Identification of lung adenocarcinoma biomarkers based on bioinformatic analysis and human samples

AURKA Activates FOXO3a to Form a Positive Feedback Loop in the Proliferation and Migration of Keloid Fibroblasts

Objective: Keloids are benign fibroproliferative disorders with invasive growth exceeding the wound boundary. Aurora kinase A (AURKA) is a serine/threonine kinase highly expressed in various tumors, facilitating tumor growth and invasion. Currently, the role of AURKA in keloid remains unclear. Approach: Fibroblasts were isolated from keloid and normal skin samples. AURKA was evaluated by qPCR, Western blot, and immunohistochemistry. Transcriptome sequencing and dual-luciferase reporter assays were applied to figure out targets of AURKA. Following expression alteration and MLN8237 (an AURKA kinase inhibitor, AKI) treatment, phenotypical experiments were conducted to clarify biological functions of AURKA along with its target, and to probe into the clinical potential of AURKA inhibition. Results: AURKA was upregulated in keloid tissues and fibroblasts. Forkhead box O 3a (FOXO3a) was verified as a downstream of AURKA. Further experiments demonstrated that AURKA transactivated FOXO3a by binding to FOXO3a, while FOXO3a directly transactivated AURKA. Functionally, AURKA and FOXO3a cooperated in enhancing the proliferation and migration of keloid fibroblasts via protein kinase B (AKT) phosphorylation. Although MLN8237 weakened the proliferation and migration in keloid fibroblasts, the transactivation of AURKA on FOXO3a was independent of kinase activity. Innovation: This study reveals that AURKA and FOXO3a compose a transactivation loop in enhancing the proliferative and migrative properties of keloid fibroblasts, and proposes AURKA as a promising target. Conclusion: AURKA/FOXO3a loop promotes the proliferation and migration of keloid fibroblasts via AKT signaling. Despite the anti-keloid effects of AKIs, AURKA acts as a transcription factor independently of kinase activity, deepening our understanding on AKI insensitivity.

Nano-mediated strategy for targeting and treatment of non-small cell lung cancer (NSCLC)

Lung cancer is the most common type of cancer, with over 2.1 million cases diagnosed annually worldwide. It has a high incidence and mortality rate, leading to extensive research into various treatment options, including the use of nanomaterial-based carriers for drug delivery. With regard to cancer treatment, the distinct biological and physico-chemical features of nano-structures have acquired considerable impetus as drug delivery system (DDS) for delivering medication combinations or combining diagnostics and targeted therapy. This review focuses on the use of nanomedicine-based drug delivery systems in the treatment of lung cancer, including the use of lipid, polymer, and carbon-based nanomaterials for traditional therapies such as chemotherapy, radiotherapy, and phototherapy. The review also discusses the potential of stimuli-responsive nanomaterials for drug delivery in lung cancer, and the limitations and opportunities for improving the design of nano-based materials for the treatment of non-small cell lung cancer (NSCLC).

AURKA, TOP2A and MELK are the key genes identified by WGCNA for the pathogenesis of lung adenocarcinoma

The comprehensive analysis of single or multiple microarray datasets is currently available in Gene Expression Omnibus (GEO) databases, with several studies having identified genes strongly associated with the development of lung adenocarcinoma (LUAD). However, the mechanisms of LUAD development remain largely unknown and has not yet been systematically studied; thus, further studies are required in this field. In the present study, weighted gene co-expression network analysis (WGCNA) was used for the evaluation of key genes with potential high risk of LUAD, and to provide more reliable evidence concerning its pathogenesis. The GSE140797 dataset from the high-throughput GEO database was downloaded and was first analyzed using the Limma package in the R language in order to determine the differentially expressed genes. The dataset was then analyzed using the WGCNA package to analyze the co-expressed genes, and the modular genes with the highest correlation with the clinical phenotype were identified. Subsequently, the pathogenic genes shared in common between the result of the two analyses were imported into the STRING database for protein-protein interaction network analysis. The hub genes were screened out using Cytoscape, and then The Cancer Genome Atlas analysis, receiver operating characteristic analysis and survival analysis were subsequently performed. Finally, the key genes were evaluated using reverse transcription-quantitative PCR and western blot analysis. Bioinformatics analysis of the GSE140797 dataset revealed eight key genes: AURKA, BUB1, CCNB1, CDK1, MELK, NUSAP1, TOP2A and PBK. Finally, the AURKA, TOP2A and MELK genes were evaluated in samples from patients with lung cancer using WGCNA and RT-qPCR, western blot analysis experiments, providing basis for further research on the mechanisms of LUAD development and targeted therapy.

