Integrated Analysis of Microarray and RNA-Seq Data for the Identification of Hub Genes and Networks Involved in the Pancreatic Cancer

Integrative multi-transcriptomic analysis uncovers core genes and potential defense mechanisms in rice-Magnoporthe oryzae interaction

KEY MESSAGE

Multiple transcriptomic comprehensive analyses highlight key genes and cast new light on multifaceted pathways that may be important arenas in rice innate immunity against Magnoporthe oryzae blast disease. Magnaporthe oryzae (MOR) poses a significant threat to rice production worldwide. However, defense mechanisms in rice against MOR remain inadequately defined. In this study, a multi-transcriptomic integrative analysis on 441 samples from diverse microarrays and RNA-seq sets was conducted to reveal critical factors in rice defense against MOR infection. A robust pattern of 3534 upregulated genes and 2920 repressed genes was commonly identified across all MOR-infected arrays and RNA-seq profiles. Interestingly, enrichment analysis revealed a consistent triggering of endoplasmic reticulum (ER)-related mechanisms and citric acid cycle (TCA) influx in rice response to MOR infection across all the transcriptome profiles, suggesting their critical role in modulating rice immunity against the pathogen. By contrast, chloroplast and photosynthesis pathways were frequently repressed across all the profiles. Among ER-related mechanisms, the phagosome pathway involved in the activation of NADPH oxidase was highly triggered in early response to MOR infection. Moreover, WGCNA analysis highlighted four key co-expressed gene modules and 80 significant hub genes associated with MOR infection. Among the core genes, Sec61 gene involved in the ER-translocation process was identified along with OsMFP (peroxisomal oxidation gene) and OSAHH gene (involved in cyclic-trans-methylation). Furthermore, MPK6, WRKY24, NUP35, and NPR1 genes were observed as core co-expressed genes, suggesting their significance in regulating rice immunity against MOR. Our findings elucidate key genes and multifaceted mechanisms in rice-MOR interaction, proposing new informative clues that can be exploited to improve rice resistance against blast disease.

Weighted gene co-expression network analysis reveals key stromal prognostic markers in pancreatic cancer

In recent years, it has been shown that stroma compartment can favor tumor proliferation and aggressiveness. Although extensive research with network analyses such as Weighted Gene Co-expression Network Analysis (WGCNA) has been conducted on pancreatic cancer and its stromal components, WGCNA has not previously been applied to isolate and identify genes associated with the abundance of stroma and survival outcome from bulk RNA data. We investigated the gene expression profile and clinical information of 140 pancreatic ductal adenocarcinoma patients from TCGA. Network analysis was performed using WGCNA and four modules were found to be associated to patients' clinical traits. Specifically, one module of 2459 genes, was associated to stromal sample content. Subsequently, those genes were further analyzed for survival association through log-rank test and Cox regression. HPGDS and ITGA9-AS1 emerged as significant indicators of favorable prognosis while KCMF1 and YARS1 were implicated in poorer prognostic outcomes. Importantly, HPGDS was found to be stromal-specific in the TMA cohort of Human Protein Atlas. Single sample GSEA showed that the stromal module is enriched for stromal signature of Moffitt and Puleo. These findings suggest that we uncovered a stromal specific signature through WGCNA and found putative prognostic markers.

Deciphering early responsive signature genes in rice blast disease: an integrated temporal transcriptomic study

Rice blast disease, caused by Magnaporthe oryzae, reigns as the top-most cereal killer, jeopardizing global food security. This necessitates the timely scouting of pathogen stress-responsive genes during the early infection stages. Thus, we integrated time-series microarray (GSE95394) and RNA-Seq (GSE131641) datasets to decipher rice transcriptome responses at 12- and 24-h post-infection (Hpi). Our analysis revealed 1580 differentially expressed genes (DEGs) overlapped between datasets. We constructed a protein-protein interaction (PPI) network for these DEGs and identified significant subnetworks using the MCODE plugin. Further analysis with CytoHubba highlighted eight plausible hub genes for pathogenesis: RPL8 (upregulated) and RPL27, OsPRPL3, RPL21, RPL9, RPS5, OsRPS9, and RPL17 (downregulated). We validated the expression levels of these hub genes in response to infection, finding that RPL8 exhibited significantly higher expression compared with other downregulated genes. Remarkably, RPL8 formed a distinct cluster in the co-expression network, whereas other hub genes were interconnected, with RPL9 playing a central role, indicating its pivotal role in coordinating gene expression during infection. Gene Ontology highlighted the enrichment of hub genes in the ribosome and protein translation processes. Prior studies suggested that plant immune defence activation diminishes the energy pool by suppressing ribosomes. Intriguingly, our study aligns with this phenomenon, as the identified ribosomal proteins (RPs) were suppressed, while RPL8 expression was activated. We anticipate that these RPs could be targeted to develop new stress-resistant rice varieties, beyond their housekeeping role. Overall, integrating transcriptomic data revealed more common DEGs, enhancing the reliability of our analysis and providing deeper insights into rice blast disease mechanisms.

