Predicting histologic grades for pancreatic neuroendocrine tumors by radiologic image-based artificial intelligence: a systematic review and meta-analysis.

Background: Accurate detection of the histological grade of pancreatic neuroendocrine tumors (PNETs) is important for patients' prognoses and treatment. Here, we investigated the performance of radiological image-based artificial intelligence (AI) models in predicting histological grades using meta-analysis. Method: A systematic literature search was performed for studies published before September 2023. Study characteristics and diagnostic measures were extracted. Estimates were pooled using random-effects meta-analysis. Evaluation of risk of bias was performed by the QUADAS-2 tool. Results: A total of 26 studies were included, 20 of which met the meta-analysis criteria. We found that the AI-based models had high area under the curve (AUC) values and showed moderate predictive value. The pooled distinguishing abilities between different grades of PNETs were 0.89 [0.84-0.90]. By performing subgroup analysis, we found that the radiomics feature-only models had a predictive value of 0.90 [0.87-0.92] with I2 = 89.91%, while the pooled AUC value of the combined group was 0.81 [0.77-0.84] with I2 = 41.54%. The validation group had a pooled AUC of 0.84 [0.81-0.87] without heterogenicity, whereas the validation-free group had high heterogenicity (I2 = 91.65%, P=0.000). The machine learning group had a pooled AUC of 0.83 [0.80-0.86] with I2 = 82.28%. Conclusion: AI can be considered as a potential tool to detect histological PNETs grades. Sample diversity, lack of external validation, imaging modalities, inconsistent radiomics feature extraction across platforms, different modeling algorithms and software choices were sources of heterogeneity. Standardized imaging, transparent statistical methodologies for feature selection and model development are still needed in the future to achieve the transformation of radiomics results into clinical applications. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42022341852.

FLNB overexpression promotes tumor progression and associates with immune suppression, evasion and stemness in pancreatic cancer.

Pancreatic cancer (PC) is an immunosuppressive cancer. Immune-based therapies that enhance or recruit antitumor immune cells into the tumor microenvironment (TME) remain promising strategies for PC treatment. Consequently, a deeper understanding of the molecular mechanisms involved in PC immune suppression is critical for developing immune-based therapies to improve survival rates. In this study, weighted gene co-expression network analysis (WGCNA) was used to identify Filamin B (FLNB) correlated with the infiltration of CD8+ T cells and tumor-associated macrophages (TAMs). The clinical significance and potential biological function of FLNB were evaluated using bioinformatic analysis. The oncogenic role of FLNB in PC was determined using in vitro and in vivo studies. We further analyzed possible associations between FLNB expression and tumor immunity using CIBERSORT, single sample gene set enrichment analysis, and ESTIMATE algorithms. We found FLNB was overexpressed in PC tissues and was correlated with poorer overall survival, tumor recurrence, larger tumor size, and higher histologic grade. Moreover, FLNB overexpression was associated with the mutation status and expression of driver genes, especially for KRAS and SMAD4. Functional enrichment analysis identified the role of FLNB in the regulation of cell cycle, focal adhesion, vascular formation, and immune regulation. Knockdown of FLNB expression inhibited cancer cell proliferation and migration in-vitro and suppressed tumor growth in-vivo. Furthermore, FLNB overexpression caused high infiltration of Treg cells, Th2 cells, and TAMs, but reduced infiltration of CD8+ T cells and Th1/Th2. Collectively, our findings suggest FLNB promotes PC progression and may be a novel biomarker for PC.

LINC00909 up-regulates pluripotency factors and promotes cancer stemness and metastasis in pancreatic ductal adenocarcinoma by targeting SMAD4.

