Resistance to anoikis is a key characteristic of many cancer cells, promoting cell survival. However, the mechanism of anoikis in hepatocellular carcinoma (HCC) remains unknown. In this study, we applied differentially expressed overlapping anoikis-related genes to classify The Cancer Genome Atlas (TCGA) samples using an unsupervised cluster algorithm. Then, we employed weighted gene coexpression network analysis (WGCNA) to identify highly correlated genes and constructed a prognostic risk model based on univariate Cox proportional hazards regression. This model was validated using external datasets from the International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO). Finally, we used a CIBERSORT algorithm to investigate the correlation between risk score and immune infiltration. Our results showed that the TCGA cohorts could be divided into two subgroups, with subgroup A having a lower survival probability. Five genes (BAK1, SPP1, BSG, PBK and DAP3) were identified as anoikis-related prognostic genes. Moreover, the prognostic risk model effectively predicted overall survival, which was validated using ICGC and GEO datasets. In addition, there was a strong correlation between infiltrating immune cells and prognostic genes and risk score. In conclusion, we identified anoikis-related subgroups and prognostic genes in HCC, which could be significant for understanding the molecular mechanisms and treatment of HCC.

The etching process on micropatterned Si (111) and silicon dioxide surfaces in 40% ammonium fluoride aqueous solution has been studied with atomic force microscopy. The etching rates of silicon and silicon dioxide are obtained from air-saturated and oxygen-free solutions. From the measurements at different temperatures (20–40 °C), the apparent activation energies are deduced. It is found that the etching rates are substantially different in silicon and silicon dioxide and that the dissolved oxygen in the solution facilitates the etching of silicon but obstructs it for silicon dioxide. It is also demonstrated that the thickness of the silicon dioxide film on the silicon substrate can be determined accurately from the jump of the etching rate at the SiO2/Si interface.

Data availabilityThe raw sequence data for all samples used in this study have been deposited in the public database of the National Omics Data Encyclopedia under project number OEP001296. These publicly deposited data (with controlled access on reasonable request) are available at https://www.biosino.org/node/ review/detail/OEV000137?code=CGCLNOL2.

BackgroundAlthough messenger RNA (mRNA) vaccines have unique advantages against multiple tumors, mRNA vaccine targets in stomach adenocarcinoma (STAD) remain unknown. The potential effectiveness of mRNA vaccines is closely associated with the tumor immune infiltration microenvironment. The present study aimed to identify tumor antigens of STAD as mRNA vaccine targets and systematically determine immune subtypes (ISs) of STAD that might be suitable for immunotherapy.MethodsGene expression profiles and clinical data of patients with gastric cancer were downloaded from The Cancer Genome Atlas (TCGA; n = 409) and the Gene Expression Omnibus (GEO; n = 433), and genomic data were extracted from cBioPortal. Differential gene expression was analyzed using the limma package, genetic alterations were visualized using maftools, and prognosis was analyzed using ToPP. Correlations between gene expression and immune infiltration were calculated using TIMER software, and potential ISs were identified using ConsensusClusterPlus. Functional enrichment was analyzed in clusterProfiler, and r co-expression networks were analyzed using the weighted gene co-expression network analysis (WGCNA) package in R.ResultsOverexpression of the prognostic and highly mutated antigens ADAMTS18, COL10A1, PPEF1, and STRA6 was associated with infiltration by antigen-presenting cells in STAD. Five ISs (IS1–IS5) in STAD with distinct prognoses were developed and validated in TCGA and GEO databases. The tumor mutational burden and molecular and clinical characteristics significantly differed among IS1–IS5. Both IS1 and IS2 were associated with a high mutational burden, massive infiltration by immune cells, especially antigen-presenting cells, and better survival compared with the other subtypes. Both IS4 and IS5 were associated with cold immune infiltration and correlated with advanced pathological stages. We analyzed the immune microenvironments of five subtypes of immune modulators and biomarkers to select suitable populations for mRNA vaccination and established four co-expressed key modules to validate the characteristics of the ISs. Finally, the correlation of these four mRNA vaccine targets with the transcription factors of DC cells, including BATF3, IRF4, IRF8, ZEB2, ID2, KLF4, E2-2, and IKZF1, were explored to reveal the underlying mechanisms.ConclusionsADAMTS18, COL10A1, PPEF1, and STRA6 are potential mRNA vaccine candidates for STAD. Patients with IS1 and IS2 are suitable populations for mRNA vaccination immunotherapy.

BackgroundPatients with stage I-III gastric cancer (GC) undergoing R0 radical resection display extremely different prognoses. How to discriminate high-risk patients with poor survival conveniently is a clinical conundrum to be solved urgently.MethodsPatients with stage I-III GC from 2010 to 2016 were included in our study. The associations of clinicopathological features with disease-free survival (DFS) and overall survival (OS) were examined via Cox proportional hazard model. Nomograms were developed which systematically integrated prognosis-related features. Kaplan–Meier survival analysis was performed to compare DFS and OS among groups. The results were then externally validated by The Sixth Affiliated Hospital, Sun Yat-sen University.ResultsA total of 585 and 410 patients were included in the discovery cohort and the validation cohort, respectively. T stage, N stage, lymphatic/vascular/nerve infiltration, preoperative CEA, and CA19-9 were independent prognostic factors (P < 0.05). Two prognostic signatures with a concordance index (C-index) of 0.7502 for DFS and 0.7341 for OS were developed based on the nomograms. The 3-year and 5-year calibration curves showed a perfect correlation between predicted and observed outcomes. Patients were divided into three risk groups (low, intermediate, high), and distinct differences were noticed (p < 0.001). Similar results were achieved in the validation cohort. Notably, a free website was constructed based on our signatures to predict the recurrence risk and survival time of patients with stage I-III GC.ConclusionsThe signatures demonstrate the powerful ability to conveniently identify distinct subpopulations, which may provide significant suggestions for individual follow-up and adjuvant therapy.