Bioinformatics Analysis of Candidate Genes and Pathways Related to Hepatocellular Carcinoma in China: A Study Based on Public Databases.

Background and Objective: Hepatocellular carcinoma (HCC) is a highly aggressive malignant tumor of the digestive system worldwide. Chronic hepatitis B virus (HBV) infection and aflatoxin exposure are predominant causes of HCC in China, whereas hepatitis C virus (HCV) infection and alcohol intake are likely the main risk factors in other countries. It is an unmet need to recognize the underlying molecular mechanisms of HCC in China. Methods: In this study, microarray datasets (GSE84005, GSE84402, GSE101685, and GSE115018) derived from Gene Expression Omnibus (GEO) database were analyzed to obtain the common differentially expressed genes (DEGs) by R software. Moreover, the gene ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed by using Database for Annotation, Visualization and Integrated Discovery (DAVID). Furthermore, the protein-protein interaction (PPI) network was constructed, and hub genes were identified by the Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape, respectively. The hub genes were verified using Gene Expression Profiling Interactive Analysis (GEPIA), UALCAN, and Kaplan-Meier Plotter online databases were performed on the TCGA HCC dataset. Moreover, the Human Protein Atlas (HPA) database was used to verify candidate genes' protein expression levels. Results: A total of 293 common DEGs were screened, including 103 up-regulated genes and 190 down-regulated genes. Moreover, GO analysis implied that common DEGs were mainly involved in the oxidation-reduction process, cytosol, and protein binding. KEGG pathway enrichment analysis presented that common DEGs were mainly enriched in metabolic pathways, complement and coagulation cascades, cell cycle, p53 signaling pathway, and tryptophan metabolism. In the PPI network, three subnetworks with high scores were detected using the Molecular Complex Detection (MCODE) plugin. The top 10 hub genes identified were CDK1, CCNB1, AURKA, CCNA2, KIF11, BUB1B, TOP2A, TPX2, HMMR and CDC45. The other public databases confirmed that high expression of the aforementioned genes related to poor overall survival among patients with HCC. Conclusion: This study primarily identified candidate genes and pathways involved in the underlying mechanisms of Chinese HCC, which is supposed to provide new targets for the diagnosis and treatment of HCC in China.

Seven bacterial response-related genes are biomarkers for colon cancer.

BACKGROUND: Colon cancer (CC) is a common tumor that causes significant harm to human health. Bacteria play a vital role in cancer biology, particularly the biology of CC. Genes related to bacterial response were seldom used to construct prognosis models. We constructed a bacterial response-related risk model based on three Molecular Signatures Database gene sets to explore new markers for predicting CC prognosis. METHODS: The Cancer Genome Atlas (TCGA) colon adenocarcinoma samples were used as the training set, and Gene Expression Omnibus (GEO) databases were used as the test set. Differentially expressed bacterial response-related genes were identified for prognostic gene selection. Univariate Cox regression analysis, least absolute shrinkage and selection operator-penalized Cox regression analysis, and multivariate Cox regression analysis were performed to construct a prognostic risk model. The individual diagnostic effects of genes in the prognostic model were also evaluated. Moreover, differentially expressed long noncoding RNAs (lncRNAs) were identified. Finally, the expression of these genes was validated using quantitative polymerase chain reaction (qPCR) in cell lines and tissues. RESULTS: A prognostic signature was constructed based on seven bacterial response genes: LGALS4, RORC, DDIT3, NSUN5, RBCK1, RGL2, and SERPINE1. Patients were assigned a risk score based on the prognostic model, and patients in the TCGA cohort with a high risk score had a poorer prognosis than those with a low risk score; a similar finding was observed in the GEO cohort. These seven prognostic model genes were also independent diagnostic factors. Finally, qPCR validated the differential expression of the seven model genes and two coexpressed lncRNAs (C6orf223 and SLC12A9-AS1) in 27 pairs of CC and normal tissues. Differential expression of LGALS4 and NSUN5 was also verified in cell lines (FHC, COLO320DM, SW480). CONCLUSIONS: We created a seven-gene bacterial response-related gene signature that can accurately predict the outcomes of patients with CC. This model can provide valuable insights for personalized treatment.

Identification of differentially expressed genes and pathways in kidney of ANCA-associated vasculitis by integrated bioinformatics analysis.

The antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) is a systematic of relatively rare autoimmune diseases with unknown cause. Kidney involvement is one of the most common clinical manifestations, and the degree of renal damage is closely associated with the development and prognosis of AAV. In this study, we utilized the Robust Rank Aggreg (RRA) method in R to integrate GSE104948, GSE104954, GSE108109, GSE108112, and GSE108113 profile datasets loaded from Gene Expression Omnibus (GEO) database and identified a set of differentially expressed genes (DEGs) in kidney between AAV patients and living donors. Then, the results of gene ontology (GO) functional annotation showed that immunity and metabolism involved process of AAV both in glomerulus and tubulointerstitial. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that following pathways, such as complement and coagulation cascades pathway; Staphylococcus aureus infection; disease-COVID-19; and systemic lupus erythematosus (SLE) pathway play a crucial role in AAV. Next, the results analyzed by protein-protein interaction (PPI) network and Cytoscape software exhibited the hub genes ALB, TYROBP, and CYBB existed in both glomerular and tubulointerstitial compartments datasets. Finally, KEGG analysis using genes of two most important modules also further validated complement and coagulation cascades pathway and S. aureus infection existed both in glomerulus and tubulointerstitial compartments datasets. In conclusion, this study identified key genes and pathways involved in kidney of AAV, which was benefit to further uncover the mechanisms underlying the development and progress of AAV, biomarkers, and potential therapeutic targets as well.

Identification of Potential Key Genes and Prognostic Biomarkers of Lung Cancer Based on Bioinformatics.

Objective: To analyze and identify the core genes related to the expression and prognosis of lung cancer including lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) by bioinformatics technology, with the aim of providing a reference for clinical treatment. Methods: Five sets of gene chips, GSE7670, GSE151102, GSE33532, GSE43458, and GSE19804, were obtained from the Gene Expression Omnibus (GEO) database. After using GEO2R to analyze the differentially expressed genes (DEGs) between lung cancer and normal tissues online, the common DEGs of the five sets of chips were obtained using a Venn online tool and imported into the Database for Annotation, Visualization, and Integrated Discovery (DAVID) database for Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The protein-protein interaction (PPI) network was constructed by STRING online software for further study, and the core genes were determined by Cytoscape software and KEGG pathway enrichment analysis. The clustering heat map was drawn by Excel software to verify its accuracy. In addition, we used the University of Alabama at Birmingham Cancer (UALCAN) website to analyze the expression of core genes in P53 mutation status, confirmed the expression of crucial core genes in lung cancer tissues with Gene Expression Profiling Interactive Analysis (GEPIA) and GEPIA2 online software, and evaluated their prognostic value in lung cancer patients with the Kaplan-Meier online plotter tool. Results: CHEK1, CCNB1, CCNB2, and CDK1 were selected. The expression levels of these four genes in lung cancer tissues were significantly higher than those in normal tissues. Their increased expression was negatively correlated with lung cancer patients (including LUAD and LUSC) prognosis and survival rate. Conclusion: CHEK1, CCNB1, CCNB2, and CDK1 are the critical core genes of lung cancer and are highly expressed in lung cancer. They are negatively correlated with the prognosis of lung cancer patients (including LUAD and LUSC) and closely related to the formation and prediction of lung cancer. They are valuable predictors and may be predictive biomarkers of lung cancer.

Bioinformatics and systems-biology analysis to determine the effects of Coronavirus disease 2019 on patients with allergic asthma.

Background: The coronavirus disease (COVID-19) pandemic has posed a significant challenge for global health systems. Increasing evidence shows that asthma phenotypes and comorbidities are major risk factors for COVID-19 symptom severity. However, the molecular mechanisms underlying the association between COVID-19 and asthma are poorly understood. Therefore, we conducted bioinformatics and systems biology analysis to identify common pathways and molecular biomarkers in patients with COVID-19 and asthma, as well as potential molecular mechanisms and candidate drugs for treating patients with both COVID-19 and asthma. Methods: Two sets of differentially expressed genes (DEGs) from the GSE171110 and GSE143192 datasets were intersected to identify common hub genes, shared pathways, and candidate drugs. In addition, murine models were utilized to explore the expression levels and associations of the hub genes in asthma and lung inflammation/injury. Results: We discovered 157 common DEGs between the asthma and COVID-19 datasets. A protein-protein-interaction network was built using various combinatorial statistical approaches and bioinformatics tools, which revealed several hub genes and critical modules. Six of the hub genes were markedly elevated in murine asthmatic lungs and were positively associated with IL-5, IL-13 and MUC5AC, which are the key mediators of allergic asthma. Gene Ontology and pathway analysis revealed common associations between asthma and COVID-19 progression. Finally, we identified transcription factor-gene interactions, DEG-microRNA coregulatory networks, and potential drug and chemical-compound interactions using the hub genes. Conclusion: We identified the top 15 hub genes that can be used as novel biomarkers of COVID-19 and asthma and discovered several promising candidate drugs that might be helpful for treating patients with COVID-19 and asthma.

