Tumor promoting effects of glucagon receptor: a promising biomarker of papillary thyroid carcinoma via regulating EMT and P38/ERK pathways

Expression profiling and bioinformatics analysis of serum exosomal circular RNAs in lymph node metastasis of papillary thyroid carcinoma

Most papillary thyroid carcinoma (PTC) patients have a good prognosis, but lymph node metastasis (LNM) is the most common progressive manifestation and often leads to a poor-prognosis. However, few studies focused on the underlying mechanisms of LNM. This study aimed to identity the potential role of exosomal circRNAs that contribute to LNM in PTC. We found that 9000 aberrantly expressed exosomal circRNAs in PTC patients with LNM, including 684 observably upregulation and 2193 notably downregulation. Functional enrichment analyses indicated that these aberrantly expressed circRNAs were mainly enriched in a variety of molecules and signaling pathways related to the progression and LNM of PTC. Bioinformatics analysis screened 14 circRNA-miRNA-mRNA networks associated with LNM-related signaling pathways in PTC. Moreover, circTACC2-miR-7-EGFR and circBIRC6-miR-24-3p-BCL2L11 axes were verified for potential involvement in PTC with LNM. Additionally, 4 upregulated circRNAs-related hub genes and 8 hub genes associated with downregulated circRNAs were screened, some of which were involved in LNM of PTC through verification. Collectively, our data provided a novel framework for in-depth investigation of the function of dysregulated exosomal circRNAs and their potential biomarkers in PTC patients with LNM.

The impact of postoperative blood glucose levels on complications and prognosis after esophagectomy in patients with esophageal cancer

PURPOSE

This study aimed to characterize postoperative blood glucose fluctuation in patients who underwent esophagectomy for esophageal cancer, and to define its impact on complications and prognosis.

METHODS

The subjects of this retrospective study were 284 patients who underwent esophagectomy at Osaka University Hospital between 2015 and 2017. Data analyzed included clinicopathological background, the immediate postoperative blood glucose level (IPBG), postoperative blood glucose variability (PBGV), insulin dosage, postoperative complications, and prognosis.

RESULTS

The median IPBG and PBGV were 170 (64-260) mg/dl and 64.5 (11-217) mg/dl, respectively. Postoperative pneumonia was more common in patients with PBGV > 100 mg/dl (P = 0.015). Patients with IPBG < 170 mg/dl had significantly worse 5-year overall survival (OS) and 5-year recurrence-free survival (RFS) than those with IPBG > 170 mg/dl (54.5% vs. 80.4%, respectively, [P < 0.001] and 44.3% vs. 69.3%, respectively, [P = 0.001]). The 5-year OS rates were 43.5%, 68.3%, 80.6%, and 79.0% for patients with IPBG < 154, 154-170, 170-190, and ≥ 190 mg/dl, respectively. The corresponding 5-year RFS rates were 38.1%, 52.4%, 77.0%, and 61.3%, respectively. Multivariate analysis revealed that IPBG < 154 mg/dl and pathological stage were independent poor prognostic factors for OS.

CONCLUSION

PBGV was associated with postoperative pneumonia, and low IPBG was an independent poor prognostic factor for patients with esophageal cancer.

Construction of a predictive model for immunotherapy efficacy in lung squamous cell carcinoma based on the degree of tumor-infiltrating immune cells and molecular typing

Background

To construct a predictive model of immunotherapy efficacy for patients with lung squamous cell carcinoma (LUSC) based on the degree of tumor-infiltrating immune cells (TIIC) in the tumor microenvironment (TME).

Methods

The data of 501 patients with LUSC in the TCGA database were used as a training set, and grouped using non-negative matrix factorization (NMF) based on the degree of TIIC assessed by single-sample gene set enrichment analysis (GSEA). Two data sets (GSE126044 and GSE135222) were used as validation sets. Genes screened for modeling by least absolute shrinkage and selection operator (LASSO) regression and used to construct a model based on immunophenotyping score (IPTS). RNA extraction and qPCR were performed to validate the prognostic value of IPTS in our independent LUSC cohort. The receiver operating characteristic (ROC) curve was constructed to determine the predictive value of the immune efficacy. Kaplan–Meier survival curve analysis was performed to evaluate the prognostic predictive ability. Correlation analysis and enrichment analysis were used to explore the potential mechanism of IPTS molecular typing involved in predicting the immunotherapy efficacy for patients with LUSC.