Network Analysis of Brain and Bone Tissue Transcripts Reveals Shared Molecular Mechanisms Underlying Alzheimer's Disease and Related Dementias and Osteoporosis

BACKGROUND

Alzheimer's disease and related dementias (ADRD) and osteoporosis (OP) are 2 prevalent diseases of aging with demonstrated epidemiological association, but the underlying molecular mechanisms contributing to this association are unknown.

METHODS

We used network analysis of bone and brain transcriptomes to discover common molecular mechanisms underlying these 2 diseases. Our study included RNA-sequencing data from the dorsolateral prefrontal cortex tissue of autopsied brains in 629 participants from ROSMAP (Religious Orders Study and the Rush Memory and Aging Project), with a subgroup of 298 meeting criteria for inclusion in 5 ADRD categories, and RNA array data from transiliac bone biopsies in 84 participants from the Oslo study of postmenopausal women. After developing each network within each tissue, we analyzed associations between modules (groups of coexpressed genes) with multiple bone and neurological traits, examined overlap in modules between networks, and performed pathway enrichment analysis to discover conserved mechanisms.

RESULTS

We discovered 3 modules in ROSMAP that showed significant associations with ADRD and bone-related traits and 4 modules in Oslo that showed significant associations with multiple bone outcomes. We found significant module overlap between the 2 networks in modules linked to signaling, tissue homeostasis, and development, and Wingless-related integration site (Wnt) signaling was found to be highly enriched in OP and ADRD modules of interest.

CONCLUSIONS

These results provide translational opportunities in the development of treatments and biomarkers for ADRD and OP.

Insights into the Gene Expression Profile of Classical Hodgkin Lymphoma: A Study towards Discovery of Novel Therapeutic Targets

Classical Hodgkin lymphoma (cHL) is a common B-cell cancer and a significant health concern, especially in Western and Asian countries. Despite the effectiveness of chemotherapy, many relapse cases are being reported, highlighting the need for improved treatments. This study aimed to address this issue by discovering biomarkers through the analysis of gene expression data specific to cHL. Additionally, potential anticancer inhibitors were explored to target the discovered biomarkers. This study proceeded by retrieving microarray gene expression data from cHL patients, which was then analyzed to identify significant differentially expressed genes (DEGs). Functional and network annotation of the upregulated genes revealed the active involvement of matrix metallopeptidase 12 (MMP12) and C-C motif metallopeptidase ligand 22 (CCL22) genes in the progression of cHL. Additionally, the mentioned genes were found to be actively involved in cancer-related pathways, i.e., oxidative phosphorylation, complement pathway, myc_targets_v1 pathway, TNFA signaling via NFKB, etc., and showed strong associations with other genes known to promote cancer progression. MMP12, topping the list with a logFC value of +6.6378, was selected for inhibition using docking and simulation strategies. The known anticancer compounds were docked into the active site of the MMP12 molecular structure, revealing significant binding scores of -7.7 kcal/mol and -7.6 kcal/mol for BDC_24037121 and BDC_27854277, respectively. Simulation studies of the docked complexes further supported the effective binding of the ligands, yielding MMGBSA and MMPBSA scores of -78.08 kcal/mol and -82.05 kcal/mol for MMP12-BDC_24037121 and -48.79 kcal/mol and -49.67 kcal/mol for MMP12-BDC_27854277, respectively. Our findings highlight the active role of MMP12 in the progression of cHL, with known compounds effectively inhibiting its function and potentially halting the advancement of cHL. Further exploration of downregulated genes is warranted, as associated genes may play a role in cHL. Additionally, CCL22 should be considered for further investigation due to its significant role in the progression of cHL.