A novel cuproptosis-related prognostic lncRNA signature and lncRNA MIR31HG/miR-193a-3p/TNFRSF21 regulatory axis in lung adenocarcinoma

Lung adenocarcinoma (LUAD) remains the most common subtype of lung malignancy. Cuproptosis is a newly identified cell death which could regulate tumor cell proliferation and progression. Long non-coding RNAs (lncRNAs) are key molecules and potential biomarkers for diagnosing and treating various diseases. However, the effects of cuproptosis-related lncRNAs on LUAD are still unclear. In our study, 7 cuproptosis-related lncRNAs were selected to establish a prognostic model using univariate Cox regression analysis, LASSO algorithm, and multivariate analysis. Furthermore, we evaluated AC008764.2, AL022323.1, ELN-AS1, and LINC00578, which were identified as protective lncRNAs, while AL031667.3, AL606489.1, and MIR31HG were identified as risk lncRNAs. The risk score calculated by the prognostic model proved to be an effective independent factor compared with other clinical features by Cox regression analyses [univariate analysis: hazard ratio (HR) = 1.065, 95% confidence interval (CI) = 1.043–1.087, P < 0.001; multivariate analysis: HR = 1.067, 95% CI = 1.044–1.091, P < 0.001]. In addition, both analyses (ROC and nomogram) were used to corroborate the accuracy and reliability of this signature. The correlation between cuproptosis-related lncRNAs and immune microenvironment was elucidated, where 7 immune cells and 8 immune-correlated pathways were found to be differentially expressed between two risk groups. Furthermore, our results also identified and verified the ceRNA of cuproptosis-related lncRNA MIR31HG/miR-193a-3p/TNFRSF21 regulatory axis using bioinformatics tools. MIR31HG was highly expressed in LUAD specimens and some LUAD cell lines. Inhibition of MIR31HG clearly reduced the proliferation, migration, and invasion of the LUAD cells. MIR31HG showed oncogenic features via sponging miR-193a-3p and tended to positively regulate TNFRSF21 expression. In a word, lncRNA MIR31HG acts as an oncogene in LUAD by targeting miR-193a-3p to modulate TNFRSF21, which may be beneficial to the gene therapy of LUAD.

Induction of Ferroptosis by Ophiopogonin-B Through Regulating the Gene Signature AURKA in NSCLC

Ferroptosis is a new type of iron-dependent programmed cell death. In recent years, its role in the diagnosis and treatment of multiple tumors, including non-small cell lung cancer (NSCLC), has been continuously observed. The relationship between the ferroptosis-related genes and the prognosis of patients with NSCLC needs to be clarified. In this study, The Cancer Genome Atlas (TCGA) and the Gene Expression Synthesis database (Gene Expression Omnibus, GEO) were used to build a model of ferroptosis-related differentially expressed genes (DEGs). A total of 101 ferroptosis-related DEGs were screened using R language, and a 12-gene signature was finally established through univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO)-penalized Cox regression analysis. According to the risk scores, the patients were divided into a high-risk or a low-risk group, with patients in the low-risk group showing better prognosis. AURKA, one of the genes in the 12-gene signature, was found to be highly expressed in tumors. In addition, further study verified AURKA to be a negative regulator of ferroptosis in NSCLC cells. Ophiopogonin B (OP-B) had been reported to induce apoptosis, mitotic catastrophe, and autophagy in NSCLC cells. Herein, proteomic sequencing analysis and OP-B administration revealed the upregulation of AURKA and the downregulation of PHKG2 and SLC7A5 in the 12-gene signature, indicating that OP-B induced ferroptosis in NSCLC. Determination of the concentrations of malondialdehyde (MDA), glutathione (GSH), and intracellular iron and the mitochondrial membrane potential (MMP) confirmed the induction of ferroptosis by OP-B in vitro. Furthermore, transmission electron microscopy (TEM) examination of lung cancer xenotransplantation in nude mice confirmed that OP-B induced ferroptosis in vivo. Further study of the molecular mechanism showed that the ferroptosis effect caused by OP-B can be partially reversed by the overexpression of AURKA. Overall, our study established a new ferroptosis-related risk prediction model for the prognosis of patients with NSCLC, revealed the enrichment pathways of ferroptosis in NSCLC, and discovered the negative regulation of AURKA in ferroptosis. On this basis, we demonstrated that OP-B can induce ferroptosis in NSCLC and clarified the specific molecular mechanism of OP-B inducing ferroptosis by regulating the expression of AURKA.

