Tumor Microenvironment Can Predict Chemotherapy Response of Patients with Triple-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy.

PURPOSE: Triple-negative breast cancer (TNBC) is a breast cancer subtype that has poor prognosis and exhibits a unique tumor microenvironment. Analysis of the tumor microbiome has indicated a relationship between the tumor microenvironment and treatment response. Therefore, we attempted to reveal the role of the tumor microbiome in patients with TNBC receiving neoadjuvant chemotherapy. MATERIALS AND METHODS: We collected TNBC patient RNA-sequencing samples from the Gene Expression Omnibus and extracted microbiome count data. Differential and relative abundance were estimated with linear discriminant analysis effect size. We calculated the immune cell fraction with CIBERSORTx and conducted survival analysis using the Cancer Genome Atlas patient data. Correlations between the microbiome and immune cell compositions were analyzed and a prediction model was constructed to estimate drug response. RESULTS: Among the pathological complete response group (pCR), the beta diversity varied considerably; consequently, 20 genera and 24 species were observed to express a significant differential and relative abundance. Pandoraea pulmonicola and Brucella melitensis were found to be important features in determining drug response. In correlation analysis, Geosporobacter ferrireducens, Streptococcus sanguinis, and resting natural killer cells were the most correlated factors in the pCR, whereas Nitrosospira briensis, Plantactinospora sp. BC1, and regulatory T cells were key features in the residual disease group. CONCLUSION: Our study demonstrated that the microbiome analysis of tumor tissue can predict chemotherapy response of patients with TNBC. Further, the immunological tumor microenvironment may be impacted by the tumor microbiome, thereby affecting the corresponding survival and treatment response.

Proliferation-Related Features of the Human Mesenchymal Stem Cells Derived from Palatine Tonsils, Adipose Tissues, and Bone Marrow.

BACKGROUND: Mesenchymal stem cells (MSCs) are widely used in regenerative medicine and cell-based transplantations. However, an in-depth comparison of the different MSC origins is lacking. This study aimed to compare the expression of adipose-derived (AMSCs), bone marrow-derived (BMSCs), and tonsil-derived (TMSCs) and evaluate whether TMSCs are good alternatives for AMSCs or BMSCs. METHODS: We analyzed the expression levels of 47,000 transcripts in AMSCs (n = 4), BMSCs (n = 4), and TMSCs (n = 4) using GeneChip. Microarray data were analyzed using the LIMMA package to compare the TMSCs, AMSCs, and BMSCs. Hub genes were analyzed using STRING and Cytoscape. To ascertain the functional roles of AURKA and AURKB, small interfering RNA (siRNA) molecules specifically targeting AURKA and AURKB mRNA were synthesized and employed to induce knockdown of AURKA and AURKB in TMSC and AMSC. We analyzed the expression level of OCT4, SOX-2, and NANOG genes in TMSC and AMSCs by cell culture and real-time PCR. RESULTS: We identified commonly increased 256 and decreased 160 genes in TMSCs from the differentially expressed genes (DEGs) between the TMSCs, AMSCs, and BMSCs. In the DEG-based protein-protein interaction and gene set enrichment analysis, hub genes (AURKA, AURKB, CDC20, and BUB1) highly expressed in TMSCs were enriched for development- and progression-related oocyte meiosis, the cell cycle, and ubiquitin-mediated proteolysis. In vitro analysis demonstrated that cells with downregulated expression of AURKA and AURKB exhibited a significant reduction in proliferation compared to control cells. However, silencing of the genes did not affect the differentiation capacity in TMSCs and AMSCs. CONCLUSION: Our study compared MSCs of different origins to better understand the similarities and differences among these cell types.

Single-cell RNA sequencing reveals rebalancing of immunological response in patients with periodontitis after non-surgical periodontal therapy.

