Tianxing Dai

Background: Disulfide-dependent cell death, known as disulfide death, plays a pivotal regulatory role in the onset and progression of various cancers including pancreatic cancer. Despite its significance, little attention has been given to the study of disulfide death-related long non-coding RNAs (lncRNAs) in pancreatic cancer development and progression. Methods: This study utilized data from the Cancer Genome Atlas Project (TCGA) to analyze the transcriptome of pancreatic cancer. Co-expression analysis of genes associated with disulfide death was performed and six lncRNAs closely linked to disulfide death were identified through univariate and multivariate analysis. These lncRNAs were used to develop clinical prognostic models. The prognostic value of this model was then analyzed and further investigations included pathway enrichment analysis, tumor mutation load analysis, immune cell infiltration analysis, analysis of the tumor microenvironment (TME), and drug sensitivity analysis. Results: The developed prognostic model based on disulfide-associated lncRNAs exhibited significant prognostic value, allowing for reliable predictions of patient outcomes in pancreatic adenocarcinoma (PAAD). The analysis revealed that the six identified lncRNAs serve as independent prognostic factors, significantly correlating with patient survival and recurrence rates. Additionally, findings indicated notable differences in immune cell infiltration and drug sensitivity between high-risk and low-risk patient groups, suggesting potential therapeutic targets for enhancing treatment efficacy. Conclusions: Our findings revealed six disulfide death-associated lncRNAs with independent prognostic value, offering a crucial indicator for predicting the prognosis of pancreatic adenocarcinoma (PAAD) patients. Additionally, the analysis of tumor immune invasion and drug sensitivity provides a novel avenue for controlling tumor invasion and metastasis as well as reducing drug tolerance.

Purpose: Glycosylation has been demonstrated to be involved in tumorigenesis, progression, and immunoregulation, and to present specific profiles in different tumors. In this study, we aimed to explore the specific glycosylation-related gene (GRG) signature and its potential immunological roles and prognostic implications in hepatocellular carcinoma (HCC). Patients and Methods: The GRG expression profile was defined using the transcriptome data from The Cancer Genome Atlas and Gene Expression Omnibus. Univariate and the least absolute shrinkage and selection operator Cox analyses were performed to develop a GRG-based risk score model. A nomogram was subsequently established and validated. Its correlation with cancer immune microenvironment and drug susceptibility was further analyzed. The role and immunological correlation of ST6GALNAC4 were further experimentally validated at the tissue and cellular levels in HCC. Results: A total of 87 GRGs were identified to be significantly dysregulated in HCC, and a novel risk score model was constructed using eight critical GRGs, which demonstrated superior prognostic discrimination and predictive power in both training and validation groups. High risk scores in HCC patients were associated with lower OS. The model was also identified as an independent risk factor for HCC, and a novel nomogram was subsequently constructed and validated. Notably, significant correlations were found in risk scores with immune cells infiltration, tumor immunophenotyping, immune checkpoint genes’ expression, and sensitivities to multiple drugs. Furthermore, we validated in local HCC samples that ST6GALNAC4 was significantly upregulated and its knockdown significantly inhibited the tumor proliferation, migration and invasion ability and affected the expression of immune checkpoints on hepatoma cells. Conclusion: We identified a novel GRG-based model which showed significant prognostic and immunological correlations in HCC, and the oncogenic role of ST6GALNAC4 has been validated and may serve as a potential drug target.

Background: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors worldwide, and its prognosis remains unsatisfactory. The identification of new and effective markers is helpful for better predicting the prognosis of patients with HCC and for conducting individualized management. The oncogene Aurora kinase A (AURKA) is involved in a variety of tumors; however, its role in liver cancer is poorly understood. The aim of this study was to establish AURKA-related gene signatures for predicting the prognosis of patients with HCC.Methods: We first analyzed the expression of AURKA in liver cancer and its prognostic significance in different data sets. Subsequently, we selected genes with prognostic value related to AURKA and constructed a gene signature based on them. The predictive ability of the gene signature was tested using the HCC cohort development and verification data sets. A nomogram was constructed by integrating the risk score and clinicopathological characteristics. Finally, the influence of the gene signature on the immune microenvironment in HCC was comprehensively analyzed.Results: We found that AURKA was highly expressed in HCC, and it exhibited prognostic value. We selected eight AURKA-related genes with prognostic value through the protein-protein interaction network and successfully constructed a gene signature. The nine-gene signature could effectively stratify the risk of patients with HCC and demonstrated a good ability in predicting survival. The nomogram showed good discrimination and consistency of risk scores. In addition, the high-risk group showed a higher percentage of immune cell infiltration (i.e., macrophages, myeloid dendritic cells, neutrophils, and CD4+T cells). Moreover, the immune checkpoints SIGLEC15, TIGIT, CD274, HAVCR2, and PDCD1LG2 were also higher in the high-risk group versus the low-risk group.Conclusions: This gene signature may be useful prognostic markers and therapeutic targets in patients with HCC.

