Differences between Well-Differentiated Neuroendocrine Tumors and Ductal Adenocarcinomas of the Pancreas Assessed by Multi-Omics Profiling

Unraveling the potential of CD8, CD68, and VISTA as diagnostic and prognostic markers in patients with pancreatic ductal adenocarcinoma

Introduction

Pancreatic cancer is a truculent disease with limited treatment options and a grim prognosis. Immunotherapy has shown promise in treating various types of cancer, but its effectiveness in pancreatic cancer has been lacking. As a result, it is crucial to identify markers associated with immunological pathways in order to improve the treatment outcomes for this deadly cancer. The purpose of this study was to investigate the diagnostic and prognostic significance of three markers, CD8, CD68, and VISTA, in pancreatic ductal adenocarcinoma (PDAC), the most common subtype of pancreatic cancer.

Methods

We analyzed gene expression data from Gene Expression Omnibus (GEO) database using bioinformatics tools. We also utilized the STRING online tool and Funrich software to study the protein-protein interactions and transcription factors associated with CD8, CD68, and VISTA. In addition, tissue microarray (TMA) and immunohistochemistry (IHC) staining were performed on 228 samples of PDAC tissue and 10 samples of normal pancreatic tissue to assess the expression levels of the markers. We then correlated these expression levels with the clinicopathological characteristics of the patients and evaluated their survival rates.

Results

The analysis of the GEO data revealed slightly elevated levels of VISTA in PDAC samples compared to normal tissues. However, there was a significant increase in CD68 expression and a notable reduction in CD8A expression in pancreatic cancer. Further investigation identified potential protein-protein interactions and transcription factors associated with these markers. The IHC staining of PDAC tissue samples showed an increased expression of VISTA, CD68, and CD8A in pancreatic cancer tissues. Moreover, we found correlations between the expression levels of these markers and certain clinicopathological features of the patients. Additionally, the survival analysis revealed that high expression of CD8 was associated with better disease-specific survival and progression-free survival in PDAC patients.

Conclusion

These findings highlight the potential of CD8, CD68, and VISTA as diagnostic and prognostic indicators in PDAC.

GTM-decon: guided-topic modeling of single-cell transcriptomes enables sub-cell-type and disease-subtype deconvolution of bulk transcriptomes

Cell-type composition is an important indicator of health. We present Guided Topic Model for deconvolution (GTM-decon) to automatically infer cell-type-specific gene topic distributions from single-cell RNA-seq data for deconvolving bulk transcriptomes. GTM-decon performs competitively on deconvolving simulated and real bulk data compared with the state-of-the-art methods. Moreover, as demonstrated in deconvolving disease transcriptomes, GTM-decon can infer multiple cell-type-specific gene topic distributions per cell type, which captures sub-cell-type variations. GTM-decon can also use phenotype labels from single-cell or bulk data to infer phenotype-specific gene distributions. In a nested-guided design, GTM-decon identified cell-type-specific differentially expressed genes from bulk breast cancer transcriptomes.

Clustering analysis revealed the autophagy classification and potential autophagy regulators' sensitivity of pancreatic cancer based on multi-omics data

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy and is unresponsive to conventional therapeutic modalities due to its high heterogeneity, expounding the necessity, and priority of searching for effective biomarkers and drugs. Autophagy, as an evolutionarily conserved biological process, is upregulated in PDAC and its regulation is linked to a poor prognosis. Increased autophagy sequestered MHC-I on PDAC cells and weaken the antigen presentation and antitumor immune response, indicating the potential therapeutic strategies of autophagy inhibitors.

METHODS

By performing 10 state-of-the-art multi-omics clustering algorithms, we constructed a robust PDAC classification model to reveal the autophagy-related genes among different subgroups.

OUTCOMES

After building a more comprehensive regulating network for potential autophagy regulators exploration, we concluded the top 20 autophagy-related hub genes (GAPDH, MAPK3, RHEB, SQSTM1, EIF2S1, RAB5A, CTSD, MAP1LC3B, RAB7A, RAB11A, FADD, CFKN2A, HSP90AB1, VEGFA, RELA, DDIT3, HSPA5, BCL2L1, BAG3, and ERBB2), six miRNAs, five transcription factors, and five immune infiltrated cells as biomarkers. The drug sensitivity database was screened based on the biomarkers to predict possible drug-targeting signal pathways, hoping to yield novel insights, and promote the progress of the anticancer therapeutic strategy.

CONCLUSION

We succefully constructed an autophagy-related mRNA/miRNA/TF/Immune cells network based on a 10 state-of art algorithm multi-omics analysis, and screened the drug sensitivity dataset for detecting potential signal pathway which might be possible autophagy modulators' targets.