BACKGROUND: Periodontitis is a major inflammatory disease of the oral mucosa that is not limited to the oral cavity but also has systemic consequences. Although the importance of chronic periodontitis has been emphasized, the systemic immune response induced by periodontitis and its therapeutic effects remain elusive. Here, we report the transcriptomes of peripheral blood mononuclear cells (PBMCs) from patients with periodontitis. METHODS: Using single-cell RNA sequencing, we profiled PBMCs from healthy controls and paired pre- and post-treatment patients with periodontitis. We extracted differentially expressed genes and biological pathways for each cell type and calculated activity scores reflecting cellular characteristics. Intercellular crosstalk was classified into therapy-responsive and -nonresponsive pathways. RESULTS: We analyzed pan-cellular differentially expressed genes caused by periodontitis and found that most cell types showed a significant increase in CRIP1, which was further supported by the increased levels of plasma CRIP1 observed in patients with periodontitis. In addition, activated cell type-specific ligand-receptor interactions, including the BTLA, IFN-γ, and RESISTIN pathways, were prominent in patients with periodontitis. Both the BTLA and IFN-γ pathways returned to similar levels in healthy controls after periodontal therapy, whereas the RESISTIN pathway was still activated even after therapy. CONCLUSION: These data collectively provide insights into the transcriptome changes and molecular interactions that are responsive to periodontal treatment. We identified periodontitis-specific systemic inflammatory indicators and suggest unresolved signals of non-surgical therapy as future therapeutic targets.

Clinical big-data-based design of GLUT2-targeted carbon nanodots for accurate diagnosis of hepatocellular carcinoma.

Despite advances in diagnostic and therapeutic methods, the prognosis of patients with hepatocellular carcinoma (HCC) remains poor due to the delay in diagnosis. Herein, we aimed to discover a highly sensitive and specific biomarker for HCC based on genomic big data analysis and create an HCC-targeted imaging probe using carbon nanodots (CNDs) as contrast agents. In genomic analysis, we selected glucose transporter 2 (GLUT2) as a potential imaging target for HCC. We confirmed the target suitability by immunohisto-chemistry tests of 339 patient samples, where 81.1% of the patients exhibited underexpression of GLUT2, i.e., higher GLUT2 intensity in non-tumor tissues than in tumor tissues. To visualize GLUT2, we conjugated CNDs with glucosamine (GLN) as a targeting ligand to yield glucosamine-labeled CNDs (GLN-CNDs). A series of in vitro and in vivo experiments were conducted on GLUT2-modified HepG2 cells to confirm the specificity of the GLN-CNDs. Since the GLUT2 expression is higher in hepatocytes than in HCC cells, the GLUT2-targeted contrast agent is highly attached to normal cells. However, it is possible to produce images in the same form as the images obtained with a cancer cell-targeted contrast agent by inverting color scaling. Our results indicate that GLUT2 is a promising target for HCC and that GLN-CNDs may potentially be used as targeted imaging probes for diagnosing HCC.

Angiotensin-converting enzyme inhibitors or angiotensin receptor blockers and cancer risk: an updated meta-analysis of observational studies.

Introduction: Debate on the association between the use of angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) and the risk of developing cancer has been ongoing for decades. This study aimed to generate reliable results by analysing observational studies published in the decade after our last meta-analysis was conducted. Methods: We searched Embase and Medline databases on 21 January 2021 for cohort and case-control studies. Two researchers independently reviewed the literature and assessed the title and abstract of each publication. The I2 statistic used to evaluate the heterogeneity of the effect measures. Risk of bias was qualitatively assessed using the Newcastle-Ottawa scale. Results and discussion: We included an additional 16 cohort, 6 nested case-control, and 9 conventional case-control studies in the updated analysis. Overall HRs decreased, while overall relative risks increased. Conclusion: Our results show some protective effects through the hazard ratio and some detrimental effects through the relative risk. Large-scale investigations of cohorts followed up for decades are needed to clarify association. Plain Language Summary: Introduction: Two types of drug, angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs), have been linked to the risk of developing cancer. We performed a meta-analysis by aggregating individual studies looking into the cancer risk of ACEIs and ARBs.Methods: We searched for articles on Embase and Medline databases until 21 January, 2021. Two researchers independently reviewed the literature and assessed the title and abstract of each publication.Results: Overall, the hazard ratio showed less than 1, while the relative risks showed higher than 1.Conclusion: Our results show some protective effects through the hazard ratio and some detrimental effects through the relative risk. Evidence supporting the risk of developing cancer is insufficient to prevent prescribing ACEIs or ARBs for patients with high blood pressure.