Bioinformatics analysis of potential pathogenesis and risk genes of immunoinflammation-promoted renal injury in severe COVID-19.

Renal injury secondary to COVID-19 is an important factor for the poor prognosis of COVID-19 patients. The pathogenesis of renal injury caused by aberrant immune inflammatory of COVID-19 remains unclear. In this study, a total of 166 samples from 4 peripheral blood transcriptomic datasets of COVID-19 patients were integrated. By using the weighted gene co-expression network (WGCNA) algorithm, we identified key genes for mild, moderate, and severe COVID-19. Subsequently, taking these genes as input genes, we performed Short Time-series Expression Miner (STEM) analysis in a time consecutive ischemia-reperfusion injury (IRI) -kidney dataset to identify genes associated with renal injury in COVID-19. The results showed that only in severe COVID-19 there exist a small group of genes associated with the progression of renal injury. Gene enrichment analysis revealed that these genes are involved in extensive immune inflammation and cell death-related pathways. A further protein-protein interaction (PPI) network analysis screened 15 PPI-hub genes: ALOX5, CD38, GSF3R, LGR, RPR1, HCK, ITGAX, LYN, MAPK3, NCF4, SELP, SPI1, WAS, TLR2 and TLR4. Single-cell sequencing analysis indicated that PPI-hub genes were mainly distributed in neutrophils, macrophages, and dendritic cells. Intercellular ligand-receptor analysis characterized the activated ligand-receptors between these immune cells and parenchyma cells in depth. And KEGG enrichment analysis revealed that viral protein interaction with cytokine and cytokine receptor, necroptosis, and Toll-like receptor signaling pathway may be potentially essential for immune cell infiltration leading to COVID-19 renal injury. Finally, we validated the expression pattern of PPI-hub genes in an independent data set by random forest. In addition, we found that the high expression of these genes was correlated with a low glomerular filtration rate. Including them as risk genes in lasso regression, we constructed a Nomogram model for predicting severe COVID-19. In conclusion, our study explores the pathogenesis of renal injury promoted by immunoinflammatory in severe COVID-19 and extends the clinical utility of its key genes.

How I Use It: The Exosome Diagnostics (EPI) prostate cancer biomarker utility in urology and primary care.

Prostate-specific antigen (PSA) screening remains the mainstay for early detection of prostate cancer. Although PSA is a nonspecific prostate cancer biomarker, its specificity for high grade prostate cancer can be enhanced by pre-biopsy liquid biomarkers including the Exosome Dx Prostate IntelliScore (EPI) test. EPI is a stand-alone urine genomic test that measures 3 exosome-derived gene expression signatures without the need for digital rectal examination (DRE) or inclusion of standard of care parameters in the test algorithm. EPI has broad clinical utility as a risk stratification tool for clinically significant high grade prostate cancer in men considering diagnostic prostate biopsy (MRI-targeted and systematic biopsy). During the COVID-19 pandemic, the EPI At-Home Collection Kit was introduced and quickly became an important component of tele-urology. The EPI test has emerged as a prioritization tool for primary care referral to urologists and for prostate biopsy scheduling. EPI provides an objective and actionable genomic risk assessment tool for high grade prostate cancer and is a critical part of the informed decision-making regarding biopsy (targeted, systematic or both) in both urology and primary care practices.

Identification of tumor antigens and immune subtypes of glioma for mRNA vaccine development.

