Functionality is not an independent prognostic factor for pancreatic neuroendocrine tumors

Identifying target ion channel-related genes to construct a diagnosis model for insulinoma

Background: Insulinoma is the most common functional pancreatic neuroendocrine tumor (PNET) with abnormal insulin hypersecretion. The etiopathogenesis of insulinoma remains indefinable. Based on multiple bioinformatics methods and machine learning algorithms, this study proposed exploring the molecular mechanism from ion channel-related genes to establish a genetic diagnosis model for insulinoma. Methods: The mRNA expression profile dataset of GSE73338 was applied to the analysis, which contains 17 insulinoma samples, 63 nonfunctional PNET (NFPNET) samples, and four normal islet samples. Differently expressed ion channel-related genes (DEICRGs) enrichment analyses were performed. We utilized the protein-protein interaction (PPI) analysis and machine learning of LASSO and support vector machine-recursive feature elimination (SVM-RFE) to identify the target genes. Based on these target genes, a nomogram diagnostic model was constructed and verified by a receiver operating characteristic (ROC) curve. Moreover, immune infiltration analysis, single-gene gene set enrichment analysis (GSEA), and gene set variation analysis (GSVA) were executed. Finally, a drug-gene interaction network was constructed. Results: We identified 29 DEICRGs, and enrichment analyses indicated they were primarily enriched in ion transport, cellular ion homeostasis, pancreatic secretion, and lysosome. Moreover, the PPI network and machine learning recognized three target genes (MCOLN1, ATP6V0E1, and ATP4A). Based on these target genes, we constructed an efficiently predictable diagnosis model for identifying insulinomas with a nomogram and validated it with the ROC curve (AUC = 0.801, 95% CI 0.674-0.898). Then, single-gene GSEA analysis revealed that these target genes had a significantly positive correlation with insulin secretion and lysosome. In contrast, the TGF-beta signaling pathway was negatively associated with them. Furthermore, statistically significant discrepancies in immune infiltration were revealed. Conclusion: We identified three ion channel-related genes and constructed an efficiently predictable diagnosis model to offer a novel approach for diagnosing insulinoma.

Development and validation of a survival prediction model and risk stratification for pancreatic neuroendocrine neoplasms

PURPOSE

We explored risk variables associated with cancer-specific survival (CSS) in patients with pancreatic neuroendocrine neoplasms (PNENs) and created a network dynamic nomogram model to predict patient survival time.

METHODS

A total of 7750 patients with PNENs were included in this analysis, including 134 with functional PNENs and 7616 with nonfunctional PNENs. Clinical feature and prognosis differences between functional and nonfunctional PNENs were compared. Independent prognostic factors affecting CSS were analyzed by univariate and multifactorial Cox regression. Nomogram and web-based prognosis prediction of PNENs were developed and validated by C indices, decision curve analysis, and calibration plots.

RESULTS

Patients with functional PNENs were younger at diagnosis than those with nonfunctional PNENs. Functional PNENs had better prognoses than nonfunctional PNENs (5-year survival rates: 78.55% and 71.10%, respectively). Univariate and multifactorial Cox regression analyses showed that tumor infiltration (T), nodal metastasis (N), metastasis (M), tumor site, differentiation grade, age, marital status, and surgical treatment were independent prognostic risk factors for CSS, which were included in the prognostic nomogram and web-based prognosis calculator. The calibration plots and decision curve analysis showed that the nomogram had excellent prediction and clinical practical ability. The C indices for CSS in the training and validation cohorts were 0.848 (95% CI 0.838-0.8578) and 0.823 (95% CI 0.807-0.839), respectively. We scored all patients according to the nomogram and divided patients into three different risk groups. The prognosis of the low-risk population was significantly better than those of the middle- and high-risk populations based on Kaplan-Meier survival curve.

CONCLUSION

We analyzed the clinical features of PNENs and developed a convenient and web dynamic nomogram to predict CSS.

The incidence, prevalence, and survival analysis of pancreatic neuroendocrine tumors in the United States

PURPOSE

The incidence of pancreatic neuroendocrine tumors (pNETs) was increasing. The main purpose of this study was to statistically analyze the incidence and prevalence of pNETs and the main risk factors for the prognosis.

