Background: The application of molecular targeting therapy and immunotherapy has notably prolonged the survival of patients with hepatocellular carcinoma (HCC). However, multidrug resistance and high molecular heterogeneity of HCC still prevent the further improvement of clinical benefits. Dysfunction of tumor-infiltrating natural killer (NK) cells was strongly related to HCC progression and survival benefits of HCC patients. Hence, an NK cell-related prognostic signature was built up to predict HCC patients’ prognosis and immunotherapeutic response. Methods: NK cell markers were selected from scRNA-Seq data obtained from GSE162616 data set. A consensus machine learning framework including a total of 77 algorithms was developed to establish the gene signature in TCGA–LIHC data set, GSE14520 data set, GSE76427 data set and ICGC–LIRI–JP data set. Moreover, the predictive efficacy on ICI response was externally validated by GSE91061 data set and PRJEB23709 data set. Results: With the highest C-index among 77 algorithms, a 11-gene signature was established by the combination of LASSO and CoxBoost algorithm, which classified patients into high- and low-risk group. The prognostic signature displayed a good predictive performance for overall survival rate, moderate to high predictive accuracy and was an independent risk factor for HCC patients’ prognosis in TCGA, GEO and ICGC cohorts. Compared with high-risk group, low-risk patients showed higher IPS–PD1 blocker, IPS–CTLA4 blocker, common immune checkpoints expression but lower TIDE score, which indicated low-risk patients might be prone to benefiting from ICI treatment. Moreover, a real-world cohort, PRJEB23709, also revealed better immunotherapeutic response in low-risk group. Conclusions: Overall, the present study developed a gene signature based on NK cell-related genes, which offered a novel platform for prognosis and immunotherapeutic response evaluation of HCC patients.

Background. Colon adenocarcinoma (COAD) is a malignancy with a high incidence and is associated with poor quality of life. Dysfunction of circadian clock genes and disruption of normal rhythms are associated with the occurrence and progression of many cancer types. However, studies that systematically describe the prognostic value and immune-related functions of circadian clock genes in COAD are lacking. Methods. Genomic data obtained from The Cancer Genome Atlas (TCGA) database was analyzed for expression level, mutation status, potential biological functions, and prognostic performance of core circadian clock genes in COAD. Their correlations with immune infiltration and TMB/MSI score were analyzed by Spearmans correlation analysis. Pearsons correlation analysis was performed to analyze their associations with drug sensitivity. Lasso Cox regression analysis was performed to construct a prognosis signature. Moreover, an mRNA-miRNA-lncRNA regulatory axis was also detected by ceRNA network. Results. In COAD tissues, the mRNA levels of CLOCK, CRY1, and NR1D1 were increased, while the mRNA levels of ARNTL, CRY2, PER1, PER3, and RORA were decreased. We also summarized the relative genetic mutation variation landscape. GO and KEGG pathway analyses demonstrated that these circadian clock genes were primarily correlated with the regulation of circadian rhythms and glucocorticoid receptor signaling pathways. COAD patients with high CRY2, NR1D1, and PER2 expression had worse prognosis. A prognostic model constructed based on the 9 core circadian clock genes predicted the COAD patients overall survival with medium to high accuracy. A significant association between prognostic circadian clock genes and immune cell infiltration was found. Moreover, the lncRNA KCNQ1OT1/hsa-miRNA-32-5p/PER2/CRY2 regulatory axis in COAD was also detected through a mRNA-miRNA-lncRNA network. Conclusion. Our results identified CRY2, NR1D1, and PER2 as potential prognostic biomarkers for COAD patients and correlated their expression with immune cell infiltration. The lncRNA KCNQ1OT1/hsa-miRNA-32-5p/PER2/CRY2 regulatory axis was detected in COAD and might play a vital role in the occurrence and progression of COAD.

Background. Liver hepatocellular carcinoma (LIHC) is a malignance with high incidence and recurrence. Pyroptosis is a programed cell death pattern which both activates the effective immune response and causes cell damage. However, their potential applications of pyroptosis-related genes (PRGs) in the prognostic evaluation and immunotherapy of LIHC are still rarely discussed. Methods. Comprehensive bioinformatics analyses based on TCGA-LIHC dataset were performed in the current study. Results. A total of 33 PRGs were selected to perform the current study. Of these 33 PRGs, 26 PRGs with upregulation or downregulation in LIHC tissues were identified. We also summarized the related genetic mutation variation landscape. GO and KEGG pathway analysis demonstrated that these 26 PRGs were primarily associated with pyroptosis, positive regulation of interleukin-1 beta production, and NOD-like receptor signaling pathway. An unfavorable OS appeared in LIHC patients with high CASP3, CASP4, CASP6, CASP8, GPX4, GSDMA, GSDME, NLRP3, NLRP7, NOD1, NOD2, PLCG1, and SCAF11 expression and low NLRP6 expression. A prognostic signature constructed by the above 14 prognostic PRGs had moderate to high accuracy to predict LIHC patients prognosis. And risk score was correlated with the expression of CASP6, CASP8, GPX4, GSDMA, GSDME, NLRP6, and NOD2. Of these 7 genes, CASP8 was identified as the core gene in PPI network. Moreover, lncRNA MIR17HG/hsa-miRNA-130b-3p/CASP8 regulatory axis in LIHC was also detected. Conclusions. The current study indicated the crucial role of PRGs in the prognostic evaluation of LIHC patients and their correlations with tumor microenvironment in LIHC.