Results

The training set was divided into a low immune cell infiltration type (C1) and a high immune cell infiltration type (C2) by NMF typing, and the IPTS molecular typing based on the 17-gene model could replace the results of the NMF typing. The area under the ROC curve (AUC) was 0.82. In both validation sets, the IPTS of patients who responded to immunotherapy were significantly higher than those who did not respond to immunotherapy (P = 0.0032 and P = 0.0451), whereas the AUC was 0.95 (95% CI = 1.00–0.84) and 0.77 (95% CI = 0.58–0.96), respectively. In our independent cohort, we validated its ability to predict the response to cancer immunotherapy, for the AUC was 0.88 (95% CI = 1.00–0.66). GSEA suggested that the high IPTS group was mainly involved in immune-related signaling pathways.

Conclusions

IPTS molecular typing based on the degree of TIIC in the TME could well predict the efficacy of immunotherapy in patients with LUSC with a certain prognostic value.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03565-7.

UBE2C promotes the progression of pancreatic cancer and glycolytic activity via EGFR stabilization-mediated PI3K-Akt pathway activation

BACKGROUND

Pancreatic cancer (PC) is among the most prevalent and deadliest endocrine tumors, yet the mechanisms governing its pathogenesis remain to be fully clarified. While ubiquitin-conjugating enzyme E2C (UBE2C) has been identified as an important oncogene in several cancers, its importance in PC has yet to be established.

METHODS

UBE2C expression in PC tumor samples and cell lines was examined via quantitative real-time polymerase chain reaction (qRT-PCR), while appropriate commercial kits were used to assess lactate production, ATP generation, and the uptake of glucose.

RESULTS

UBE2C was found to be upregulated in PC patient tumors and correlated with poorer survival outcomes. In PC cell lines, the silencing of this gene suppressed the malignant activity of cells, thus supporting its identification as an oncogene in this cancer type. Mechanistically, UBE2C was found to promote enhanced matrix metalloproteinase (MMP) protein expression via activating the PI3K-Akt pathway. Moreover, it was found to bind to the epidermal growth factor receptor (EGFR), stabilizing it and driving additional PI3K-Akt pathway activation. UBE2C knockdown in PC cells impaired their uptake of glucose and their ability to produce lactate and ATP.

CONCLUSIONS

In conclusion, the results of this study support a role for UBE2C as a driver of metastatic PC progression owing to its ability to bind to EGFR and to induce signaling via the PI3K-Akt pathway.

Association of a Novel Prognosis Model with Tumor Mutation Burden and Tumor-Infiltrating Immune Cells in Thyroid Carcinoma

Although immunotherapy has recently demonstrated a substantial promise in treating advanced thyroid carcinoma (THCA), it is not appropriate for all THCA patients. As a result, this study aims to identify biomarkers for predicting immunotherapy efficacy and prognosis in THCA patients based on a constructed prognostic model. The transcriptomic and corresponding clinical data of THCA patients were obtained from the Cancer Genome Atlas (TCGA) database. We identified differentially expressed genes (DEGs) between THCA and normal samples and performed an intersection analysis of DEGs with immune-related genes (IRGs) downloaded from the ImmPort database. Functional enrichment analysis was performed on the chosen immune-related DEGs. Subsequently, Cox and LASSO regression analyses were conducted to obtain three hub immune-related DEGs, including PPBP, SEMA6B, and GCGR. Following that, a prognostic risk model was established and validated based on PPBP, SEMA6B, and GCGR genes to predict immunotherapy efficacy and THCA prognosis. Finally, we investigated the association between the constructed risk model and tumor mutational burden (TMB), abundance of tumor-infiltrating immune cells (TICs) as well as immunotherapeutic targets (PDL-1, PD-1, and CTLA4) in THCA. THCA patients in the high-risk score (RS) group showed higher TMB levels and worse prognosis than the low RS group. Patients in the high-RS group had higher proportions of monocytes, M2 macrophages, and activated dendritic cells, whereas those in the low-RS group exhibited higher numbers of M1 macrophages and dendritic resting cells. Our data implied that the constructed THCA prognostic model was sound and we concluded that the THCA patients having high TMB and low PD-L1 expression levels might respond poorly to immunotherapy. Taken together, we constructed a novel prognostic model for THCA patients to predict their prognosis and immunotherapy efficacy, providing a viable option for the future management of THCA patients in the clinic.

Identification of the EMT-Related Genes Signature for Predicting Occurrence and Progression in Thyroid Cancer

BACKGROUND

The detection rate of thyroid cancer (TC) has been continuously improved due to the development of detection technology. Epithelial-mesenchymal transition (EMT) is thought to be closely related to the malignant progression of tumors. However, the relationship between EMT-related genes (ERGs) characteristics and the diagnosis and prognosis of TC patients has not been studied.