Prognostic significance of AP-2α/γ targets as cancer therapeutics

Identifying genes with prognostic importance could improve cancer treatment. An increasing number of reports suggest the existence of successful strategies based on seemingly "untargetable" transcription factors. In addition to embryogenesis, AP-2 transcription factors are known to play crucial roles in cancer development. Members of this family can be used as prognostic factors in oncological patients, and AP-2α/γ transcription factors were previously investigated in our pan-cancer comparative study using their target genes. The present study investigates tumors that were previously found similar with an emphasis on the possible role of AP-2 factors in specific cancer types. The RData workspace was loaded back to R environment and 3D trajectories were built via Monocle3. The genes that met the requirement of specificity were listed using top_markers(), separately for mutual and unique targets. Furthermore, the candidate genes had to meet the following requirements: correlation with AP-2 factor (through Correlation AnalyzeR) and validated prognostic importance (using GEPIA2 and subsequently KM-plotter or LOGpc). Eventually, the ROC analysis was applied to confirm their predictive value; co-dependence of expression was visualized via BoxPlotR. Some similar tumors were differentiated by AP-2α/γ targets with prognostic value. Requirements were met by only fifteen genes (EMX2, COL7A1, GRIA1, KRT1, KRT14, SLC12A5, SEZ6L, PTPRN, SCG5, DPP6, NTSR1, ARX, COL4A3, PPEF1 and TMEM59L); of these, the last four were excluded based on ROC curves. All the above genes were confronted with the literature, with an emphasis on the possible role played by AP-2 factors in specific cancers. Following ROC analysis, the genes were verified using immunohistochemistry data and progression-related signatures. Staining differences were observed, as well as co-dependence on the expression of e.g. CTNNB1, ERBB2, KRAS, SMAD4, EGFR or MKI67. In conclusion, prognostic value of targets suggested AP-2α/γ as candidates for novel cancer treatment. It was also revealed that AP-2 targets are related to tumor progression and that some mutual target genes could be inversely regulated.

Identification of Novel Lung Cancer Driver Genes Connecting Different Omics Levels With a Heat Diffusion Algorithm

Cancer driver gene is a type of gene with abnormal alterations that initiate or promote tumorigenesis. Driver genes can be used to reveal the fundamental pathological mechanisms of tumorigenesis. These genes may have pathological changes at different omics levels. Thus, identifying cancer driver genes involving two or more omics levels is essential. In this study, a computational investigation was conducted on lung cancer driver genes. Four omics levels, namely, epigenomics, genomics, transcriptomics, and post-transcriptomics, were involved. From the driver genes at each level, the Laplacian heat diffusion algorithm was executed on a protein–protein interaction network for discovering latent driver genes at this level. A following screen procedure was performed to extract essential driver genes, which contained three tests: permutation, association, and function tests, which can exclude false-positive genes and screen essential ones. Finally, the intersection operation was performed to obtain novel driver genes involving two omic levels. The analyses on obtained genes indicated that they were associated with fundamental pathological mechanisms of lung cancer at two corresponding omics levels.

Cdk5 drives formation of heterogeneous pancreatic neuroendocrine tumors

Pancreatic neuroendocrine tumors (PanNETs) are a heterogeneous population of neoplasms that arise from hormone-secreting islet cells of the pancreas and have increased markedly in incidence over the past four decades. Non-functional PanNETs, which occur more frequently than hormone-secreting tumors, are often not diagnosed until later stages of tumor development and have poorer prognoses. Development of successful therapeutics for PanNETs has been slow, partially due to a lack of diverse animal models for pre-clinical testing. Here, we report development of an inducible, conditional mouse model of PanNETs by using a bi-transgenic system for regulated expression of the aberrant activator of Cdk5, p25, specifically in β-islet cells. This model produces a heterogeneous population of PanNETs that includes a subgroup of well-differentiated, non-functional tumors. Production of these tumors demonstrates the causative potential of aberrantly active Cdk5 for generation of PanNETs. Further, we show that human PanNETs express Cdk5 pathway components, are dependent on Cdk5 for growth, and share genetic and transcriptional overlap with the INS-p25OE model. The utility of this model is enhanced by the ability to form tumor-derived allografts. This new model of PanNETs will facilitate molecular delineation of Cdk5-dependent PanNETs and the development of new targeted therapeutics.

MiRNA Expression in Neuroendocrine Neoplasms of Frequent Localizations

Neuroendocrine neoplasms (NEN) are infrequent malignant tumors of a neuroendocrine nature that arise in various organs. They occur most frequently in the lungs, intestines, stomach and pancreas. Molecular diagnostics and prognosis of NEN development are highly relevant. The role of clinical biomarkers can be played by microRNAs (miRNAs). This work is devoted to the analysis of data on miRNA expression in NENs. For the first time, a search for specificity or a community of their functional characteristics in different types of NEN was carried out. Their properties as biomarkers were also analyzed. To date, more than 100 miRNAs have been characterized as differentially expressed and significant for the development of NEN tumors. Only about 10% of the studied miRNAs are expressed in several types of NEN; differential expression of the remaining 90% was found only in tumors of specific localizations. A significant number of miRNAs have been identified as potential biomarkers. However, only a few miRNAs have values that characterized their quality as markers. The analysis demonstrates the predominant specific expression of miRNA in each studied type of NEN. This indicates that miRNA’s functional features are predominantly influenced by the tissue in which they are formed.