Transcriptomics Demonstrates Significant Biological Effect of Growing Stem Cells on RGD-Cotton Scaffold

Stem cell therapy provides a viable alternative treatment for degenerated or damaged tissue. Stem cells have been used either alone or in conjunction with an artificial scaffold. The latter provides a structural advantage by enabling the cells to thrive in three-dimensional (3D) settings, closely resembling the natural in vivo environments. Previously, we disclosed the development of a 3D scaffold made from cotton, which was conjugated with arginyl-glycyl-aspartic acid (RGD), to facilitate the growth and proliferation of mesenchymal stem cells (MSCs). This scaffold allowed the MSCs to adhere and proliferate without compromising their viability or their stem cell markers. A comprehensive analysis investigation of the molecular changes occurring in MSCs adhering to the cotton fibers will contribute to the advancement of therapy. The objective of this study is to analyze the molecular processes occurring in the growth of MSCs on a cotton-RGD conjugated-based scaffold by examining their gene expression profiles. To achieve this, we conducted an experiment where MSCs were seeded with and without the scaffold for a duration of 48 h. Subsequently, cells were collected for RNA extraction, cDNA synthesis, and whole-transcriptomic analysis performed on both populations. Our analysis revealed several upregulated and downregulated differently expressed genes in the MSCs adhering to the scaffold compared with the control cells. Through gene ontology analysis, we were able to identify enriched biological processes, molecular functions, pathways, and protein-protein interactions in these differentially expressed genes. Our data suggest that the scaffold may have the potential to enhance osteogenesis in the MSCs. Furthermore, our results indicate that the scaffold does not induce oxidative stress, inflammation, or aging in the MSCs. These findings provide valuable insights for the application of MSCs in tissue engineering and regenerative medicine.

A novel feature selection algorithm for identifying hub genes in lung cancer

Lung cancer, a life-threatening disease primarily affecting lung tissue, remains a significant contributor to mortality in both developed and developing nations. Accurate biomarker identification is imperative for effective cancer diagnosis and therapeutic strategies. This study introduces the Voting-Based Enhanced Binary Ebola Optimization Search Algorithm (VBEOSA), an innovative ensemble-based approach combining binary optimization and the Ebola optimization search algorithm. VBEOSA harnesses the collective power of the state-of-the-art classification models through soft voting. Moreover, our research applies VBEOSA to an extensive lung cancer gene expression dataset obtained from TCGA, following essential preprocessing steps including outlier detection and removal, data normalization, and filtration. VBEOSA aids in feature selection, leading to the discovery of key hub genes closely associated with lung cancer, validated through comprehensive protein-protein interaction analysis. Notably, our investigation reveals ten significant hub genes-ADRB2, ACTB, ARRB2, GNGT2, ADRB1, ACTG1, ACACA, ATP5A1, ADCY9, and ADRA1B-each demonstrating substantial involvement in the domain of lung cancer. Furthermore, our pathway analysis sheds light on the prominence of strategic pathways such as salivary secretion and the calcium signaling pathway, providing invaluable insights into the intricate molecular mechanisms underpinning lung cancer. We also utilize the weighted gene co-expression network analysis (WGCNA) method to identify gene modules exhibiting strong correlations with clinical attributes associated with lung cancer. Our findings underscore the efficacy of VBEOSA in feature selection and offer profound insights into the multifaceted molecular landscape of lung cancer. Finally, we are confident that this research has the potential to improve diagnostic capabilities and further enrich our understanding of the disease, thus setting the stage for future advancements in the clinical management of lung cancer. The VBEOSA source codes is publicly available at https://github.com/TEHNAN/VBEOSA-A-Novel-Feature-Selection-Algorithm-for-Identifying-hub-Genes-in-Lung-Cancer .

Isoform switching leads to downregulation of cytokine producing genes in estrogen receptor positive breast cancer

Objective: Estrogen receptor breast cancer (BC) is characterized by the expression of estrogen receptors. It is the most common cancer among women, with an incidence rate of 2.26 million cases worldwide. The aim of this study was to identify differentially expressed genes and isoform switching between estrogen receptor positive and triple negative BC samples. Methods: The data were collected from ArrayExpress, followed by preprocessing and subsequent mapping from HISAT2. Read quantification was performed by StringTie, and then R package ballgown was used to perform differential expression analysis. Functional enrichment analysis was conducted using Enrichr, and then immune genes were shortlisted based on the ScType marker database. Isoform switch analysis was also performed using the IsoformSwitchAnalyzeR package. Results: A total of 9,771 differentially expressed genes were identified, of which 86 were upregulated and 117 were downregulated. Six genes were identified as mainly associated with estrogen receptor positive BC, while a novel set of ten genes were found which have not previously been reported in estrogen receptor positive BC. Furthermore, alternative splicing and subsequent isoform usage in the immune system related genes were determined. Conclusion: This study identified the differential usage of isoforms in the immune system related genes in cancer cells that suggest immunosuppression due to the dysregulation of CXCR chemokine receptor binding, iron ion binding, and cytokine activity.