BACKGROUND: Pancreatic cancer stem cells are crucial for tumorigenesis and cancer metastasis. Presently, long non-coding RNAs were found to be associated with Pancreatic Ductal Adenocarcinoma stemness characteristics but the underlying mechanism is largely known. Here, we aim to explore the function of LINC00909 in regulating pancreatic cancer stemness and cancer metastasis. METHODS: The expression level and clinical characteristics of LINC00909 were verified in 80-paired normal pancreas and Pancreatic Ductal Adenocarcinoma tissues from Guangdong Provincial People's Hospital cohort by in situ hybridization. RNA sequencing of PANC-1 cells with empty vector or vector encoding LINC00909 was experimented for subsequent bioinformatics analysis. The effect of LINC00909 in cancer stemness and metastasis was examined by in vitro and in vivo experiments. The interaction between LINC00909 with SMAD4 and the pluripotency factors were studied. RESULTS: LINC00909 was generally upregulated in pancreatic cancer tissues and was associated with inferior clinicopathologic features and outcome. Over-expression of LINC00909 enhanced the expression of pluripotency factors and cancer stem cells phenotype, while knock-down of LINC00909 decreased the expression of pluripotency factors and cancer stem cells phenotype. Moreover, LINC00909 inversely regulated SMAD4 expression, knock-down of SMAD4 rescued the effect of LINC00909-deletion inhibition on pluripotency factors and cancer stem cells phenotype. These indicated the effect of LINC00909 on pluripotency factors and CSC phenotype was dependent on SMAD4 and MAPK/JNK signaling pathway, another downstream pathway of SMAD4 was also activated by LINC00909. Specifically, LINC00909 was localized in the cytoplasm in pancreatic cancer cells and decreased the stability the SMAD4 mRNA. Finally, we found over-expression of LINC00909 not only accelerated tumor growth in subcutaneous mice models, but also facilitated tumorigenicity and spleen metastasis in orthotopic mice models. CONCLUSION: We demonstrate LINC00909 inhibits SMAD4 expression at the post-transcriptional level, which up-regulates the expression of pluripotency factors and activates the MAPK/JNK signaling pathway, leading to enrichment of cancer stem cells and cancer metastasis in pancreatic cancer.

Genome-wide CRISPR/Cas9 screening for drug resistance in tumors.

Genome-wide clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR associated nuclease 9 (Cas9) screening is a simple screening method for locating loci under specific conditions, and it has been utilized in tumor drug resistance research for finding potential drug resistance-associated genes. This screening strategy has significant implications for further treatment of malignancies with acquired drug resistance. In recent years, studies involving genome-wide CRISPR/Cas9 screening have gradually increased. Here we review the recent application of genome-wide CRISPR/Cas9 screening for drug resistance, involving mitogen-activated protein kinase (MAPK) pathway inhibitors, poly (ADP-ribose) polymerase inhibitors (PARPi), alkylating agents, mitotic inhibitors, antimetabolites, immune checkpoint inhibitors (ICIs), and cyclin-dependent kinase inhibitors (CDKI). We summarize drug resistance pathways such as the KEAP1/Nrf2 pathway MAPK pathway, and NF-κB pathway. Also, we analyze the limitations and conditions for the application of genome-wide CRISPR/Cas9 screening techniques.

A hierarchical prognostic model for Co-diabetes pancreatic adenocarcinoma.

Background: Co-diabetes pancreatic adenocarcinoma has a poorer prognosis than pancreatic adenocarcinoma without diabetes. This study aimed to develop a reliable prognostic model for patients with co-diabetes pancreatic adenocarcinoma. Method: Overall, 169 patients with co-diabetes pancreatic adenocarcinoma were included in our study. First, the independent risk factors affecting the prognosis of patients with co-diabetes pancreatic adenocarcinoma were determined by univariate and multivariate Cox regression analyses. Based on these identified risk factors, we developed a nomogram and evaluated its predictive ability using the concordance index, receiver operating characteristic curve, calibration plot, decision curve, and net reclassification index. Results: In this study, prealbumin, transferrin, carcinoembryonic antigen, distant metastasis, tumor differentiation neutrophil count, lymphocyte count and fasting blood glucose were confirmed as significant prognostic factors. Based on these predictors, a new nomogram was developed. Compared with the American Joint Committee on Cancer 8 staging system and other models, the nomogram achieved a higher concordance index in the training (0.795) and validation (0.729) queues. The area under the nomogram's curve for predicting patient survival at 0.5, 1, and 1.5 years in the training queue was >0.8. Patients were risk-stratified using the nomogram, and Kaplan-Meier survival curves of subgroups were plotted. The Kaplan-Meier curve also showed better separation than the American Joint Committee on Cancer 8 staging system, indicating that our model has a better risk hierarchical ability. Conclusions: Compared to the American Joint Committee on Cancer 8 staging system and other predictive models, our model showed better predictive ability for patients with co-diabetes pancreatic adenocarcinoma. Our model will help in patients' risk stratification and improves their prognosis.