Integrated bioinformatics analysis reveals upregulated extracellular matrix hub genes in pancreatic cancer: Implications for diagnosis, prognosis, immune infiltration, and therapeutic strategies

BACKGROUND

Pancreatic cancer (PC) stands out as one of the most formidable malignancies and exhibits an exceptionally unfavorable clinical prognosis due to the absence of well-defined diagnostic indicators and its tendency to develop resistance to therapeutic interventions. The primary objective of this present study was to identify extracellular matrix (ECM)-related hub genes (HGs) and their corresponding molecular signatures, with the intent of potentially utilizing them as biomarkers for diagnostic, prognostic, and therapeutic applications.

METHODS

Three microarray datasets were sourced from the NCBI database to acquire upregulated differentially expressed genes (DEGs), while MatrisomeDB was employed for filtering ECM-related genes. Subsequently, a protein-protein interaction (PPI) network was established using the STRING database. The created network was visually inspected through Cytoscape, and HGs were identified using the CytoHubba plugin tool. Furthermore, enrichment analysis, expression pattern analysis, clinicopathological correlation, survival analysis, immune cell infiltration analysis, and examination of chemical compounds were carried out using Enrichr, GEPIA2, ULCAN, Kaplan Meier plotter, TIMER2.0, and CTD web platforms, respectively. The diagnostic and prognostic significance of HGs was evaluated through the ROC curve analysis.

RESULTS

Ten genes associated with ECM functions were identified as HGs among 131 DEGs obtained from microarray datasets. Notably, the expression of these HGs exhibited significantly (p < 0.05) higher in PC, demonstrating a clear association with tumor advancement. Remarkably, higher expression levels of these HGs were inversely correlated with the likelihood of patient survival. Moreover, ROC curve analysis revealed that identified HGs are promising biomarkers for both diagnostic (AUC > 0.75) and prognostic (AUC > 0.64) purposes. Furthermore, we observed a positive correlation between immune cell infiltration and the expression of most HGs. Lastly, our study identified nine compounds with significant interaction profiles that could potentially act as effective chemical agents targeting the identified HGs.

CONCLUSION

Taken together, our findings suggest that COL1A1, KRT19, MMP1, COL11A1, SDC1, ITGA2, COL1A2, POSTN, FN1, and COL5A1 hold promise as innovative biomarkers for both the diagnosis and prognosis of PC, and they present as prospective targets for therapeutic interventions aimed at impeding the progression PC.

The impact of wood dust on pulmonary function and blood immunoglobulin E, erythrocyte sedimentation rate, and C‐ reactive protein: A cross‐sectional study among sawmill workers in Tangail, Bangladesh

Background and Aims

Occupational exposure to wood dust leads to lung function abnormalities that are prominent causes of morbidity and disability of sawmill workers. The adverse respiratory effects of wood dust in sawmills have not been studied thoroughly in Bangladesh. This study aimed to investigate the effect of wood dust on the respiratory health of sawmill workers compared to controls as well as to determine the association of wood dust‐exposing effects with inflammatory blood biomarkers, such as immunoglobulin E (IgE), erythrocyte sedimentation rate (ESR), and C‐reactive protein (CRP).

Methods

This cross‐sectional study included 100 sawmill workers from 25 distinct sawmills in various areas of Tangail, Bangladesh as well as 100 healthy volunteers who were adopted as a control group. Questionaries' survey and pulmonary function tests were performed face to face. Furthermore, after performing lung function tests, blood was drawn for further IgE, ESR, and CRP analyses.

Results

Respiratory symptoms including breathlessness (32%), coughing (39%), sneezing (43%), chest tightness (30%), and itching (40%) were significantly higher in sawmill workers compared with control. Besides, sawmill workers' exposure to wood dust revealed a significantly lower level of spirometry parameters (forced vital capacity ​​​​​[FVC], FVC (%), forced expiratory volume in 1 s [FEV1], FEV1 (%), peak expiratory flow [PEF], PEF (%), FEV1/FVC (%), FEF25, FEF75, and FEF2575) compared with control and these spirometry parameters decreased with the increasing length of service. Moreover, a significantly higher level of IgE was observed in sawmill workers (290.90 ± 39.49) than in the control (120.95 ± 23.00). The high level of IgE suggests that the lower pulmonary function may be linked to allergic responses to wood dust among sawmill workers.

