An immune-related microRNA signature prognostic model for pancreatic carcinoma and association with immune microenvironment

Advances in pancreatic cancer diagnosis: from DNA methylation to AI-assisted imaging

INTRODUCTION

Pancreatic Cancer (PC) is a highly aggressive tumor that is mainly diagnosed at later stages. Various imaging technologies, such as CT, MRI, and EUS, possess limitations in early PC diagnosis. Therefore, this review article explores the various innovative biomarkers for PC detection, such as DNA methylation, Noncoding RNAs, and proteomic biomarkers, and the role of AI in PC detection at early stages.

AREA COVERED

Innovative biomarkers, such as DNA methylation genes, show higher specificity and sensitivity in PC diagnosis. Additionally, various non-coding RNAs, such as long non-coding RNAs (lncRNAs) and microRNAs, show high diagnostic accuracy and serve as diagnostic and prognostic biomarkers. Additionally, proteomic biomarkers retain higher diagnostic accuracy in different body fluids. Apart from this, the utilization of AI showed that AI surpassed the radiologist's diagnostic performance in PC detection.

EXPERT OPINION

The combination of AI and advanced biomarkers can revolutionize early PC detection. However, large-scale, prospective studies are needed to validate its clinical utility. Further. standardization of biomarker panels and AI algorithms is a vital step toward their reliable applications in early PC detection, ultimately improving patient outcomes. [Figure: see text].

Noncoding Ribonucleic Acids (RNAs) May Improve Response to Immunotherapy in Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC) is the seventh most common cause of cancer-related mortality. Despite different methods of treatment, nearly more than 90% of patients with PDAC die shortly after diagnosis. Contrary to promising results in other cancers, immune checkpoint inhibitors (ICIs) showed limited success in PDAC. Recent studies have shown that noncoding RNAs (ncRNAs) are extensively involved in PDAC cell-immune cell interaction and mediate immune evasion in this vicious cancer. PDAC cells recruit numerous ncRNAs to widely affect the phenotype and function of immune cells through various mechanisms. For instance, PDAC cells upregulate miR-301a and downregulate miR-340 to induce M2 polarization of macrophages or overexpress miR-203, miR-146a, and miR-212-3p to downregulate toll-like receptor 4 (TLR4), CD80, CD86, CD1a, major histocompatibility complex (MHC) II, and CD83, thereby evading recognition by dendritic cells. By downregulating miR-4299 and miR-153, PDAC cells can decrease the expression of NK group 2D (NKG2D) and MHC class I chain-related molecules A and B (MICA/B) to blunt the natural killer (NK) cell response. PDAC cells also highly express lncRNA AL137789.1, hsa_circ_0046523, lncRNA LINC00460, and miR-155-5p to upregulate immune checkpoint proteins and escape T cell cytotoxicity. On the other hand, ncRNAs derived from suppressive immune cells promote proliferation, invasion, and drug resistance in PDAC cells. ncRNAs can be applied to overcome resistance to ICIs, monitor the immune microenvironment of PDAC, and predict response to ICIs. This Review article comprehensively discusses recent findings regarding the roles of ncRNAs in the immune evasion of PDAC.

MicroRNAs in Pancreatic Cancer: Advances in Biomarker Discovery and Therapeutic Implications

Pancreatic cancer remains a formidable malignancy characterized by high mortality rates, primarily attributable to late-stage diagnosis and a dearth of effective therapeutic interventions. The identification of reliable biomarkers holds paramount importance in enhancing early detection, prognostic evaluation, and targeted treatment modalities. Small non-coding RNAs, particularly microRNAs, have emerged as promising candidates for pancreatic cancer biomarkers in recent years. In this review, we delve into the evolving role of cellular and circulating miRNAs, including exosomal miRNAs, in the diagnosis, prognosis, and therapeutic targeting of pancreatic cancer. Drawing upon the latest research advancements in omics data-driven biomarker discovery, we also perform a case study using public datasets and address commonly identified research discrepancies, challenges, and limitations. Lastly, we discuss analytical approaches that integrate multimodal analyses incorporating clinical and molecular features, presenting new insights into identifying robust miRNA-centric biomarkers.

Low UPB1 Level Correlates With Poor Prognosis in Lung Adenocarcinoma

OBJECTIVES

Lung adenocarcinoma (LUAD) is a critical cancer with high mortality, worse prognosis, and crucial lymphatic metastasis. Consequently, prognostic biomarkers for LUAD are truly required. β-Ureidopropionase (UPB1) is abnormally expressed in various cancers. However, the function of UPB1 in LUAD is still ambiguous. This study aimed to explore the expression profile and prognostic significance of UPB1 in LUAD.

MATERIALS AND METHODS

The differential UPB1 levels in pan cancers and their prognostic significance were comprehensively investigated through Gene Expression Profiling Interactive Analysis, UALCAN, Tumor Immune Estimation Resource, and Kaplan-Meier plotter platform. The correlation between UPB1 and tumor infiltration immune cells was explored using Tumor Immune Estimation Resource, Gene Expression Profiling Interactive Analysis, and Tumor-Immune System Interactions and Drug Bank database databases.

RESULTS

The UPB1 level was abnormally expressed in pan-tumor tissue than in adjacent tissue from The Cancer Genome Atlas tool. Low UPB1 level was correlated with poor overall survival in patients with LUAD. Furthermore, a comparison of the various pathologic characteristics of LUAD between high and low UPB1 level subgroups revealed that low UPB1 expression was correlated with lymph node metastasis. Kaplan-Meier survival analysis indicated that a low UPB1 level was associated with worse progression‑free survival and overall survival in patients with LUAD. Univariate and multivariate analyses suggested that UPB1 could be a useful prognostic indicator for LUAD. Abnormal UPB1 may be correlated with aberrant LUAD immune infiltration, prompting a worse survival outcome.

CONCLUSIONS

Results showed that low UPB1 is correlated with a worse prognosis of LUAD and may be a valuable prognostic indicator for LUAD.

SDC1 and ITGA2 as novel prognostic biomarkers for PDAC related to IPMN

The existing biomarkers are insufficient for predicting the prognosis of pancreatic ductal adenocarcinoma (PDAC). Intraductal papillary mucinous neoplasm (IPMN) is a precursor to PDAC; therefore, identifying biomarkers from differentially expressed genes (DEGs) of PDAC and IPMN is a new and reliable strategy for predicting the prognosis of PDAC. In this study, four datasets were downloaded from the Gene Expression Omnibus database and standardized using the R package 'limma.' A total of 51 IPMN and 81 PDAC samples were analyzed, and 341 DEGs in PDAC and IPMN were identified; DEGs were involved in the extracellular matrix and tumor microenvironment. An acceptable survival prognosis was demonstrated by SDC1 and ITGA2, which were highly expressed during in vitro PDAC cell proliferation, apoptosis, and migration. SDC1high was enriched in interferon alpha (IFN-α) response and ITGA2high was primarily detected in epithelial-mesenchymal transition (EMT), which was verified using western blotting. We concluded that SDC1 and ITGA2 are potential prognostic biomarkers for PDAC associated with IPMN. Downregulation of SDC1 and ITGA2 expression in PDAC occurs via a mechanism involving possible regulation of IFN-α response, EMT, and immunity, which may act as new targets for PDAC therapy.