METHODS

Based on the Surveillance, Epidemiology, and End Results (SEER) database, with three registries integrated, this study comprehensively displayed the annual age adjust incidence of pNETs from 1975 to 2018, the estimated 20-year limited-duration prevalence, and conducted the univariate and multivariate survival analysis.

RESULTS

The incidence of pNETs has increased to about 1.5 per 100,000 population, and the prevalence has reached about 0.008% with the aged, Grade 1 and nonfunctional tumors accounting for the majority. The average median overall survival (OS), 5-year survival rate, and median disease-free survival (DFS) of pNETs patients from 1975 to 2018 were 85 months, 57.55%, and 220 months, respectively. From 2000 to 2018, the median OS was 94 months, and the 5-year survival rate was 59.94%. In multivariate survival analysis, the greatest risk factor was Grade 3&4 with HR = 3.62 (3.10-4.28), followed by distant stage with HR = 2.77 (2.28-3.36), and aged over 80 years old with HR = 2.26 (1.33-3.83). Surgery was a protective prognostic factor with HR = 0.34 (0.29-0.40).

CONCLUSION

The incidence and prevalence of pNETs were still increasing, but the trend was gradual and aging in recent years. The survival time of pNETs was longer but has not changed much in recent years. The degrees of malignancy, stage, and operation were the most important prognosis factors.

Impact of tumor grade on pancreatic neuroendocrine tumors

BACKGROUND/OBJECTIVE

Pancreatic neuroendocrine tumors (P-NETs) are highly heterogeneous with wide spectrum of biological behaviors and growth patterns. Here, we aimed to assess the impact of tumor grading on P-NETs prognosis and survival outcomes.

METHODS

Patients with P-NET were recruited to determine correlations between grades and clinicopathological factors, survival outcomes and prognostic factors.

RESULT

A total of 152 patients with P-NETs were enrolled. G1 P-NET were associated with significantly lower rates of perineural invasion, lymphovascular invasion, lymph node involvement and distant metastasis. The pancreatic head was the most common location of P-NETs. The 1-year, 5-year and 10-year overall survival rates of the patients were 94.4%, 89.1% and 78.8%, respectively. Majority of pancreatic neuroendocrine carcinoma (P-NEC) were unresectable (90.9%), and P-NECs patients had poor survival rates (1-year, 20% and no 5-year). Male sex, tumor size ≥2.5 cm, perineural invasion, lymph node invasion, metastasis, and advanced stage were significantly associated with poorer survival outcomes. Tumor grade and sex were independent survival predictors. Moreover, tumor grade was the most powerful prognostic factor.

CONCLUSIONS

Tumor grade, sex, perineural invasion, tumor size, lymph node involvement, metastasis, and stage are survival predictors for patients with P-NETs. Tumor grade is the most powerful independent prognostic factor.

Gender differences in pancreatic neuroendocrine neoplasms: A retrospective study based on the population of Hubei Province, China

OBJECTIVE

The aims of the present study were to investigate gender differences in the clinicopathological features, distant metastasis and prognosis of pancreatic neuroendocrine neoplasms (pNENs) in a Chinese population, and to identify any important gaps in the classification and management of pNENs relative to gender.

METHODS

Retrospective collection of the clinicopathological data of 193 patients with pathologically confirmed pNENs were analyzed and follow up was extended to observe the prognosis of the disease. Differences between genders in basic characteristics, clinical symptoms, comorbidities, and tumor parameters were analyzed.

RESULTS

There was no significant difference in females and males, however, moderately higher for females (52.8% vs. 47.2%), with the largest subgroup being 40~60 years of age (54.9%). Age at onset (P=0.002) and age at diagnosis (P=0.005) were both younger in females compared to males. Males lived more in urban areas and females lived more in rural areas (P=0.047). The proportion of smokers and alcohol drinkers was significantly higher in males than in females (P < 0.001). Non-functional pNENs were more frequent in males and functional pNENs in females (P=0.032). In women, functional status of the tumor was significantly associated with metastatic outcome (P=0.007) and functional tumors proved to be a protective factor compared to non-functional tumors (OR=0.090,95% CI: 0.011~ 0.752). There were no gender differences in tumor size, location, grade, stage or prognosis.

CONCLUSIONS

Gender differences in some clinicopathological features, and distant metastasis in patients with pNENs were identified, which suggested certain management details that justified emphasis based on gender.