A prognostic and immunotherapy effectiveness model for pancreatic adenocarcinoma based on cuproptosis-related lncRNAs signature

Prognostic Significance and Immune Landscape of a Cuproptosis-Related LncRNA Signature in Ovarian Cancer

Background: Cuproptosis is a copper-induced mitochondrial cell death, and regulating cuproptosis is becoming a rising cancer treatment modality. Here, we attempted to establish a cuproptosis-associated lncRNAs (CRLs) signature (CRlncSig) to predict the survival, immune landscape, and treatment response in ovarian cancer (OC) patients. Methods: A series of statistical analyses were used to identify the key CRLs that are closely related to the prognosis, and a prognostic CRlncSig was constructed. The predictive accuracy of the CRlncSig was further validated in an independent Gene Expression Omnibus (GEO) set. Then, we compared the immune cell infiltration, immune checkpoints, tumor microenvironment (TME), tumor mutational burden (TMB), drug sensitivity, and efficacy of immunotherapy between the two subgroups. We further built a nomogram integrating the CRlncSig and different clinical traits to enhance the clinical application of the CRlncSig. Results: Nine hub CRLs, namely RGMB-AS1, TYMSOS, DANCR, LINC00702, LINC00240, LINC00996, DNM1P35, LINC00892, and TMEM254-AS1, were correlated with the overall survival (OS) of OC and a prognostic CRlncSig was established. The CRlncSig classified OC patients into two risk groups with strikingly different survival probabilities. The time-dependent ROC (tdROC) curves demonstrated good predictive ability in both the training cohort and an independent validation cohort. Multivariate analysis confirmed the independent predictive performance of the CRlncSig. We constructed a nomogram based on the CRlncSig, which can predict the prognosis of OC patients. The high-risk score was characterized by decreased immune cell infiltration and activation of stroma, while activation of immunity was observed in the low-risk subgroup. Moreover, patients in low-risk subgroups had more Immunophenoscore (IPS) and fewer immune escapes compared to high-risk subgroups. Finally, an immunotherapeutic cohort confirmed the value of the CRlncSig in predicting immunotherapy outcomes. Conclusions: The developed CRlncSig may be promising for the clinical prediction of OC patient outcomes and immunotherapeutic responses.

Identification of a novel m6A-related lncRNAs signature and immunotherapeutic drug sensitivity in pancreatic adenocarcinoma

BACKGROUND

Pancreatic adenocarcinoma (PDAC) ranks as the fourth leading cause for cancer-related deaths worldwide. N6-methyladenosine (m6A) and long non-coding RNAs (lncRNAs) are closely related with poor prognosis and immunotherapeutic effect in PDAC. The aim of this study is to construct and validate a m6A-related lncRNAs signature and assess immunotherapeutic drug sensitivity in PDAC.

METHODS

RNA-seq data for 178 cases of PDAC patients and 167 cases of normal pancreatic tissue were obtained from TCGA and GTEx databases, respectively. A set of 21 m6A-related genes were downloaded based on the previous report. Co-expression network was conducted to identify m6A-related lncRNAs in PDAC. Cox analyses and least absolute shrinkage and selection operator (Lasso) regression model were used to construct a risk prognosis model. The relationship between signature genes and immune function was explored by single-sample GSEA (ssGSEA). The tumor immune dysfunction and exclusion (TIDE) score and tumor mutation burden (TMB) were utilized to evaluate the response to immunotherapy. Furthermore, the expression levels of 4 m6A-related lncRNAs on PDAC cell lines were measured by the quantitative real-time PCR (qPCR). The drug sensitivity between the high- and low-risk groups was validated using PDAC cell lines by Cell-Counting Kit 8 (CCK8).

RESULTS

The risk prognosis model was successfully constructed based on 4 m6A-related lncRNAs, and PDAC patients were divided into the high- and low-risk groups. The overall survival (OS) of the high-risk groups was more unfavorable compared with the low-risk groups. Receiver operating characteristic (ROC) curves demonstrated that the risk prognosis model reasonably predicted the 2-, 3- and 5-year OS of PDAC patients. qPCR analysis confirmed the decreased expression levels of 4 m6A-related lncRNAs in PDAC cells compared to the normal pancreatic cells. Furthermore, CCK8 assay revealed that Phenformin exhibited higher sensitivity in the high-risk groups, while Pyrimethamine exhibited higher sensitivity in the low-risk groups.

CONCLUSION

The prognosis of patients with PDAC were well predicted in the risk prognosis model based on m6A-related lncRNAs, and selected immunotherapy drugs have potential values for the treatment of pancreatic cancer.

Risk Assessment and Radiomics Analysis in Magnetic Resonance Imaging of Pancreatic Intraductal Papillary Mucinous Neoplasms (IPMN)

Intraductal papillary mucinous neoplasms (IPMNs) are a very common incidental finding during patient radiological assessment. These lesions may progress from low-grade dysplasia (LGD) to high-grade dysplasia (HGD) and even pancreatic cancer. The IPMN progression risk grows with time, so discontinuation of surveillance is not recommended. It is very important to identify imaging features that suggest LGD of IPMNs, and thus, distinguish lesions that only require careful surveillance from those that need surgical resection. It is important to know the management guidelines and especially the indications for surgery, to be able to point out in the report the findings that suggest malignant degeneration. The imaging tools employed for diagnosis and risk assessment are Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) with contrast medium. According to the latest European guidelines, MRI is the method of choice for the diagnosis and follow-up of patients with IPMN since this tool has a highest sensitivity in detecting mural nodules and intra-cystic septa. It plays a key role in the diagnosis of worrisome features and high-risk stigmata, which are associated with IPMNs malignant degeneration. Nowadays, the main limit of diagnostic tools is the ability to identify the precursor of pancreatic cancer. In this context, increasing attention is being given to artificial intelligence (AI) and radiomics analysis. However, these tools remain in an exploratory phase, considering the limitations of currently published studies. Key limits include noncompliance with AI best practices, radiomics workflow standardization, and clear reporting of study methodology, including segmentation and data balancing. In the radiological report it is useful to note the type of IPMN so as the morphological features, size, rate growth, wall, septa and mural nodules, on which the indications for surveillance and surgery are based. These features should be reported so as the surveillance time should be suggested according to guidelines.