Recent evidence suggested that the mRNA vaccine has been effective for many tumors, but its progress in gliomas was slow. In this study, we screened potential tumor antigens and suitable populations for mRNA vaccine to develop mRNA vaccine for glioma. We integrated the normalized RNA sequencing expression data and somatic mutation data from TCGA-GBM, TCGA-LGG, and CGGA datasets. Putative antigens in glioma were identified by selecting highly mutated genes with intimate correlation with clinical survival and immune infiltration. An unsupervised partition around medoids algorithm was utilized to stably cluster the patients into five different immune subtypes. Among them, IS1/2 was cold tumor with low tumor mutation burden (TMB), immunogenic cell death (ICDs), and immune checkpoints (ICPs), and IS4/5 was hot tumor with high TMB, ICDs, and ICPs. Monocle3 package was used to evaluate the immune status similarity and evolution in glioma, which identified cluster IS2A/2B within IS2 subtype to be more suitable vaccination receivers. Weighted gene co-expression network analysis identified five hub immune genes as the biomarkers of patients' immune status in glioma. In conclusion, NAT1, FRRS1, GTF2H2C, BRCA2, GRAP, NR5A2, ABCB4, ZNF90, ERCC6L, and ZNF813 are potential antigens suitable for glioma mRNA vaccine. IS1/2A/2B are suitable for mRNA vaccination.

Genetic alterations, RNA expression profiling and DNA methylation of HMGB1 in malignancies.

The high mobility group box 1 (HMGB1) is a potential biomarker and therapeutic target in various human diseases. However, a systematic, comprehensive pan-cancer analysis of HMGB1 in human cancers remains to be reported. This study analysed the genetic alteration, RNA expression profiling and DNA methylation of HMGB1 in more than 30 types of tumours. It is worth noting that HMGB1 is overexpressed in malignant tissues, including lymphoid neoplasm diffuse large B-cell lymphoma (DLBC), pancreatic adenocarcinoma (PAAD) and thymoma (THYM). Interestingly, there is a positive correlation between the high expression of HMGB1 and the high survival prognosis of THYM. Finally, this study comprehensively evaluates the genetic variation of HMGB1 in human malignant tumours. As a prospective biomarker of COVID-19, the role that HMGB1 plays in THYM is highlighted.

C1R, CCL2, and TNFRSF1A Genes in Coronavirus Disease-COVID-19 Pathway Serve as Novel Molecular Biomarkers of GBM Prognosis and Immune Infiltration.

Glioblastoma multiforme (GBM) is a prevalent intracranial brain tumor associated with a high rate of recurrence and treatment difficulty. The prediction of novel molecular biomarkers through bioinformatics analysis may provide new clues into early detection and eventual treatment of GBM. Here, we used data from the GTEx and TCGA databases to identify 1923 differentially expressed genes (DEGs). GO and KEGG analyses indicated that DEGs were significantly enriched in immune response and coronavirus disease-COVID-19 pathways. Survival analyses revealed a significant correlation between high expression of C1R, CCL2, and TNFRSF1A in the coronavirus disease-COVID-19 pathway and the poor survival in GBM patients. Cell experiments indicated that the mRNA expression levels of C1R, CCL2, and TNFRSF1A in GBM cells were very high. Immune infiltration analysis revealed a significant difference in the proportion of immune cells in tumor and normal tissue, and the expression levels of C1R, CCL2, and TNFRSF1A were associated with immune cell infiltration of GBM. Additionally, the protein-protein interaction networks of C1R, CCL2, and TNFRSF1A involved a total of 65 nodes and 615 edges. These results suggest that C1R, CCL2, and TNFRSF1A may be used as molecular biomarkers of prognosis and immune infiltration in GBM patients in the future.

Molecular Beacon for Detection miRNA-21 as a Biomarker of Lung Cancer.

Lung cancer (LC) is the leading cause of cancer-related death worldwide. Although the diagnosis and treatment of non-small cell lung cancer (NSCLC), which accounts for approximately 80% of LC cases, have greatly improved in the past decade, there is still an urgent need to find more sensitive and specific screening methods. Recently, new molecular biomarkers are emerging as potential non-invasive diagnostic agents to screen NSCLC, including multiple microRNAs (miRNAs) that show an unusual expression profile. Moreover, peripheral blood mononuclear cells' (PBMCs) miRNA profile could be linked with NSCLC and used for diagnosis. We developed a molecular beacon (MB)-based miRNA detection strategy for NSCLC. Following PBMCs isolation and screening of the expression profile of a panel of miRNA by RT-qPCR, we designed a MB targeting of up-regulated miR-21-5p. This MB 21-5p was characterized by FRET-melting, CD, NMR and native PAGE, allowing the optimization of an in-situ approach involving miR-21-5p detection in PBMCs via MB. Data show the developed MB approach potential for miR-21-5p detection in PBMCs from clinical samples towards NSCLC.