Effect of PPP1R14D gene high expression in lung adenocarcinoma knocked out on proliferation and apoptosis of DMS53 cell

PURPOSE

Globally, lung cancer remains the most commonly diagnosed cancer and the leading cause of cancer-related mortality. Lung adenocarcinoma (LUAD) is a common subtype of lung cancer and carries a poor prognosis. Treatment outcomes biomarkers in LUAD are critical, and there is currently a paucity of data; therefore, there is a need for novel biomarkers and newer therapeutic targets.

METHODS

Bayesian analysis was used to obtain the whole-genome t value of LUAD. Gene set enrichment analysis (GSEA) was conducted to obtain the normalized enrichment scores (NES) of the whole genome, and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway was analyzed using the Gene Set Analysis Toolkit. Herein, we investigated the PPP1R14D expression level at the protein level in LUAD and the impact of PPP1R14D knockdown on the proliferation and apoptosis of LUAD cells in vitro.

RESULTS

A total of 483 LUAD samples and 59 normal control samples were included, and 904 differentially expressed genes (DEGs) and 504 LUAD-related genes reported in the literature were obtained. The DEGs showed that PPP1R14D was the most significantly up-regulated gene. Western blot of 30 cases of LUAD tissue and adjacent normal tissue also found that PPP1R14D was significantly highly expressed in cancer tissues. Lentivirus-mediated shRNA strategy effectively inhibited PPP1R14D expression in human LUAD cells DMS53, while PPP1R14D knockdown induced apoptosis and cell proliferation in DMS53 cells.

CONCLUSION

Abnormally up-regulated PPP1R14D promotes the survival and proliferation of tumor cells in human LUAD and may serve as a therapeutic and diagnostic target for LUAD.

EGFR Mutation Status and Subtypes Predicted by CT-Based 3D Radiomic Features in Lung Adenocarcinoma

Objective

In this study, we aim to establish a non-invasive tool to predict epidermal growth factor receptor (EGFR) mutation status and subtypes based on radiomic features of computed tomography (CT).

Methods

A total of 233 lung adenocarcinoma patients were investigated and randomly divided into the training and test cohorts. In this study, 2300 radiomic features were extracted from original and filtered (Exponential, Laplacian of Gaussian, Logarithm, Gabor, Wavelet) CT images. The radiomic features were divided into four categories, including histogram, volumetric, morphologic, and texture features. An RF-BFE algorithm was developed to select the features for building the prediction models. Clinicopathological features (including age, gender, smoking status, TNM staging, maximum diameter, location, and growth pattern) were combined to establish an integrated model with radiomic features. ROC curve and AUC quantified the effectiveness of the predictor of EGFR mutation status and subtypes.

Results

A set of 10 features were selected to predict EGFR mutation status between EGFR mutant and wild type, while 9 selected features were used to predict mutation subtypes between exon 19 deletion and exon 21 L858R mutation. To predict the EGFR mutation status, the AUC of the training cohort was 0.778 and the AUC of the test cohort was 0.765. To predict the EGFR mutation subtypes, the AUC of training cohort was 0.725 and the AUC of test cohort was 0.657. The integrated model showed the most optimal predictive performance with EGFR mutation status (AUC = 0.870 and 0.759) and subtypes (AUC = 0.797 and 0.554) in the training and test cohorts.

Conclusion

CT-based radiomic features can extract information on tumor heterogeneity in lung adenocarcinoma. In addition, we have established a radiomic model and an integrated model to non-invasively predict the EGFR mutation status and subtypes of lung adenocarcinoma, which is conducive to saving clinical costs and guiding targeted therapy.

