Diagnostic value of digital droplet polymerase chain reaction and digital multiplexed detection of single-nucleotide variants in pancreatic cytology specimens collected by EUS-guided FNA

Golgi-apparatus genes related signature for predicting the progression-free interval of patients with papillary thyroid carcinoma

BACKGROUND

We aimed to build a novel model with golgi apparatus related genes (GaGs) signature and relevant clinical parameters for predicting progression-free interval (PFI) after surgery for papillary thyroid carcinoma (PTC).

METHODS

We performed a bioinformatic analysis of integrated PTC datasets with the GaGs to identify differentially expressed GaGs (DE-GaGs). Then we generated PFI-related DE-GaGs and established a novel GaGs based signature. After that, we validated the signature on multiple external datasets and PTC cell lines. Further, we conducted uni- and multivariate analyses to identify independent prognostic characters. Finally, we established a signature and clinical parameters-based nomogram for predicting the PFI of PTC.

RESULTS

We identified 260 DE-GaGs related to PFI in PTC. The functional enrichment analysis showed that the DE-MTGs were associated with an essential oncogenic glycoprotein biosynthetic process. Consequently, we established and optimized a novel 11 gene signature that could distinguish patients with poorer prognoses and predicted PFI accurately. The novel signature had a C-index of 0.78, and the relevant nomogram had a C-index of 0.79. Also, it was closely related to the pivotal clinical characters of and anaplastic potential in datasets and PTC cell lines. And the signature was confirmed a significant independent prognostic factor in PTC. Finally, we built a nomogram by including the signature and relevant clinical factors. Validation analysis showed that the nomogram's efficacy was satisfying in predicting PTC's PFI.

CONCLUSION

The GaGs signature and nomogram were closely associated with PTC prognosis and may help clinicians improve the individualized prediction of PFI, especially for high-risk patients after surgery.

Multiplex digital PCR assay to detect multiple KRAS and GNAS mutations associated with pancreatic carcinogenesis from minimal specimen amounts

Digital PCR (dPCR) allows for highly sensitive quantification of low-frequency mutations and facilitates early detection of cancer. However, low throughput targeting of single hotspots in dPCR hinders variant specification when multiple probes are used. Here we developed a dPCR method to simultaneously identify major variants related to pancreatic carcinogenesis. Using a 2-D plot of droplet fluorescence under the optimized concentration of two fluorescent probe pools, we determined the absolute quantification of different KRAS and GNAS variants. Successful detection of the multiple driver mutations was verified in 24 surgically resected tumor samples from 19 patients and 22 FNA samples from patients with pancreatic ductal adenocarcinoma. Precise quantification of the variant allele frequency was optimized using template DNA at a concentration as low as 1-10 ng. Furthermore, amplicons targeting multiple hotspots were successfully enriched with fewer false positives using high-fidelity polymerase, allowing for the detection of various KRAS and GNAS mutations with high probability in small cell/tissue specimens. Using this target enrichment, mutations at a rate of 90% in small residual tissues, such as the FNA needle flush and microscopic lesions in resected specimens, have successfully been identified. The proposed method allows for low-cost and accurate detection of driver mutations to diagnose cancers, even with minimal tissue collection.

A novel prognostic model for papillary thyroid cancer based on epithelial-mesenchymal transition-related genes

BACKGROUND

The frequent incidence of postsurgical recurrence issues in papillary thyroid cancer (PTC) patients is a primary concern considering the low cancer-related mortality. Previous studies have demonstrated that epithelial-mesenchymal transition (EMT) activation is closely related to PTC progression and invasion. In this study, we aimed to develop a novel EMT signature and ancillary nomogram to improve personalized prediction of progression-free interval (PFI).

METHODS

First, we carried out a differential analysis of PTC samples and pairwise normal thyroid samples to explore the differentially expressed genes (DEGs). The intersection of the DEGs with EMT-related genes (ERGs) were identified as differentially expressed EMT-related genes (DE-ERGs). We determined PFI-related DE-ERGs by Cox regression analysis and then established a novel gene classifier by LASSO regression analysis. We validated the signature in external datasets and in multiple cell lines. Further, we used uni- and multivariate analyses to identify independent prognostic characters.

RESULTS

We identified 244 prognosis-related DE-ERGs. The 244 DE-ERGs were associated with several pivotal oncogenic processes. We also constructed a novel 10-gene signature and relevant prognostic model for recurrence prediction of PTC. The 10-gene signature had a C-index of 0.723 and the relevant nomogram had a C-index of 0.776. The efficacy of the signature and nomogram was satisfying and closely correlated with relevant clinical parameters. Furthermore, the signature also had a unique potential in differentiating anaplastic thyroid cancer (ATC) samples.

CONCLUSIONS

The novel EMT signature and nomogram are useful and convenient for personalized management for thyroid cancer.

An update on the diagnosis of gastroenteropancreatic neuroendocrine neoplasms

Gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) arise from neuroendocrine cells found throughout the gastrointestinal tract and islet cells of the pancreas. The incidence and prevalence of GEP-NENs have been increasing each year due to higher awareness, improved diagnostic modalities, and increased incidental detection on cross-sectional imaging and endoscopy for cancer screening and other conditions and symptoms. GEP-NENs are a heterogeneous group of tumors and have a wide range in clinical presentation, histopathologic features, and molecular biology. Clinical presentation most commonly depends on whether the GEP-NEN secretes an active hormone. The World Health Organization recently updated the classification of GEP-NENs to introduce a distinction between high-grade neuroendocrine tumors and neuroendocrine carcinomas, which can be identified using histology and molecular studies and are more aggressive with a worse prognosis compared to high-grade neuroendocrine tumors. As our understanding of the biology of GEP-NENs has grown, new and improved diagnostic modalities can be developed and optimized. Here, we discuss clinical features and updates in diagnosis, including histopathological analysis, biomarkers, molecular techniques, and radiology of GEP-NENs. We review established diagnostic tests and discuss promising novel diagnostic tests that are currently in development or require further investigation and validation prior to broad utilization in patient care.