Metalloproteinases 1 and 3 as Potential Biomarkers in Breast Cancer Development.

Breast cancer continues to be one of the main causes of morbidity and mortality globally and was the leading cause of cancer death in women in Spain in 2020. Early diagnosis is one of the most effective methods to lower the incidence and mortality rates of breast cancer. The human metalloproteinases (MMP) mainly function as proteolytic enzymes degrading the extracellular matrix and plays important roles in most steps of breast tumorigenesis. This retrospective cohort study shows the immunohistochemical expression levels of MMP-1, MMP-2, MMP-3, and MMP-9 in 154 women with breast cancer and 42 women without tumor disease. The samples of breast tissue are assessed using several tissue matrices (TMA). The percentages of staining (≤50%->50%) and intensity levels of staining (weak, moderate, or intense) are considered. The immunohistochemical expression of the MMP-1-intensity (p = 0.043) and MMP-3 percentage (p = 0.018) and intensity, (p = 0.025) present statistically significant associations with the variable group (control-case); therefore, expression in the tumor tissue samples of these MMPs may be related to the development of breast cancer. The relationships between these MMPs and some clinicopathological factors in breast cancer are also evaluated but no correlation is found. These results suggest the use of MMP-1 and MMP-3 as potential biomarkers of breast cancer diagnosis.

Highly sensitive and portable mRNA detection platform for early cancer detection.

Pancreatic cancer, at unresectable advanced stages, presents poor prognoses, which could be prevented by early pancreatic cancer diagnosis methods. Recently, a promising early-stage pancreatic cancer biomarker, extracellular vesicles (EVs) related glypican-1 (GPC1) mRNA, is found to overexpress in pancreatic cancer cells. Current mRNA detection methods usually require expensive machinery, strict preservation environments, and time-consuming processes to guarantee detection sensitivity, specificity, and stability. Herein, we propose a novel two-step amplification method (CHAGE) via the target triggered Catalytic Hairpin Assembly strategy combined with Gold-Enhanced point-of-care-testing (POCT) technology for sensitive visual detection of pancreatic cancer biomarker. First, utilizing the catalyzed hairpin DNA circuit, low expression of the GPC1 mRNA was changed into amplification product 1 (AP1, a DNA duplex) as the next detection targets of the paper strips. Second, the AP1 was loaded onto a lateral flow assay and captured with the gold signal nanoparticles to visualize results. Finally, the detected results can be further enhanced by depositing gold to re-enlarge the sizes of gold nanoparticles in detection zones. As a result, the CHAGE methodology lowers the detection limit of mRNA to 100 fM and provides results within 2 h at 37 °C. Furthermore, we demonstrate the successful application in discriminating pancreatic cancer cells by analyzing EVs' GPC1 mRNA expression levels. Hence, the CHAGE methodology proposed here provides a rapid and convenient POCT platform for sensitive detection of mRNAs through unique probes designs (COVID, HPV, etc.).

New Insights Into Pancreatic Cancer: Notes from a Virtual Meeting.

Pancreatic ductal adenocarcinoma remains a major challenge in cancer medicine. Given the increase in incidence and mortality, interdisciplinary research is necessary to translate basic knowledge into therapeutic strategies improving the outcome of patients. On the 4th and 5th of February 2021, three German pancreatic cancer research centers, the Clinical Research Unit 5002 from Göttingen, the Collaborative Research Center 1321 from Munich, and Clinical Research Unit 325 from Marburg organized the 1st Virtual Göttingen-Munich-Marburg Pancreatic Cancer Meeting in order to foster scientific exchange. This report summarizes current research and proceedings presented during that meeting.

High concordance of 70-gene recurrence risk signature and 80-gene molecular subtyping signature between core needle biopsy and surgical resection specimens in early-stage breast cancer.