Purpose: The aldo-keto reductase (AKR) superfamily members have been proposed with multiple roles in various tumors. Here, a comprehensive analysis on the integral role of AKR genes was conducted to evaluate the expression profile, regulation network, and prognostic significance in hepatocellular carcinoma (HCC). Materials and Methods: Transcriptome datasets of HCC were obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus. Univariate and multivariate Cox regression analyses were used to build a novel risk score model, and then were further used to identify independent prognostic factors for overall survival (OS) of HCC. A prognostic nomogram was developed and validated. The expression of these critical AKR members was also evaluated by quantitative real-time polymerase chain reaction and immunohistochemistry in HCC specimens. Results: Eight differentially expressed AKR genes were identified in HCC. The dysregulation of most AKR genes was negatively correlated with DNA methylation, and a regulation network with transcription factors (TFs) was also established. Then, three critical AKR genes (AKR1B10, AKR1D1, and AKR7A3) were screened out to build a novel risk score model. Worse OS was observed in high-risk patients. Besides, a prognostic nomogram based on the model was further established and validated in both the TCGA and GSE14520 cohorts, which showed superior performance in predicting the OS of HCC patients. Notably, close correlations were identified between the risk score and tumor immune microenvironment, somatic mutation profiles, and drug susceptibilities of HCC. Finally, the upregulated AKR1B10 and downregulated AKR1D1 and AKR7A3 were further verified in HCC tumor and adjacent tissues from our institution. Conclusion: The dysregulated AKR genes could be mediated by DNA methylation and TFs in HCC. The risk model established with superior prognostic performance further suggested the significant role of AKR genes involved in the progression of HCC.

Purpose: Ferroptosis, as a novel regulated cell death form, has a close interaction with metabolism, which is largely unknown in cancer. In the present study, we conducted a comprehensive analysis of ferroptosis-related metabolic genes to delineate the metabolic signatures induced by ferroptosis and evaluate its prognostic significance in hepatocellular carcinoma (HCC). Methods: The ferroptosis-related metabolic genes (Fer-MRGs) were identified by correlation analyses with transcriptome data from The Cancer Genome Atlas and Gene Expression Omnibus. Then, univariate and the least absolute shrinkage and selection operator Cox regression analysis was used to establish a novel risk score model. Univariate and multivariate COX analyses were used to identify independent prognostic factors for overall survival (OS) of HCC, and a nomogram was developed. The Fer-MRGs’ expression was further evaluated by quantitative real-time polymerase chain reaction in HCC. Results: A total of 77 metabolic genes were identified as Fer-MRGs, and 26 were found with prognostic values for OS of HCC. Then, a novel nine-gene (AKR1C3, ATIC, G6PD, GMPS, GNPDA1, IMPDH1, PRIM1, RRM2, and TXNRD1) risk score model was constructed. Survival analyses showed worse OS in high-risk patients both in the training and validation groups. The model was also identified as an independent prognostic factor for HCC, and a prognostic nomogram for OS was further established with superior discriminative capacity and prediction accuracy. Notably, close correlations were also identified between the risk score and the expression of immune checkpoint genes, immune subtypes of tumor, and susceptibility of HCC to chemotherapeutic agents. Finally, elevated expression of eight Fer-MRGs (except for IMPDH1) was further verified in 16 pairs of HCC tumor and adjacent tissues. Conclusion: These results indicated the intense interaction between ferroptosis and metabolism, the significant role of ferroptosis-related MRGs, and the great potential of the novel risk score model for prognosis prediction in HCC.

The paper aims to assess the association between Hepatitis B Virus infection and colorectal liver metastasis by conducting a meta-analysis. The relevant studies were searched until 24 July 2020, Studies that assessed the correlation between HBV infection and CRLM were recruited. A random effects model was applied to calculate the odds ratio (OR) with 95% confidence interval (CI). All data analyses were performed by STATA 12.0 software. Ten studies involving 17529 participants were included in the study. The results shown that there was obvious association between HBV infection and CRLM (OR: 0.51, 95% CI: 0.28–0.91). The study type and case–control rate may be the main causes of heterogeneity. In addition, HBV infection had no association with extrahepatic metastasis or prognosis of patients with CRLM. Sensitivity analyses confirmed that the results were stable, and Egg’s test indicated that there was no publication bias. Patients with HBV infection have the reduced risk of CRLM. GRAPHICAL ABSTRACT

Background: Neutrophil-to-lymphocyte ratio (NLR) has been demonstrated a significant association with the prognosis of hepatocellular carcinoma (HCC). The current study aimed to evaluate the prognostic value of NLR at different time points in HCC patients receiving liver resection. Methods: Data were retrospectively collected from 195 HCC patients receiving liver resection. The preoperative NLR (pre-NLR), postoperative NLR (post-NLR) and corresponding changes of NLR (NLRc) at different time points were calculated. The disease-free survival (DFS) and overall survival (OS) was calculated by the Kaplan-Meier method and compared by the log-rank test. Both univariate and multivariate analyses were performed to evaluate their prognostic values for DFS and OS. And the prognostic significance of pre-NLR, post-NLRs, and NLRcs were further evaluated with subgroup analysis and with early and late recurrence of HCC. Results: Pre-NLR was not significantly correlated with DFS or OS (both P>0.05), whereas higher post-NLR at 4–8 weeks [NLR (4–8 w)] and 3–6 months [(NLR (3–6 m)] predicted worse DFS (P=0.023 and Pvs. 0.572) and OS (0.650 vs. 0.621). Multivariate analyses showed that NLRc (4–8 w) was not a significant predictor of DFS (P=0.369) or OS (P=0.173), while the NLRc (3–6 m) with 25% increase was found to be an independent factor for adverse DFS in patients with HCC (P=0.041). The AUC of NLRc (3–6 m) for DFS was 0.600. Subgroup analysis showed NLR (3–6 m) was significantly corrected to DFS (PConclusions: The post-NLRs are significant predictors of clinical outcome in HCC patients receiving liver resection, and post-NLR and NLRc with a relatively long-term interval after operation have better prognostic values.

BACKGROUND:Inflammation and activation of the coagulation cascades have a role in the pathogenesis of malignancy, including hepatocellular carcinoma (HCC). This retrospective study aimed to investigate the prognostic role of the combined fibrinogen a...