Conclusion

This study suggested that exposure to wood dust can cause impairment of respiratory function along with high IgE levels.

A Computational Approach to Justifying Stratifin as a Candidate Diagnostic and Prognostic Biomarker for Pancreatic Cancer

Pancreatic cancer (PC) is considered a silent killer because it does not show specific symptoms at an early stage. Thus, identifying suitable biomarkers is important to avoid the burden of PC. Stratifin (SFN) encodes the 14-3-3σ protein, which is expressed in a tissue-dependent manner and plays a vital role in cell cycle regulation. Thus, SFN could be a promising therapeutic target for several types of cancer. This study was aimed at investigating, using online bioinformatics tools, whether SFN could be used as a diagnostic and prognostic biomarker in PC. SFN expression was explored by utilizing the ONCOMINE, UALCAN, GEPIA2, and GENT2 tools, which revealed that SFN expression is higher in PC than in normal tissues. The clinicopathological analysis using the ULCAN tool showed that the intensity of SFN expression is commensurate with cancer progression. GEPIA2, R2, and OncoLnc revealed a negative correlation between SFN expression and survival probability in PC patients. The ONCOMINE, UCSC Xena, and GEPIA2 tools showed that cofilin 1 is strongly coexpressed with SFN. Moreover, enrichment and network analyses of SFN were performed using the Enrichr and NetworkAnalyst platforms, respectively. Receiver operating characteristic (ROC) curves revealed that tissue-dependent expression of the SFN gene could serve as a diagnostic and prognostic biomarker. However, further wet laboratory studies are necessary to determine the relevance of SFN expression as a biomarker.

Common genetic aspects between COVID-19 and sarcoidosis: A network-based approach using gene expression data

The pandemic situation of novel coronavirus disease 2019 (COVID-19) is a global threat on our current planet, with its rapid spread and high mortality rate. Sarcoidosis patients are at high risk to COVID-19 severity for having lung injuries as well as treating with immunosuppressive agents. So, physicians are in dilemma whether they should use immunosuppressive agents or not for the patients with sarcoidosis history and COVID-19 infection. Therefore, common factors should be identified to provide effective treatment. For determining the common genes between COVID-19 and sarcoidosis, GSE164805 and GSE18781 were retrieved from the Gene Expression Omnibus (GEO) database. Common upregulated genes were identified by using R language to investigate their involved pathways and gene ontologies (GO). With the aid of the STRING Cytoscape plugin tool, protein-protein interactions (PPIs) network was constructed. From the PPIs network, Hub genes and essential modules were detected by using Cytohubba, and MCODE respectively. For hub genes, TFs, TFs-miRNA, and drug, interaction networks were built through the NetworkAnalyst web platform. A total of 34 common upregulated genes were identified and among them, five hub genes, including TET2, MUC5AC, VDR, NFE2L2, and BCL6 were determined. In addition, a cluster having VDR and NFE2L2 was detected from the PPIs network. Moreover, 32 transcription factors and 9 miRNA were recognized for hub genes. Furthermore, vitamin D and some of its analogous compounds were obtained from the drug interaction network. In conclusion, hub genes identified in this study might have potential roles in modulating COVID-19 infection and sarcoidosis. However, further studies are required to corroborate this study.

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Sarcoidosis patients are at high risk to COVID-19 severity.

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This study aimed to find out common genetic factors for COVID-19 and Sarcoidosis.

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34 common upregulated genes were identified from GSE164805 and GSE18781 datasets.

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From common upregulated genes, five hub genes, VDR, NFE2L2, BCL6, TET2, and MUC5AC, were recognized.

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Hub genes associated with miRNA, TFs, and drug molecules were also identified.