In vitro anti-carcinogenic effect of andarine as a selective androgen receptor modulator on MIA-PaCa-2 cells by decreased proliferation and cell-cycle arrest at G0/G1 phase

Pancreatic cancer (PC) continues to be devastating due to its highly malignant nature and poor prognosis. The limited benefits of the chemotherapeutic drugs and increasing resistance pose a critical challenge to overcome and warrant investigations for new therapeutic agents. Several preclinical and clinical studies have suggested a possible role of the androgen receptor (AR) signaling pathway in PC development and progression. Nevertheless, the studies are limited and inconclusive in explaining the molecular link between AR signaling and PC. Selective androgen receptor modulators (SARMs) are small molecule drugs with high affinity for the androgen receptor. SARMs elicit selective anabolic activities while abrogating undesired androgenic side effects. There is no study focusing on the utility of SARMs as inhibitors of PC. Here, we report the first study evaluating the possible anti-carcinogenic influences of andarine, a member of the SARMs, on PC. The data we presented here has illustrated that andarine repressed PC cell growth and proliferation via cell cycle arrest at G₀/G₁ phase. Gene expression analysis revealed that it downregulates CDKN1A expression level accordingly. Furthermore, we established that the anti-carcinogenic activity of andarine is not mediated by the PI3K/AKT/mTOR signaling pathway, a crucial regulator of cell survival. Our findings suggest that andarine might be considered as a prospective drug for PC.

A self-training subspace clustering algorithm based on adaptive confidence for gene expression data

Gene clustering is one of the important techniques to identify co-expressed gene groups from gene expression data, which provides a powerful tool for investigating functional relationships of genes in biological process. Self-training is a kind of important semi-supervised learning method and has exhibited good performance on gene clustering problem. However, the self-training process inevitably suffers from mislabeling, the accumulation of which will lead to the degradation of semi-supervised learning performance of gene expression data. To solve the problem, this paper proposes a self-training subspace clustering algorithm based on adaptive confidence for gene expression data (SSCAC), which combines the low-rank representation of gene expression data and adaptive adjustment of label confidence to better guide the partition of unlabeled data. The superiority of the proposed SSCAC algorithm is mainly reflected in the following aspects. 1) In order to improve the discriminative property of gene expression data, the low-rank representation with distance penalty is used to mine the potential subspace structure of data. 2) Considering the problem of mislabeling in self-training, a semi-supervised clustering objective function with label confidence is proposed, and a self-training subspace clustering framework is constructed on this basis. 3) In order to mitigate the negative impact of mislabeled data, an adaptive adjustment strategy based on gravitational search algorithm is proposed for label confidence. Compared with a variety of state-of-the-art unsupervised and semi-supervised learning algorithms, the SSCAC algorithm has demonstrated its superiority through extensive experiments on two benchmark gene expression datasets.

Integrative, In Silico and Comparative Analysis of Breast Cancer Secretome Highlights Invasive-Ductal-Carcinoma-Grade Progression Biomarkers

Globally, BC is the most frequently diagnosed cancer in women. The aim of this study was to identify novel secreted biomarkers that may indicate progression to high-grade BC malignancies and therefore predict metastatic potential. A total of 33 studies of breast cancer and 78 of other malignancies were screened via a systematic review for eligibility, yielding 26 datasets, 8 breast cancer secretome datasets, and 18 of other cancers that were included in the comparative secretome analysis. Sequential bioinformatic analysis using online resources enabled the identification of enriched GO_terms, overlapping clusters, and pathway reconstruction. This study identified putative predictors of IDC grade progression and their association with breast cancer patient mortality outcomes, namely, HSPG2, ACTG1, and LAMA5 as biomarkers of in silico pathway prediction, offering a putative approach by which the abovementioned proteins may mediate their effects, enabling disease progression. This study also identified ITGB1, FBN1, and THBS1 as putative pan-cancer detection biomarkers. The present study highlights novel, putative secretome biomarkers that may provide insight into the tumor biology and could inform clinical decision making in the context of IDC management in a non-invasive manner.