BACKGROUND AND OBJECTIVES: With increased neoadjuvant therapy recommendations for early-stage breast cancer patients due to the COVID-19 pandemic, it is imperative that molecular diagnostic assays provide reliable results from preoperative core needle biopsies (CNB). The study objective was to determine the concordance of MammaPrint and BluePrint results between matched CNB and surgical resection (SR) specimens. METHODS: Matched tumor specimens (n = 121) were prospectively collected from women enrolled in the FLEX trial (NCT03053193). Concordance is reported using overall percentage agreement and Cohen's kappa coefficient. Correlation is reported using Pearson correlation coefficient. RESULTS: We found good concordance for MammaPrint results between matched tumor samples (90.9%, κ = 0.817), and a very strong correlation of MammaPrint indices (r = 0.94). The concordance of BluePrint subtyping in matched samples was also excellent (98.3%). CONCLUSIONS: CNB samples demonstrated high concordance with paired SR samples for MammaPrint risk classification and BluePrint molecular subtyping, suggesting that physicians are provided with accurate prognostic information that can be used to guide therapy decisions.

Defining the emergence of myeloid-derived suppressor cells in breast cancer using single-cell transcriptomics.

Myeloid-derived suppressor cells (MDSCs) are innate immune cells that acquire the capacity to suppress adaptive immune responses during cancer. It remains elusive how MDSCs differ from their normal myeloid counterparts, which limits our ability to specifically detect and therapeutically target MDSCs during cancer. Here, we sought to determine the molecular features of breast cancer-associated MDSCs using the widely studied mouse model based on the mouse mammary tumor virus (MMTV) promoter-driven expression of the polyomavirus middle T oncoprotein (MMTV-PyMT). To identify MDSCs in an unbiased manner, we used single-cell RNA sequencing to compare MDSC-containing splenic myeloid cells from breast tumor-bearing mice with wild-type controls. Our computational analysis of 14,646 single-cell transcriptomes revealed that MDSCs emerge through an aberrant neutrophil maturation trajectory in the spleen that confers them an immunosuppressive cell state. We establish the MDSC-specific gene signature and identify CD84 as a surface marker for improved detection and enrichment of MDSCs in breast cancers.

BC-N102 suppress breast cancer tumorigenesis by interfering with cell cycle regulatory proteins and hormonal signaling, and induction of time-course arrest of cell cycle at G1/G0 phase.

Mechanisms of breast cancer progression and invasion, often involve alteration of hormonal signaling, and upregulation and/or activation of signal transduction pathways that input to cell cycle regulation. Herein, we describe a rationally designed first-in-class novel small molecule inhibitor for targeting oncogenic and hormonal signaling in ER-positive breast cancer. BC-N102 treatment exhibits dose-dependent cytotoxic effects against ER+ breast cancer cell lines. BC-N102 exhibited time course- and dose-dependent cell cycle arrest via downregulation of the estrogen receptor (ER), progesterone receptor (PR), androgen receptor (AR), phosphatidylinositol 3-kinase (PI3K), phosphorylated (p)-extracellular signal-regulated kinase (ERK), p-Akt, CDK2, and CDK4 while increasing p38 mitogen-activated protein kinase (MAPK), and mineralocorticoid receptor (MR) signaling in breast cancer cell line. In addition, we found that BC-N102 suppressed breast cancer tumorigenesis in vivo and prolonged the survival of animals. Our results suggest that the proper application of BC-N102 may be a beneficial chemotherapeutic strategy for ER+ breast cancer patients.

Bioinformatics approach to identify common gene signatures of patients with coronavirus 2019 and lung adenocarcinoma.

Coronavirus disease 2019 (COVID-19) continues as a global pandemic. Patients with lung cancer infected with COVID-19 may develop severe disease or die. Treating such patients severely burdens overwhelmed healthcare systems. Here, we identified potential pathological mechanisms shared between patients with COVID-19 and lung adenocarcinoma (LUAD). Co-expressed, differentially expressed genes (DEGs) in patients with COVID-19 and LUAD were identified and used to construct a protein-protein interaction (PPI) network and to perform enrichment analysis. We used the NetworkAnalyst platform to establish a co-regulatory of the co-expressed DEGs, and we used Spearman's correlation to evaluate the significance of associations of hub genes with immune infiltration and immune checkpoints. Analysis of three datasets identified 112 shared DEGs, which were used to construct a protein-PPI network. Subsequent enrichment analysis revealed co-expressed genes related to biological process (BP), molecular function (MF), and cellular component (CC) as well as to pathways, specific organs, cells, and diseases. Ten co-expressed hub genes were employed to construct a gene-miRNA, transcription factor (TF)-gene, and TF-miRNA network. Hub genes were significantly associated with immune infiltration and immune checkpoints. Finally, methylation level of hub genes in LUAD was obtained via UALCAN database. The present multi-dimensional study reveals commonality in specific gene expression by patients with COVID-19 and LUAD. These findings provide insights into developing strategies for optimising the management and treatment of patients with LUAD with COVID-19.

