Thyroid carcinoma is one of the most common endocrine malignancies, in which papillary thyroid carcinoma (PTC) is the main pathotype. ANXA1 plays a significant role in many cancer types, but how it works in PTC has not been identified. MYC is a common transcript factor involved in tumorigenesis, development, invasion, and metastasis. The relation between ANXA1 and MYC has not been proved in PTC. In this study, firstly, we analyzed the expression and prognostic value of ANXA1 in pan-cancer using the data from the UCSC database. Then we explore the role of ANXA1 in PTC, including expression, prognostic value, and immune infiltration. In addition, we evaluated the relation between ANXA1 and the transcription factor MYC. Finally, we identified the expression of ANXA1 and MYC and then evaluated their function associated with proliferation and apoptosis in PTC cell lines by CCK8 proliferation and flow cytometry apoptosis experiment. We found that ANXA1 is up-regulated in PTC comparing with normal patients. High expression of ANXA1 was associated with adverse overall survival of PTC. ANXA1 may be regulated by MYC to promote the proliferation of PTC. MYC may regulate the expression of ANXA and thus affect the proliferation of PTC.

Background: RecQ mediated genome instability 2 ( RMI2 ) is an essential component of the BLM-TopoIIIa-RMI1- RMI2 (BTR) complex. However, the mysterious veil of the potential immunological relationship of RMI2 in tumorigenesis and development has not been revealed. Methods: We conducted the differential expression (DE) analysis of the RMI2 in pan-cancer using data onto Oncomine, TIMER, and GEPIA databases. Afterward, survival analysis and clinical-stage correlation analysis were performed via the TCGA database. Subsequently, we used R software to further explore the relationship between the expression level of RMI2 and tumor mutation burden (TMB), microsatellite instability (MSI), tumor microenvironment (TME), tumor immune-infiltrated cells (TILs), immune checkpoints (ICP), mismatch repairs (MMRs) -related genes, m6A-related genes, DNA methylation-related genes. Finally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional networks were also performed for annotation via gene set enrichment analysis (GSEA). Results: The RMI2 expressed remarkably high in most cancer types compared to cancer adjacent normal tissues ( P < 0.05). High expression of RMI2 was linked to unfavorable prognosis and advanced stage of disease, especially in LIHC and PAAD. RMI2 expression was related to TMB in 16 cancer types and MSI in 8 cancer types. Furthermore, it is significant positive correlations between RMI2 and stromal and immune cells, ICP-related genes, MMRs-related genes, m6A-related genes, and DNA methylation-related genes. Finally, GSEA analysis revealed that RMI2 was engaged in a variety of signaling pathways in pan-cancers. Conclusions: RMI2 may serve as a potential biological target and probably assume a crucial part in tumorigenesis and progression.

Hepatocellular carcinoma (HCC) is one of the most common types of cancer, and its treatment remains difficult. Since the early symptoms of HCC are not obvious, many HCC patients are already at an advanced stage of the disease at the time of diagnosis. Although current targeted therapy and immunotherapy have been initially effective in HCC patients, several patients have shown low response rates or developed drug resistance, which leads to tumor progression and even death. Hence, there is an urgent need for new biomarkers to guide the prognosis and treatment of HCC. In our study, a prognostic signature consisting of nine SLC genes was constructed in HCC by comprehensive analysis. By calculating risk scores, HCC patients could be divided into high-risk and low-risk groups, with the high-risk group having a significantly poorer prognosis. In addition, we found a hub gene, SLC7A11, which is a robust prognostic marker of HCC. In conclusion, our study can serve as a reference for the prognostic evaluation and treatment of HCC.

Background: Pancreatic cancer (PC), the most common fatal solid malignancy, has a very dismal prognosis. Clinical computerized tomography (CT) and pathological TNM staging are no longer sufficient for determining a patient’s prognosis. Although numerous studies have suggested that glycolysis is important in the onset and progression of cancer, there are few publications on its impact on PC.Methods: To begin, the single-sample gene set enrichment analysis (ssGSEA) approach was used to quantify the glycolysis pathway enrichment fraction in PC patients and establish its prognostic significance. The genes most related to the glycolytic pathway were then identified using weighted gene co-expression network analysis (WGCNA). The glycolysis-associated prognostic signature in PC patients was then constructed using univariate Cox regression and lasso regression methods, which were validated in numerous external validation cohorts. Furthermore, we investigated the activation of the glycolysis pathway in PC cell subtypes at the single-cell level, performed a quasi-time series analysis on the activated cell subtypes and then detected gene changes in the signature during cell development. Finally, we constructed a decision tree and a nomogram that could divide the patients into different risk subtypes, according to the signature score and their different clinical characteristics and assessed the prognosis of PC patients.Results: Glycolysis plays a risky role in PC patients. Our glycolysis-related signature could effectively discriminate the high-risk and low-risk patients in both the trained cohort and the independent externally validated cohort. The survival analysis and multivariate Cox analysis indicated this gene signature to be an independent prognostic factor in PC. The prognostic ROC curve analysis suggested a high accuracy of this gene signature in predicting the patient prognosis in PC. The single-cell analysis suggested that the glycolytic pathway may be more activated in epithelial cells and that the genes in the signature were also mainly expressed in epithelial cells. The decision tree analysis could effectively identify patients in different risk subgroups, and the nomograms clearly show the prognostic assessment of PC patients.Conclusion: Our study developed a glycolysis-related signature, which contributes to the risk subtype assessment of patients with PC and to the individualized management of patients in the clinical setting.