Background The rise in incidence and mortality of gastrointestinal mixed adenoneuroendocrine carcinoma (MANEC) has not been well focused. The aim of our study was to examine epidemiological trends in incidence and incidence-based (IB) mortality of gastrointestinal MANEC at a population level. Methods The incidence and IB mortality of gastrointestinal MANEC as well as data on affected patients from 2000 to 2016 were obtained from the Surveillance, Epidemiology, and End Results database. Trends in incidence and IB mortality were assessed using Joinpoint regression. The Kaplan–Meier method and log-rank test were used for survival analysis. Cox proportional hazards regression was used to identify independent predictors of mortality. Results 581 patients diagnosed with gastrointestinal MANEC were enrolled. Gastrointestinal MANEC incidence was 0.23 cases per 1,000,000 individuals in 2000 and 1.16 cases per 1,000,000 individuals in 2016, with an annual percent change (APC) of 8.0% (95% CI 5.7–10.3%, P < 0.05). IB mortality also showed a sustained increase (APC 12.9%, 95% CI 9.0–16.8%, P < 0.05). In Cox regression analysis, age at diagnosis, tumor grade and stage, lymph node metastasis, surgery, and tumor size were independently associated with mortality. Median survival was 75 months (95% CI 60–128 months). Median survival of appendiceal MANEC was significantly longer than that of cecal MANEC (115 vs. 31 months; P < 0.001). Conclusions We found a sustained and rapid increase both in incidence and IB mortality of gastrointestinal MANEC, manifesting that there has been no significant improvement in patient outcomes, nor progress in prevention and treatment. Additional resources should be devoted to gastrointestinal MANEC research.
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 Spearman’s correlation analysis. Pearson’s 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 NCAPG, non-SMC subunit in the concentrate I complex, might promote the proliferation of hepatocellular carcinoma (HCC), but the mechanism is unclear. The aim of this study was to explore how NCAPG affects PTEN to influence the proliferation of HCC. Methods Western blotting, qRT-PCR and immunohistochemistry were used to detect NCAPG expression in HCC tissues. The effect of NCAPG on the proliferation of HCC cell lines was evaluated using an EdU incorporation assay, a Cell Counting Kit-8 assay and Fluorescence in situ hybridization (FISH). BALB/c-nu/nu mice were used for the in vivo proliferation experiment. Transcriptome sequencing was used to determine the relationship between NCAPG and PTEN. Immunocoprecipitation-mass spectrometry (IP-MS), proteomic sequencing and Co-immunoprecipitation (CO-IP) were used to identify and examine the interaction between the NCAPG and CKII proteins. Results We confirmed that NCAPG was abnormally overexpressed in HCC and promoted the proliferation of HCC cells. Transcriptome sequencing revealed that NCAPG inhibited the transcription of PTEN and promoted the activation of the PI3K-AKT pathway. We found a close association between NCAPG and CKII through proteomic sequencing; their interaction was confirmed by Co-IP. There was a positive correlation between NCAPG and CKII that promoted the phosphorylation of PTEN and thus inhibited its transcription and functions. We also proved that CKII was the key factor in the induction of proliferation by NCAPG. Conclusion We revealed the mechanism by which NCAPG regulates the proliferation of HCC: NCAPG inhibits PTEN through its interaction with CKII, and then activates the PI3K-AKT pathway to promote the proliferation of HCC.
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.
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