Identification of hub genes and molecular subtypes in COVID-19 based on WGCNA.

OBJECTIVE: The heterogeneity of clinical manifestations and mortality rates in Coronavirus disease 2019 (COVID-19) patients may be related to the existence of molecular subtypes in COVID-19. To improve current management, it is essential to find the hub genes and pathways associated with different COVID-19 subtypes. MATERIALS AND METHODS: The whole-genome sequencing information (GSE156063, GSE163151) of nasopharyngeal swabs from normal subjects and COVID-19 patients were downloaded from the Gene Expression Omnibus (GEO) database. The molecular subtypes of patients with COVID-19 were classified using the "consistent clustering" method, and the specific genes associated with each subtype were found. Differentially expressed genes (DEGs) were screened between normal subjects and COVID-19 patients; the Weighted gene co-expression network analysis (WGCNA) method was used to find the key module genes of COVID-19 patients. Subtype-specific, differentially expressed and module-related genes were collected and intersected. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were carried out and protein-protein interaction (PPI) networks were generated. The pathways enriched in COVID-19 subtypes were analyzed by gene set variation analysis (GSVA). RESULTS: Patients with COVID-19 were divided into three subtypes, and there was no significant difference in gender and age distribution between subtypes. 82 differential gene pathways were screened between Subtypes I and II, 131 differential gene pathways were screened between Subtypes I and III, and 107 differential gene pathways were screened between Subtypes II and III. Finally, 44 differentially expressed key genes were screened, including 11 hub genes (RSAD2, IFIT1, MX1, OAS1, OAS2, BST2, IFI27, IFI35, IFI6, IFITM3, STAT2). CONCLUSIONS: There are significant differences in gene activation and pathway enrichment among different molecular subtypes of COVID-19, which may account for the heterogeneity in clinical presentation and the prognosis of patients.

How Does Invasive Breast Cancer Oncotype Dx Recurrence Score on Core Needle Biopsies Influence Neoadjuvant Treatment Decision? A Descriptive Study.

BACKGROUND: Oncotype Dx (ODx) is a genomic assay which estimates the risk of distant recurrence and predicts adjuvant chemotherapy benefit in early stage breast cancer patients. Most ODx data is derived from excisional specimens. AIM: We assess the utility of ODx on core needle biopsies (CNB) and measure its impact on neoadjuvant treatment decisions, particularly in patients with clinically complicated situations. METHODS: Consecutive ODx results on breast CNBs with invasive carcinoma from 2012-2020 at 3 tertiary care hospitals with dedicated Breast Health Centers were reviewed. Clinical indications to perform ODx on CNB were recorded through a review of patients' electronic medical records. Clinicopathologic features, surgical or oncologic modalities and follow-up data were recorded. RESULTS: Three distinct clinical indications for performing ODx on CNB in 85 ER+ invasive breast carcinomas were identified: 1) Excisions with insufficient tissue to perform ODx, 2) adjudicate neoadjuvant therapy versus primary surgical resection, and 3) select neoadjuvant chemotherapy (NAC) versus neoadjuvant endocrine therapy (NET). Primary surgery was selected in patients with low score RS (<18), and NET was preferred in patients with intermediate or high RS (>18). NET was preferred over NAC in patients with low RS (<18). CONCLUSION: This study shows that CNB ODx RS helps guide treatment decisions in a neoadjuvant setting along with other contributing factors such as the presence of pathogenic mutations, node positivity, patient age, and comorbidities. The use of ODx on CNB is furthermore valuable in the midst of the COVID-19 pandemic for early breast cancer patients to administer effective therapy in a timely manner.