METHODS

Four datasets from Gene Expression Omnibus (GEO) were used to perform transcriptomic profile analysis. The overlapping differentially expressed ERGs (DEERGs) were analyzed using the R package "limma". Then, the hub genes, which had a higher degree, were identified by the protein-protein interaction (PPI) network. Gene expression analysis between the TC and normal data, the disease-free survival (DFS) analysis of TC patients from The Cancer Genome Atlas Thyroid Cancer (TCGA-THCA) cohort, function analysis, and immunohistochemistry (IHC) were performed to verify the importance of the hub genes. Finally, a prognostic risk scoring was constructed to predict DFS in patients with the selected genes.

RESULTS

A total of 43 DEERGs were identified and 10 DEERGs were considered hub ERGs, which had a high degree of connectivity in the PPI network. Then, the differential expressions of FN1, ITGA2, and KIT between TC and normal tissues were verified in the TCGA-THCA cohort and their protein expressions were also verified by IHC. DFS analysis indicated upregulations of FN1 expression (P<0.01) and ITGA2 expression (P<0.01) and downregulation of KIT expression (P=0.01) increased risks of decreased DFS for TCGA-THCA patients. Besides, by building a prognostic risk scoring model, we found that the DFS of TCGA-THCA patients was significantly worse in high-risk groups.

CONCLUSION

In summary, these hub ERGs were potential biomarkers for diagnosis and prognosis of TC, which can provide a basis for further exploring the efficacy of EMT in patients with TC.

Development of an Immune-Related Risk Signature for Predicting Prognosis in Lung Squamous Cell Carcinoma

Lung squamous cell carcinoma (LSCC) is the most common subtype of non-small cell lung cancer. Immunotherapy has become an effective treatment in recent years, while patients showed different responses to the current treatment. It is vital to identify the potential immunogenomic signatures to predict patient' prognosis. The expression profiles of LSCC patients with the clinical information were downloaded from TCGA database. Differentially expressed immune-related genes (IRGs) were extracted using edgeR algorithm, and functional enrichment analysis showed that these IRGs were primarily enriched in inflammatory- and immune-related processes. "Cytokine-cytokine receptor interaction" and "PI3K-AKT signaling pathway" were the most enriched KEGG pathways. 27 differentially expressed IRGs were significantly correlated with the overall survival (OS) of patients using univariate Cox regression analysis. A prognostic risk signature that comprises seven IRGs (GCCR, FGF8, CLEC4M, PTH, SLC10A2, NPPC, and FGF4) was developed with effective predictive performance by multivariable Cox stepwise regression analysis. Most importantly, the signature could be an independent prognostic predictor after adjusting for clinicopathological parameters, and also validated in two independent LSCC cohorts (GSE4573 and GSE17710). Potential molecular mechanisms and tumor immune landscape of these IRGs were investigated through computational biology. Analysis of tumor infiltrating lymphocytes and immune checkpoint molecules revealed distinct immune landscape in high- and low-risk group. The study was the first time to construct IRG-based immune signature in the recognition of disease progression and prognosis of LSCC patients.

MicroRNA hsa-mir-3923 serves as a diagnostic and prognostic biomarker for gastric carcinoma

Gastric carcinoma (GC) refers to a common digestive system disease that exhibits a very high incidence. MicroRNA hsa-mir-3923 belongs to a type of miRNA, of which the function has been merely investigated in breast, pancreatic cancers and pre-neoplasic stages of gastric cancer. It has not been studied or reported in gastric carcinoma, so the relationship between gastric hsa-mir-3923 expression and the clinics feature and pathology of GC cases was examined. This study employed data mining for analyzing gastric carcinoma data in The Cancer Genome Atlas database. A Chi squared test was performed for assessing the relations of hsa-mir-3923 expression with clinics-related and pathology-regulated variables. This study conducted the assessment of the role of hsa-mir-3923 in prognostic process using Kaplan-Meier curves, Receiver operating characteristic (ROC) analysis and proportional hazards model (Cox) study. With the use of Gene Expression Omnibus, this study carried out gene set enrichment analysis (GSEA). In the meantime, the common miRNA database was compared to predict potential target genes; as revealed by co-expression analysis, a regulatory network probably existed, containing hsa-mir-3923. For the analysis of the most tightly associated cytological behavior and pathway in GC, this study adopted the databases for Annotation, Visualization and Integrated Discovery (David) and KO-Based Annotation System (KOBAS). Cytoscape, R and STRING were employed for mapping probable regulatory networks displaying relations to hsa-mir-3923. Lastly, we obtained 69 genes most tightly associated with hsa-mir-3923 and described their relationship with Circos plot. As revealed from the results, hsa-mir-3923 displayed up-regulation in gastric carcinoma, and it displayed associations with vital status, N stage and histologic grade when being expressed. The predicted results of miRNA target genes suggested that there may be a close relationship between 66 genes and hsa-mir-3923 in gastric cancer. As indicated from co-expression data, a small regulating network of 4 genes probably existed. Our results elucidated that hsa-mir-3923 high-expression reveals poor prognosis of GC patients.