An Extensive Review on Preclinical and Clinical Trials of Oncolytic Viruses Therapy for Pancreatic Cancer

Chemotherapy resistance and peculiar tumor microenvironment, which diminish or mitigate the effects of therapies, make pancreatic cancer one of the deadliest malignancies to manage and treat. Advanced immunotherapies are under consideration intending to ameliorate the overall patient survival rate in pancreatic cancer. Oncolytic viruses therapy is a new type of immunotherapy in which a virus after infecting and lysis the cancer cell induces/activates patients’ immune response by releasing tumor antigen in the blood. The current review covers the pathways and molecular ablation that take place in pancreatic cancer cells. It also unfolds the extensive preclinical and clinical trial studies of oncolytic viruses performed and/or undergoing to design an efficacious therapy against pancreatic cancer.

HGF/c-Met pathway facilitates the perineural invasion of pancreatic cancer by activating the mTOR/NGF axis

Perineural invasion (PNI) is a pathologic feature of pancreatic cancer and is associated with poor outcomes, metastasis, and recurrence in pancreatic cancer patients. However, the molecular mechanism of PNI remains unclear. The present study aimed to investigate the mechanism that HGF/c-Met pathway facilitates the PNI of pancreatic cancer. In this study, we confirmed that c-Met expression was correlated with PNI in pancreatic cancer tissues. Activating the HGF/c-Met signaling pathway potentiated the expression of nerve growth factor (NGF) to recruit nerves and promote the PNI. Activating the HGF/c-Met signaling pathway also enhanced the migration and invasion ability of cancer cells to facilitate cancer cells invading nerves. Mechanistically, HGF/c-Met signaling pathway can active the mTOR/NGF axis to promote the PNI of pancreatic cancer. Additionally, we found that knocking down c-Met expression inhibited cancer cell migration along the nerve, reduced the damage of the sciatic nerve caused by cancer cells and protected the function of the sciatic nerve in vivo. Taken together, our findings suggest a supportive mechanism of the HGF/c-Met signaling pathway in promoting PNI by activating the mTOR/NGF axis in pancreatic cancer. Blocking the HGF/c-Met signaling pathway may be an effective target for the treatment of PNI.

Interaction Analysis of Adenovirus L5 Protein With Pancreatic Cancer Cell Surface Receptor to Analyze Its Affinity for Oncolytic Virus Therapy

This study seeks to investigate the interaction profile of the L5 protein of oncolytic adenovirus with the overexpressed surface receptors of pancreatic cancer. This is an important area of research because pancreatic cancer is one of the most fatal malignancies with a very low patient survival rate. Multiple therapies to date to improve the survival rate are reported; however, they show a comparatively low success rate. Among them, oncolytic virus therapy is a type of immunotherapy that is currently under deliberation by researchers for multiple cancer types in various clinical trials. Talimogene laherparepvec (T-VEC) is the first oncolytic virus approved by the US Food and Drug Administration (FDA) for melanoma. The oncolytic virus not only kills cancer cells but also activates the anticancer immune response. Therefore, it is preferred over others to deal with aggressive pancreatic cancer. The efficacy of therapy primarily depends on how effectively the oncolytic virus enters and infects the cancer cell. Cell surface receptors and their interactions with virus coat proteins are a crucial step for oncolytic virus entry and a pivotal determinant. The L5 proteins of the virus coat are the first to interact with host cell surface receptors. Therefore, the objective of this study is to analyze the interaction profile of the L5 protein of oncolytic adenovirus with overexpressed surface receptors of pancreatic cancer. The L5 proteins of three adenovirus serotypes HAdV2, HAdV5, and HAdV3 were utilized in this study. Overexpressed pancreatic cancer receptors include SLC2A1, MET, IL1RAP, NPR3, GABRP, SLC6A6, and TMPRSS4. The protein structures of viral and cancer cell protein were docked using the High Ambiguity Driven protein–protein DOCKing (HADDOCK) server. The binding affinity and interaction profile of viral proteins against all the receptors were analyzed. Results suggest that the HAdV3 L5 protein shows better interaction as compared to HAdV2 and HAdV5 by elucidating high binding affinity with 4 receptors (NPR3, GABRP, SLC6A6, and TMPRSS4). The current study proposed that HAdV5 or HAdV2 virus pseudotyped with the L5 protein of HAdV3 can be able to effectively infect pancreatic cancer cells. Moreover, the current study surmises that the affinity maturation of HAdV3 L5 can enhance virus attachment with all the receptors of cancer cells.