Microbial and molecular differences according to the location of head and neck cancers.

BACKGROUND: Microbiome has been shown to substantially contribute to some cancers. However, the diagnostic implications of microbiome in head and neck squamous cell carcinoma (HNSCC) remain unknown. METHODS: To identify the molecular difference in the microbiome of oral and non-oral HNSCC, primary data was downloaded from the Kraken-TCGA dataset. The molecular differences in the microbiome of oral and non-oral HNSCC were identified using the linear discriminant analysis effect size method. RESULTS: In the study, the common microbiomes in oral and non-oral cancers were Fusobacterium, Leptotrichia, Selenomonas and Treponema and Clostridium and Pseudoalteromonas, respectively. We found unique microbial signatures that positively correlated with Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways in oral cancer and positively and negatively correlated KEGG pathways in non-oral cancer. In oral cancer, positively correlated genes were mostly found in prion diseases, Alzheimer disease, Parkinson disease, Salmonella infection, and Pathogenic Escherichia coli infection. In non-oral cancer, positively correlated genes showed Herpes simplex virus 1 infection and Spliceosome and negatively correlated genes showed results from PI3K-Akt signaling pathway, Focal adhesion, Regulation of actin cytoskeleton, ECM-receptor interaction and Dilated cardiomyopathy. CONCLUSIONS: These results could help in understanding the underlying biological mechanisms of the microbiome of oral and non-oral HNSCC. Microbiome-based oncology diagnostic tool warrants further exploration.

DeepCIA: a novel deep-learning model for cancer type identification using class activation map via transcription factor expression.

Deep learning methods are powerful analytical tools for large-scale data analysis. Here, we introduce DeepCIA as a novel diagnostic deep-learning model for cancer type identification using a class activation map via transcription factor expression. Although many deep learning researches attempts have recently been made in relation to cancer diagnosis, there are difficulties in using cancer data due to a large-scale problem. Therefore, From The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) public databases, we selected transcription factor expression profiles of eight cancer types. TCGA included 3496 samples and divided the train and validation sets in an 8:2 ratio. ICGC included 552 samples and was used as a test set for external validation. To compare the performance of 1D-CNN models, we also used SVM and KNN from machine learning. In external validation, 1D-CNN showed a high average accuracy of 98% and was superior to support vector machine (SVM) and k-nearest neighbor (KNN) with a difference in the accuracy of 10-12%. Also, 1D-CNN performed very well in several performance metrics (98.2% Recall, 98.1% Precision, 98.2% F score, 99.8% Specificity, 99.8% AUC, and 99.0% Balanced Accuracy). In each data set evaluation, 1-network, 5-network, and 2-network with high accuracy were selected and visualized through the Class Activation Map. We identified the Cys2Hys2 zinc finger group with the highest distribution across all cancer types. Collectively, DeepCIA can be used as a decision support system for cancer and a classifier for diagnosing unknown primary cancer, while emphasizing its usefulness in cancer diagnosis.

SOCS3 is Related to Cell Proliferation in Neuronal Tissue: An Integrated Analysis of Bioinformatics and Experiments.

Glioma is the most common primary malignant tumor that occurs in the central nervous system. Gliomas are subdivided according to a combination of microscopic morphological, molecular, and genetic factors. Glioblastoma (GBM) is the most aggressive malignant tumor; however, efficient therapies or specific target molecules for GBM have not been developed. We accessed RNA-seq and clinical data from The Cancer Genome Atlas, the Chinese Glioma Genome Atlas, and the GSE16011 dataset, and identified differentially expressed genes (DEGs) that were common to both GBM and lower-grade glioma (LGG) in three independent cohorts. The biological functions of common DEGs were examined using NetworkAnalyst. To evaluate the prognostic performance of common DEGs, we performed Kaplan-Meier and Cox regression analyses. We investigated the function of SOCS3 in the central nervous system using three GBM cell lines as well as zebrafish embryos. There were 168 upregulated genes and 50 downregulated genes that were commom to both GBM and LGG. Through survival analyses, we found that SOCS3 was the only prognostic gene in all cohorts. Inhibition of SOCS3 using siRNA decreased the proliferation of GBM cell lines. We also found that the zebrafish ortholog, socs3b, was associated with brain development through the regulation of cell proliferation in neuronal tissue. While additional mechanistic studies are necessary, our results suggest that SOCS3 is an important biomarker for glioma and that SOCS3 is related to the proliferation of neuronal tissue.