Background The feasibility and safety of laparoscopic major hepatectomy (LMH) are still uncertain. The purpose of the present study is to compare the short- and long-term outcomes of LMH with those of open major hepatectomy (OMH) for hepatocellular carcinoma (HCC). Method Between January 2012 and December 2018, a total of 26 patients received laparoscopic major hepatectomy in our center. To minimize any confounding factors, a 1:3 case-matched analysis was conducted based on the demographics and extent of liver resection. Data of demographics, perioperative outcomes, and long-term oncologic outcomes were reviewed. Results Intraoperative blood loss ( P = 0.007) was significantly lower in the LMH group. In addition, the LMH group exhibited a lower overall complication rate ( P = 0.039) and shorter postoperative hospital stay ( P = 0.024). However, no statistically significant difference was found between LMH and OMH regarding operation time ( P = 0.215) and operative cost ( P = 0.860). Two laparoscopic cases were converted to open liver resection. In regard to long-term outcomes, there was no significant difference between LMH and OMH regarding disease-free survival (DFS) ( P = 0.079) and overall survival (OS) ( P = 0.172). Conclusion LMH can be an effective and safe alternative to OMH for selected patients with liver cancer in short- and long-term outcomes.

Background. Liver hepatocellular carcinoma (LIHC) ranks the sixth in global cancer incidence with poor prognosis. Necroptosis is a kind of regulated cell death and has been proved to be of significance in cancer occurrence and progression. However, few studies comprehensively discuss the potential applications of necroptosis-related genes (NRGs) in the prognostic evaluation and immunotherapy of LIHC. Methods. The prognostic signature in the present study was built up using LASSO Cox regression analysis. Integrated bioinformatics tools were utilized to explore the potential mRNA-miRNA-lncRNA regulatory axis in LIHC. Furthermore, qRT-PCR method was used to verify the EZH2 expression in LIHC tissues. Furthermore, prognostic performance of EZH2 in LIHC was assessed by Kaplan-Meier method. Results. A total of 14 NRGs were differentially expressed in LIHC tissues. The overall genetic mutation status of these NRGs in LIHC was also shown. NRGs were significantly correlated with programmed necrotic cell death, as well as Toll-like receptor signaling pathway in GO and KEGG pathway analysis. Kaplan-Meier analysis revealed that ALDH2, EZH2, NDRG2, PGAM5, RIPK1, and TRAF2 were related to the prognosis. A prognostic signature was constructed by these six genes and showed medium to high accuracy in the prediction of LIHC patients prognosis. Further analysis revealed that NRGs were correlated with pathological stage, immune infiltration, and drug resistance in LIHC. Moreover, we identified a potential lncRNA TUG1/miR-26b-5p/EZH2 regulatory axis in LIHC, which might affect the progression of LIHC. qRT-PCR suggested a higher mRNA level of EZH2 in LIHC tissues. And a poor overall survival rate was detected in LIHC patients with high EZH2 expression. Moreover, EZH2 expression and cancer stage were identified as the independent risk factors affecting LIHC patients prognosis. Conclusion. In the present study, we conducted comprehensive bioinformatic analyses and built up a necroptosis-related prognostic signature containing four genes (ALDH2, EZH2, NDRG2, and PGAM5) for patients with LIHC, and this prognostic signature showed a medium to high predictive accuracy. And our study also identified a lncRNA TUG1/miR-26b-5p/EZH2 regulatory axis, which might be of great significance in LIHC progression. In addition, based on the data from our center, the result of qRT-PCR and survival analysis showed a higher mRNA level of EZH2 in LIHC tissues and an unfavorable prognosis in high EZH2 expression group, respectively.

Stomach adenocarcinoma (STAD) is one of the subtype of gastric cancer with high invasiveness, extreme heterogeneity, high morbidity, and high mortality. The degradome is the most abundant class of cellular enzymes that play an essential role in regulating cellular activity and carcinogenesis. An integrative machine learning procedure including 10 methods was performed to develop a prognostic degradome-based prognostic signature (DPS) in TCGA, GSE15459, GSE26253, and GSE62254 datasets. Investigations of the DPS concerning immune infiltration, immunotherapy benefits, and drug priority were orchestrated. The DPS developed by Enet [alpha = 0.3] method was regarded as the optimal prognostic model. The DPS had a stable and powerful performance in predicting the clinical outcome of STAD and served as an independent risk factor in training and testing cohorts. The C-index of DPS was higher than that of age, sex, and clinical stage. STAD patients with low DPS scores had a higher abundance of B cells, CD8⁺ T cells, higher cytolytic scores, and T cell co-stimulation scores. Moreover, low DPS score indicated a lower tumor immune dysfunction and exclusion score, lower T cell dysfunction and exclusion score, higher PD1&CTLA4 immunophenoscore, and higher tumor mutation burden score in STAD, demonstrating a better immunotherapy response. STAD patients with a high DPS score had a lower IC50 value of common chemotherapy and targeted therapy regimens (Cisplatin, Docetaxel, Gefitinib, etc). Our study developed an optimal DPS for STAD. The DPS could predict the prognosis, risk stratification and guide treatment for STAD patients.