Transplantation of umbilical cord-derived mesenchymal stem cells promotes the recovery of thin endometrium in rats

The endometrium plays a critical role in embryo implantation and pregnancy, and a thin uterus is recognized as a key factor in embryo implantation failure. Umbilical cord mesenchymal stem cells (UC-MSCs) have attracted interest for the repair of intrauterine adhesions. The current study investigated the repair of thin endometrium in rats using the UC-MSCs and the mechanisms involved. Rats were injected with 95% ethanol to establish a model of thin endometrium. The rats were randomly divided into normal, sham, model, and UC-MSCs groups. Endometrial morphological alterations were observed by hematoxylin-eosin staining and Masson staining, and functional restoration was assessed by testing embryo implantation. The interaction between UC-MSCs and rat endometrial stromal cells (ESCs) was evaluated using a transwell 3D model and immunocytochemistry. Microarray mRNA and miRNA platforms were used for miRNA-mRNA expression profiling. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) analyses were performed to identify the biological processes, molecular functions, cellular components, and pathways of endometrial injury and UC-MSCs transplantation repair and real-time quantitative reverse transcription PCR (qRT-PCR) was performed to further identify the expression changes of key molecules in the pathways. Endometrium thickness, number of glands, and the embryo implantation numbers were improved, and the degree of fibrosis was significantly alleviated by UC-MSCs treatment in the rat model of thin endometrium. In vitro cell experiments showed that UC-MSCs migrated to injured ESCs and enhanced their proliferation. miRNA microarray chip results showed that expression of 45 miRNAs was downregulated in the injured endometrium and upregulated after UC-MSCs transplantation. Likewise, expression of 39 miRNAs was upregulated in the injured endometrium and downregulated after UC-MSCs transplantation. The miRNA-mRNA interactions showed the changes in the miRNA and mRNA network during the processes of endometrial injury and repair. GO and KEGG analyses showed that the process of endometrial injury was mainly attributed to the decomposition of the extracellular matrix (ECM), protein degradation and absorption, and accompanying inflammation. The process of UC-MSCs transplantation and repair were accompanied by the reconstruction of the ECM, regulation of chemokines and inflammation, and cell proliferation and apoptosis. The key molecules involved in ECM-receptor interaction pathways were further verified by qRT-PCR. Itga1 and Thbs expression decreased in the model group and increased by UC-MSCs transplantation, while Laminin and Collagen expression increased in both the model group and MSCs group, with greater expression observed in the latter. This study showed that UC-MSCs transplantation could promote recovery of thin endometrial morphology and function. Furthermore, it revealed the expression changes of miRNA and mRNA after endometrial injury and UC-MSCs transplantation repair processed, and signaling pathways that may be involved in endometrial injury and repair.

Identification of potential core genes at single-cell level contributing to pathogenesis of pancreatic ductal adenocarcinoma through bioinformatics analysis

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) prognosis has not improved over the last decades because of the lack of effective diagnostic and therapeutic methods in the early stage of the disease.

METHODS

Several gene expression profiles were downloaded from the Expression Omnibus (GEO) database. We calculated the differentially expressed mRNAs (DEGs) and miRNAs (DEmiRs). Then, we constructed a miRNA-mRNA regulatory network by using the miRWalk database. For the DEGs regulated by DEmiRs, we introduced GEPIA to confirm these DEGs' expression and effect on overall survival. We used other GEO datasets and mRNA-miRNA target databases to validate these DEGs and their relationship with DEmiRs. All these potential core DEGs regulated by DEmiRs were also analyzed at the single-cell level to confirm their cell type source.

RESULTS

CCNB2 and KCNN4, which were regulated by several micro RNAs, showed relatively high expression levels in PDAC patients and significant association with worse overall survival. Furthermore, we identified many DEGs at single-cell level and found that 10 oncogenes were significantly upregulated in type 2 ductal cell type, thereby further demonstrating that type 2 ductal cells might be major sources of malignant cells and are valuable therapeutic targets in PDAC.

CONCLUSIONS

Our data added some new insights into the molecular mechanism of PDAC and may be helpful for finding potential biomarkers for diagnosis. These discovery at single-cell level may also be useful for developing new therapeutic targets for PDAC patients.