification, plays a vital role in the development of LUAD. To date, no study has explored the potential of neddylation-associated genes for LUAD classification, prognosis prediction, and treatment response evaluation.

Methods: Seventy-six neddylation-associated prognostic genes were identified by Univariate Cox analysis. Patients with LUAD were classified into two patterns based on unsupervised consensus clustering analysis. In addition, a 10-gene prognostic signature was constructed using LASSO-Cox and a multivariate stepwise regression approach.

Results: Substantial differences were observed between the two patterns of LUAD in terms of prognosis. Compared with neddylation cluster2, neddylation cluster1 exhibited low levels of immune infiltration that promote tumor progression. Additionally, the neddylation-related risk score correlated with clinical parameters and it can be a good predictor of patient outcomes, gene mutation levels, and chemotherapeutic responses.

Conclusion: Neddylation patterns can distinguish tumor microenvironment and prognosis in patients with LUAD. Prognostic signatures based on neddylation-associated genes can predict patient outcomes and guide personalized treatment.

Sample-Specific Perturbation of Gene Interactions Identifies Pancreatic Cancer Subtypes

Pancreatic cancer is a highly fatal disease and an increasing common cause of cancer mortality. Mounting evidence now indicates that molecular heterogeneity in pancreatic cancer significantly impacts its clinical features. However, the dynamic nature of gene expression pattern makes it difficult to rely solely on gene expression alterations to estimate disease status. By contrast, biological networks tend to be more stable over time under different situations. In this study, we used a gene interaction network from a new point of view to explore the subtypes of pancreatic cancer based on individual-specific edge perturbations calculated by relative gene expression value. Our study shows that pancreatic cancer patients from the TCGA database could be separated into four subtypes based on gene interaction perturbations at the individual level. The new network-based subtypes of pancreatic cancer exhibited substantial heterogeneity in many aspects, including prognosis, phenotypic traits, genetic mutations, the abundance of infiltrating immune cell, and predictive therapeutic efficacy (chemosensitivity and immunotherapy efficacy). The new network-based subtypes were closely related to previous reported molecular subtypes of pancreatic cancer. This work helps us to better understand the heterogeneity and mechanisms of pancreatic cancer from a network perspective.