Prognostic role of high cathepsin D expression in breast cancer: a systematic review and meta-analysis.

BACKGROUND: High cathepsin D has been associated with poor prognosis in breast cancer; however, the results of many studies are controversial. Here, we assessed the association between high cathepsin D levels and worse breast cancer prognosis by conducting a meta-analysis. METHODS: A comprehensive search strategy was used to search relevant literature in PUBMED and EMBASE by September 2018. The meta-analysis was performed in Review Manager 5.3 using hazard ratios (HRs) with 95% confidence intervals (CIs). RESULTS: A total of 15,355 breast cancer patients from 26 eligible studies were included in this meta-analysis. Significant associations between elevated high cathepsin D and poor overall survival (OS) (HR = 1.61, 95% CI: 1.35-1.92, p < 0.0001) and disease-free survival (DFS) (HR = 1.52, 95% CI: 1.31-2.18, p < 0.001) were observed. In the subgroup analysis for DFS, high cathepsin D was significantly associated with poor prognosis in node-positive patients (HR = 1.38, 95% CI: 1.25-1.71, p < 0.00001), node-negative patients (HR = 1.78, 95% CI: 1.39-2.27, p < 0.0001), early stage patients (HR = 1.73, 95% CI: 1.34-2.23, p < 0.0001), and treated with chemotherapy patients (HR = 1.60, 95% CI: 1.21-2.12, p < 0.001). Interestingly, patients treated with tamoxifen had a low risk of relapse when their cathepsin D levels were high (HR = 0.71, 95% CI: 0.52-0.98, p = 0.04) and a high risk of relapse when their cathepsin D levels were low (HR = 1.50, 95% CI: 1.22-1.85, p = 0.0001). CONCLUSIONS: Our meta-analysis suggests that high expression levels of cathepsin D are associated with a poor prognosis in breast cancer. Based on our subgroup analysis, we believe that cathepsin D can act as a marker for poor breast cancer prognosis and also as a therapeutic target for breast cancer.

Risk of metastatic pheochromocytoma and paraganglioma in SDHx mutation carriers: a systematic review and updated meta-analysis.

BACKGROUND: Pheochromocytoma and paraganglioma (PPGL) are tumours that arise from chromaffin cells. Some genetic mutations influence PPGL, among which, those in genes encoding subunits of succinate dehydrogenase (SDHA, SDHB, SDHC and SDHD) and assembly factor (SDHAF2) are the most relevant. However, the risk of metastasis posed by these mutations is not reported except for SDHB and SDHD mutations. This study aimed to update the metastatic risks, considering prevalence and incidence of each SDHx mutation, which were dealt formerly all together. METHODS: We searched EMBASE and MEDLINE and selected 27 articles. The patients included in the studies were divided into three groups depending on the presence of PPGL. We checked the heterogeneity between studies and performed a meta-analysis using Hartung-Knapp-Sidik-Jonkman method based on a random effect model. RESULTS: The highest PPGL prevalence was for SDHB mutation, ranging from 23% to 31%, and for SDHC mutation (23%), followed by that for SDHA mutation (16%). The lowest prevalence was for SDHD mutation, ranging from 6% to 8%. SDHAF2 mutation showed no metastatic events. The PPGL incidence showed a tendency similar to that of its prevalence with the highest risk of metastasis posed by SDHB mutation (12%-41%) and the lowest risk by SDHD mutation (~4%). CONCLUSION: There was no integrated evidence of how SDHx mutations are related to metastatic PPGL. However, these findings suggest that SDHA, SDHB and SDHC mutations are highly associated and should be tested as indicators of metastasis in patients with PPGL.