Upregulation of glucosamine-phosphate N-acetyltransferase 1 is a promising diagnostic and predictive indicator for poor survival in patients with lung adenocarcinoma

Lung adenocarcinoma, a type of non-small cell lung cancer, is the leading cause of cancer death worldwide. Great efforts have been made to identify the underlying mechanism of adenocarcinoma, especially in relation to oncogenes. The present study by integrating computational analysis with western blotting, aimed to understand the role of the upregulation of glucosamine-phosphate N-acetyltransferase 1 (GNPNAT1) in carcinogenesis. In the present study, publicly available gene expression profiles and clinical data were downloaded from The Cancer Genome Atlas to determine the role of GNPNAT1 in lung adenocarcinoma (LUAD). In addition, the association between LUAD susceptibility and GNPNAT1 upregulation were analyzed using Wilcoxon signed-rank test and logistic regression analysis. In LUAD, GNPNAT1 upregulation was significantly associated with disease stage [odds ratio (OR)=2.92, stage III vs. stage I], vital status (dead vs. alive, OR=1.89), cancer status (tumor status vs. tumor-free status, OR=1.85) and N classification (yes vs. no, OR=1.75). Cox regression analysis and the Kaplan-Meier method were utilized to evaluate the association between GNPNAT1 expression and overall survival (OS) time in patients with LUAD. The results demonstrated that patients with increased GNPNAT1 expression levels exhibited a reduced survival rate compared with those with decreased expression levels (P=8.9×10⁻⁵). In addition, Cox regression analysis revealed that GNPNAT1 upregulation was significantly associated with poor OS time [hazard ratio (HR): 1.07; 95% confidence interval (CI): 1.04–1.10; P<0.001]. The gene set enrichment analysis revealed that ‘cell cycle’, ‘oocyte meiosis’, ‘pyrimidine mediated metabolism’, ‘ubiquitin mediated proteolysis’, ‘one carbon pool by folate’, ‘mismatch repair progesterone-mediated oocyte maturation’ and ‘basal transcription factors purine metabolism’ were differentially enriched in the GNPNAT1 high-expression samples compared with GNPNAT1 low-expression samples. The aforementioned pathways are involved in the pathogenesis of LUAD. The findings of the present study suggested that GNPNAT1 upregulation may be considered as a promising diagnostic and prognostic biomarker in patients with LUAD. In addition, the aforementioned pathways may be pivotal pathways perturbed by the abnormal expression of GNPNAT1 in LUAD. The findings of the present study demonstrated the therapeutic value of the regulation of GNPNAT1 in lung adenocarcinoma.

CLCA4 and MS4A12 as the significant gene biomarkers of primary colorectal cancer

Background: Primary colorectal cancer (PCRC) is a common digestive tract cancer in the elderly. However, the treatment effect of PCRC is still limited, and the long-term survival rate is low. Therefore, further exploring the pathogenesis of PCRC, and searching for specific molecular targets for diagnosis are the development trends of precise medical treatment, which have important clinical significance.

Methods: The public data were downloaded from Gene Expression Omnibus (GEO) database. Verification for repeatability of intra-group data was performed by Pearson’s correlation test and principal component analysis. Differentially expressed genes (DEGs) between normal and PCRC were identified, and the protein–protein interaction (PPI) network was constructed. Significant module and hub genes were found in the PPI network. A total of 192 PCRC patients were recruited between 2010 and 2019 from the Fourth Hospital of Hebei Medical University. RT-PCR was used to measure the relative expression of CLCA4 and MS4A12. Furthermore, the study explored the effect of expression of CLCA4 and MS4A12 for overall survival.

Results: A total of 53 DEGs were identified between PCRC and normal colorectal tissues. Ten hub genes concerned to PCRC were screened, namely CLCA4, GUCA2A, GCG, SST, MS4A12, PLP1, CHGA, PYY, VIP, and GUCA2B. The PCRC patients with low expression of CLCA4 and MS4A12 has a worse overall survival than high expression of CLCA4 and MS4A12 (P<0.05).

Conclusion: The research of DEGs in PCRC (53 DEGs, 10 hub genes, especially CLCA4 and MS4A12) and related signaling pathways is conducive to the differential analysis of the molecular mechanism of PCRC.

Potential Genes Associated with the Survival of Lung Adenocarcinoma Were Identified by Methylation

Background

Lung adenocarcinoma (LUAD) is the most common pathological type of lung cancer. The purpose of this study is to search for genes related to the prognosis of LUAD through methylation based on a linear mixed model (LMM).

Methods

Gene expression, methylation, and survival data of LUAD patients were downloaded from the TCGA database. Based on the LMM model, the GEMMA algorithm was used to screen the predictive genes related to LUAD survival. The Cox model was used to further screen the predicted genes, and then, protein-protein interaction (PPI) network was constructed. Through the software plugin Cytoscape MCODE 3.8.0, the most closely related genes in the PPI network module were selected for in-depth biological function analysis to further explore the interaction and correlation between genes.

