ObjectiveThe objective of this research was to screen prognostic related genes of thyroid papillary carcinoma (PTC) by single-cell RNA sequencing (scRNA-seq), to construct the diagnostic and prognostic models based on The Cancer Genome Atlas Thyroid Cancer (TCGA-THCA) data, and to evaluate the association between tumor immune microenvironment and the prognostic model.MethodThe differentially expressed genes (DEGs) and tumor evolution were analyzed by scRNA-seq based on public databases. The potential regulatory networks of DEGs related to prognosis were analyzed by multi-omics data in the THCA. Logistic regression and Cox proportional hazards regression were utilized to construct the diagnosis and prognostic model of PTC. The performance of the diagnostic model was verified by bulk RNA sequencing (RNA-seq) of our cohort. The tumor immune microenvironment associated with the prognostic model was evaluated using multi-omics data. In addition, qRT-PCR was performed on tumor tissues and adjacent normal tissues of 20 patients to verify the expression levels of DEGs.ResultsThe DEGs screened by scRNA-seq can distinguish between tumor and healthy samples. DEGs play different roles in the evolution from normal epithelial cells to malignant cells. Three DEGs ((FN1, CLU, and ANXA1)) related to prognosis were filtered, which may be regulated by DNA methylation, RNA methylation (m6A) and upstream transcription factors. The area under curve (AUC) of the diagnostic model based on 3-gene in the validation of our RNA-seq was 1. In the prognostic model based on 3-gene, the overall survival (OS) of high-risk patients was shorter. Combined with the clinical information of patients, a nomogram was constructed by using tumor size (pT) and risk score to quantify the prognostic risk. The age and tumor size of high-risk patients in the prognostic model were greater. In addition, the increase of tumor mutation burden (TMB) and diversity of T cell receptor (TCR), and the decrease of CD8+ T cells in high-risk group suggest the existence of immunosuppressive microenvironment.ConclusionWe applied the scRNA-seq pipeline to focus on epithelial cells in PTC, simulated the process of tumor evolution, and revealed a prognostic prediction model based on 3 genes, which is related to tumor immune microenvironment.

Anaplastic thyroid carcinoma (ATC) is an extremely aggressive tumor with a high mortality rate and poor prognosis. However, the pathogenesis of ATC is complex and poorly understood, and the effective treatment options are limited. Analysis of data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases showed that collagen triple helix repeat containing-1 (CTHRC1) was specifically upregulated in ATC tissues and was negatively correlated with overall survival (OS) in thyroid carcinoma patients. In vitro knockdown of CTHRC1 dramatically decreased the proliferation, migration, and invasion abilities of ATC cells, and in vivo studies in BALB/c nude mice confirmed that CTHRC1 knockdown significantly inhibited tumor growth. Mechanistically, CTHRC1 knockdown was found to suppress the Wnt/β-catenin pathway and epithelial-mesenchymal transition (EMT) at the protein level. These findings suggest that CTHRC1 promotes the progression of ATC via upregulating tumor cell proliferation, migration, and invasion, which may be achieved by activating the Wnt/β-catenin pathway and EMT.

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Purpose Characterizing tumor microenvironment by using single-cell RNA sequencing has been a promising strategy for cancer diagnosis and treatment. However, a few studies have focused on diagnosing papillary thyroid cancer (PTC) through this technology. Therefore, our study explored tumor microenvironment (TME) features and identified potential biomarkers to establish a diagnostic model for papillary thyroid cancer.Methods The cell types were identified using the markers from the CellMarker database and published research. The CellChat package was conducted to analyze the cell-cell interaction. The SCEVAN package was used to identify malignant thyroid cells. The SCP package was used to perform multiple single-cell downstream analyses, such as GSEA analysis, enrichment analysis, pseudotime trajectory analysis, and differential expression analysis. The diagnostic model of PTC was estimated using the calibration curves, receiver operating characteristic curves, and decision curve analysis. RT-qPCR was performed to validate the expression of candidate genes in human papillary thyroid samples.Results Eight cell types were identified in the scRNA-seq dataset by published cell markers. Extensive cell-cell interactions like FN1/ITGB1 existed in PTC tissues. We identified 26 critical genes related to PTC progression. Further, eight subgroups of PTC tumor cells were identified and exhibited high heterogenicity. The MDK/LRP1, MDK/ALK, GAS6/MERTK, and GAS6/AXL were identified as potential ligand-receptor pairs involved in the interactions between fibroblasts/endothelial cells and tumor cells. Eventually, the diagnostic model constructed by TRPC5, TENM1, NELL2, DMD, SLC35F3, and AUTS2 showed a good efficiency for distinguishing the PTC and normal tissues.Conclusions Our study comprehensively characterized the tumor microenvironment in papillary thyroid cancer. Through combined analysis with bulk RNA-seq, six potential diagnostic biomarkers were identified and validated. The diagnostic model we constructed was a promising tool for PTC diagnosis. Our findings provide new insights into the heterogenicity of thyroid cancer and the theoretical basis for diagnosing thyroid cancer.

Background. The incidence of pancreatic cancer continues to rise globally, with pancreatic head cancer accounting for nearly 60–70%. Pancreatic head cancer occurs mainly in people over the age of 60, and its morbidity and mortality increase with age. We investigated whether these elderly patients with nondistant metastases would benefit more from expanded pancreaticoduodenectomy (EPD) compared with standard pancreaticoduodenectomy (SPD). Methods. 3317 elderly patients with pancreatic head cancer from the SEER database were included in the study based on the inclusion and exclusion criteria. These patients were divided into a nonsurgical group and surgical group (including EPD and SPD). Univariate and multivariate Cox proportional hazards models were applied to identify the independent risk factors for cancer-specific survival (CSS). The survival differences between the nonsurgical group and surgical group were compared. Propensity score matching (PSM) methods were applied to balance covariates and reduce the interference of confounding variables. The two groups of patients were matched in a 1 : 1 ratio, and the covariates between the two groups were compared to verify the matching validity. The survival difference in different groups was compared after the matching analysis. Results. 3317 enrolled patients were divided into the surgical group (n = 984) and nonsurgical group (n = 2333). Before PSM, there were significant differences in overall survival (OS) and CSS between the nonsurgical group and surgical group (median OS: 8 months vs. 20 months, P < 0.001 ; median CSS: 8 months vs. 22 months, P < 0.001 ). The multivariate CSS Cox regression analysis demonstrated surgery is an independent risk factor. However, no significant differences were founded between the SPD and EPD groups (median OS: 20 months vs. 22 months, P = 0.636 ; median CSS: 22 months vs. 22 months, P = 0.270 ). After PSM, there were also no significant differences in OS and CSS between the SPD and EPD groups (median OS: 23 months vs. 18 months, P = 0.415 ; median CSS: 26 months vs. 18 months, P = 0.329 ). Conclusion. This study uses PSM to evaluate the effects of EPD and SPD for elderly patients with nondistant metastatic pancreatic head adenocarcinoma. It found that surgery is an independent prognostic factor, but expanded surgery has no survival advantage for these patients, whereas SPD provides a better survival advantage than EPD. SPD is a reasonable treatment option for these patients.