Results

We screened out 97 predictive genes from 18,834 genes and eliminated one gene associated with lung squamous cell carcinoma from previous studies, leaving 96 genes. The MCODE and the Kaplan-Meier curve analysis were used to finally identify two genes ASB16 and NEDD4 that are related to the prognosis of LUAD.

Conclusions

The newly identified two genes associated with the prognosis of LUAD may provide a basis for the treatment of patients.

Downregulation of histone-lysine N-methyltransferase EZH2 inhibits cell viability and enhances chemosensitivity in lung cancer cells

Histone-lysine N-methyltransferase EZH2 (EZH2) is the principle component of the polycomb repressive complex 2 (PRC2)/embryonic ectoderm development protein-EZH2 complex, which promotes tumorigenesis by repressing transcription of tumor suppressor genes. EZH2 is considered a key marker in several types of cancer, such as colorectal and prostate cancer. However, the molecular mechanisms and clinical value of EZH2 in lung cancer have not yet been fully investigated. The aim of the present study was to investigate the functions of EZH2 in lung cancer progression and to determine whether treatment with an EZH2 inhibitor enhanced the chemosensitivity of lung cancer cells to cisplatin (CDDP). At the logarithmic growth phase, A549 cells were treated with a small interfering (si)RNA-EZH2, and cell viability was detected using an MTT assay. The degree of apoptosis and cell cycle were detected using flow cytometry. Cell migration and invasion were detected via wound healing and Transwell Matrigel assays. According to information from the Gene Expression Omnibus database, the results of the present study demonstrated that EZH2 was upregulated in lung cancer. Furthermore, overexpression of EZH2 was associated with poor patient prognosis, while EZH2 knockdown inhibited cell viability and migration, and enhanced apoptosis and chemosensitivity in a lung cancer cell line. EZH2 knockdown and treatment of A549 cells using EZH2 inhibitor elevated the inhibitory effects of CDDP on cell viability and apoptosis. Western blot and reverse transcription-quantitative PCR analyses were performed to assess the expression levels of relative protein and mRNA, respectively, in A549 cells treated with siRNA-EZH2 or with CDDP. Overall, the results of the present study demonstrated that high EZH2 expression was associated with poor prognosis, accompanied with a potential impairment of migration and viability in lung cancer cells. These findings suggest that EZH2 may act as a candidate molecular target for gene therapy, and treatment with EZH2 inhibitor may be used to increase chemosensitivity to CDDP agents in lung cancer.

Identification of Key Genes in Lung Adenocarcinoma and Establishment of Prognostic Mode

BACKGROUND

The development of human tumors is associated with the abnormal expression of various functional genes, and a massive tumor-based database needs to be deeply mined. Based on a multigene prediction model, access to urgent prognosis of patients has become possible.

MATERIALS AND METHODS

We selected three RNA expression profiles (GSE32863, GSE10072, and GSE43458) from the lung adenocarcinoma (LUAD) database of the Gene Expression Omnibus (GEO) and analyzed the differentially expressed genes (DEGs) between tumor and normal tissue using GEO2R program. After that, we analyzed the transcriptome data of 479 LUAD samples (54 normal tissue samples and 425 cancer tissue samples) and their clinical follow-up data from the (TCGA) database. Kaplan-Meier (KM) curve and receiver operating characteristic (ROC) were used to assess the prediction model. Multivariate Cox analysis was used to identify independent predictors. TCGA pancreatic adenocarcinoma datasets were used to establish a nomogram model.

RESULTS

We found 98 significantly prognosis-related genes using KM and COX analysis, among which six genes were found to be the DEGs in GEO. Using multivariate analysis, it was found that a single gene could not be used as an independent predictor of prognosis. However, the risk score calculated by weighting these six genes could serve as an independent prognosis predictor. COX analysis performed with multiple covariates such as age, gender, tumor stage, and TNM typing showed that risk score could still be utilized as an independent risk factor for patient survival rate (p = 0.013) and had an applicable reliability (area under the curve, AUC = 0.665). By combining risk score and various clinical features, the nomogram model was constructed, which had been proven to have high consistency for the prediction of 3- and 5-year survival rate (concordance = 0.751) and high accuracy as tested by ROC (AUC = 0.71;AUC = 0.708).

CONCLUSION

We proposed a method to predict the prognosis of LUAD by weighting multiple genes and constructed a nomogram model suitable for the prognostic evaluation of LUAD, which could provide a new tool for the identification of therapeutic targets and the efficacy evaluation of LUAD.

CKS2 and RMI2 are two prognostic biomarkers of lung adenocarcinoma

Background

Lung adenocarcinoma (ACA) is the most common subtype of non-small-cell lung cancer. About 70%–80% patients are diagnosed at an advanced stage; therefore, the survival rate is poor. It is urgent to discover accurate markers that can differentiate the late stages of lung ACA from the early stages. With the development of biochips, researchers are able to efficiently screen large amounts of biological analytes for multiple purposes.

Methods

Our team downloaded GSE75037 and GSE32863 from the Gene Expression Omnibus (GEO) database. Next, we utilized GEO’s online tool, GEO2R, to analyze the differentially expressed genes (DEGs) between stage I and stage II–IV lung ACA. The using the Cytoscape software was used to analyze the DEGs and the protein-protein interaction (PPI) network was further constructed. The function of the DEGs were further analyzed by cBioPortal and Gene Expression Profiling Interactive Analysis (GEPIA) online tools. We validated these results in 72 pairs human samples.

Results

We identified 109 co-DEGs, most of which were involved in either proliferation, S phase of mitotic cell cycle, regulation of exit from mitosis, DNA replication initiation, DNA replication, and chromosome segregation. Utilizing cBioPortal and University of California Santa Cruz databases, we further confirmed 35 hub genes. Two of these genes, encoding CDC28 protein kinase regulatory subunit 2 (CKS2) and RecQ-mediated genome instability 2 (RMI2), were upregulated in lung ACA compared with adjacent normal tissues. The Kaplan–Meier curves revealed upregulation of CKS2 and RMI2 are associated with worse survival. Using CMap analysis, we discovered 10 small molecular compounds that reversed the altered DEGs, the top five are phenoxybenzamine, adiphenine, resveratrol, and trifluoperazine. We also evaluated 72 pairs resected samples, results revealed that upregulation of CKS2 and RMI2 in lung ACA were associated with larger tumor size. Our results allow the deeper recognizing of the mechanisms of the progression of lung ACA, and may indicate potential therapeutic strategies for the therapy of lung ACA.

Identification of prognosis-related genes and construction of multi-regulatory networks in pancreatic cancer microenvironment by bioinformatics analysis

Background

As one of the most lethal cancers, pancreatic cancer has been characterized by abundant supportive tumor-stromal cell microenvironment. Although the advent of tumor-targeted immune checkpoint blockers has brought light to patients with other cancers, its clinical efficacy in pancreatic cancer has been greatly limited due to the protective stroma. Thus, it is urgent to find potential new targets and establish multi-regulatory networks to predict patient prognosis and improve treatment.

Methods

We followed a strategy based on mining the Cancer Genome Atlas (TCGA) database and ESTIMATE algorithm to obtain the immune scores and stromal scores. Differentially expressed genes (DEGs) associated with poor overall survival of pancreatic cancer were screened from a TCGA cohort. By comparing global gene expression with high vs. low immune scores and subsequent Kaplan–Meier analysis, DEGs that significantly correlate with poor overall survival of pancreatic cancer in TCGA cohort were extracted. After constructing the protein–protein interaction network using STRING and limiting the genes within the above DEGs, we utilized RAID 2.0, TRRUST v2 database and degree and betweenness analysis to obtain non-coding RNA (ncRNA)-pivotal nodes and TF-pivotal nodes. Finally, multi-regulatory networks have been constructed and pivotal drugs with potential benefit for pancreatic cancer patients were obtained by screening in the DrugBank.

Results

In this study, we obtained 246 DEGs that significantly correlate with poor overall survival of pancreatic cancer in the TCGA cohort. With the advent of 38 ncRNA-pivotal nodes and 7 TF-pivotal nodes, the multi-factor regulatory networks were constructed based on the above pivotal nodes. Prognosis-related genes and factors such as HCAR3, PPY, RFWD2, WSPAR and Amcinonide were screened and investigated.

Conclusion

The multi-regulatory networks constructed in this study are not only beneficial to improve treatment and evaluate patient prognosis with pancreatic cancer, but also favorable for implementing early diagnosis and personalized treatment. It is suggested that these factors may play an essential role in the progression of pancreatic cancer.