Establishment, characterization, and genetic profiling of patient-derived osteosarcoma cells from a patient with retinoblastoma.

Osteosarcoma is the most common malignant bone cancer in pediatric patients. Patients who respond poorly to chemotherapy experience worse clinical outcomes with a high mortality rate. The major challenge is the lack of effective drugs for these patients. To introduce new drugs for clinical approval, preclinical studies based on in vitro models must demonstrate the potency of the tested drugs, enabling the drugs to enter phase 1 clinical trials. Patient-derived cell culture is a promising testing platform for in vitro studies, as they more accurately recapitulate cancer states and genetic profiles compared to cell lines. In the present study, we established patient-derived osteosarcoma cells (PDC) from a patient who had previously been diagnosed with retinoblastoma. We identified a new variant of a germline mutation in the RB1 gene in the tissue of the patient. The biological effects of this PDC were studied to observe whether the cryopreserved PDC retained a feature of fresh PDC. The cryopreserved PDC preserved the key biological effects, including cell growth, invasive capability, migration, and mineralization, that define the conserved phenotypes compared to fresh PDC. From whole genome sequencing analysis of osteosarcoma tissue and patient-derived cells, we found that cryopreserved PDC was a minor population in the origin tissue and was selectively grown under the culture conditions. The cryopreserved PDC has a high resistance to conventional chemotherapy. This study demonstrated that the established cryopreserved PDC has the aggressive characteristics of osteosarcoma, in particular the chemoresistance phenotype that might be used for further investigation in the chemoresistant mechanism of osteosarcoma. In conclusion, the approach we applied for primary cell culture might be a promising method to generate in vitro models for functional testing of osteosarcoma.

Potential target identification for osteosarcoma treatment: Gene expression re-analysis and drug repurposing.

Survival rate of osteosarcoma has remained plateaued for the past three decades. New treatment is needed to improve survival rate. Drug repurposing, a method to identify new indications of previous drugs, which saves time and cost compared to the de novo drug discovery. Data mining from gene expression profile was carried out and new potential targets were identified by using drug repurposing strategy. Selected data were newly categorized as pathophysiology and metastasis groups. Data were normalized and calculated the differential gene expression. Genes with log fold change ≥ 2 and adjusted p-value ≤ 0.05 were selected as primary candidate genes (PCGs). PCGs were further enriched to determine the secondary candidate genes (SCGs) by protein interaction analysis, upstream transcription factor and related-protein kinase identification. PCGs and SCGs were further matched with gene targeted of corresponding drugs from the Drug Repurposing Hub. A total of 778 targets were identified (360 from PCGs, and 418 from SCGs). This newly identified KLHL13 is a new candidate target based on its molecular function. KLHL13 was upregulated in clinical samples. We found 256 drugs from matching processes (50anti-cancerand206non-anticancerdrugs). Clinical trials of anti-cancer drugs from 5 targets (CDK4, BCL-2, JUN, SRC, PIK3CA) are being performed for osteosarcoma treatment. Niclosamide and synthetic PPARÉ£ ligands are candidates for repurposing due to the possibility based on their mechanism and pharmacology properties. Re-analysis of gene expression profile could identify new potential targets, confirm a current implication, and expand the chance of repurposing drugs for osteosarcoma treatment.

Integrated bioinformatic analysis of potential biomarkers of poor prognosis in triple-negative breast cancer.

Background: Triple-negative breast cancer (TNBC) is a heterogeneous disease associated with late-stage diagnosis and high metastatic rates. However, a gene signature for reliable TNBC biomarkers is not available yet. We aimed to identify potential key genes and their association with poor prognosis in TNBC through integrated bioinformatics. Methods: Microarray datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in TNBC vs. non-TNBC and TNBC vs. normal tissues were analyzed. Overlapping upregulated and downregulated DEGs were selected as inputs for Gene Ontology and pathway enrichment analyses using Metascape. Then, UALCAN and Kaplan-Meier plotter were employed to analyze the prognostic values of all overlapping DEGs. Results: We identified 21 upregulated and 24 downregulated overlapping DEGs in TNBC vs. non-TNBC and TNBC vs. normal breast tissue. The upregulated overlapping DEGs were mainly enriched in various pathways including chromosome segregation, cell cycle phase transition, and cell division, whereas overlapping DEGs were significantly downregulated in pathways, such as multicellular organismal homeostasis, tissue homeostasis, and negative regulation of cell population proliferation. Key genes were identified by association with poor overall survival (OS). Our results showed that high expression of CENPW and HORMAD1 was associated with poor OS of TNBC patients. Conversely, the low expression of PIP, APOD, and ZNF703 indicated worse OS. Conclusions: We identified key genes (CENPW, HORMAD1, APOD, PIP, and ZNF703) associated with poor OS. Thus, these genes might serve as candidate prognostic markers for TNBC.

Osteosarcoma-Specific Genes as a Diagnostic Tool and Clinical Predictor of Tumor Progression.

A liquid biopsy is currently an interesting tool for measuring tumor material with the advantage of being non-invasive. The overexpression of vimentin and ezrin genes was associated with epithelial-mesenchymal transition (EMT), a key process in metastasis and progression in osteosarcoma (OS). In this study, we identified other OS-specific genes by calculating differential gene expression using the Gene Expression Omnibus (GEO) database, confirmed by using quantitative reverse transcription-PCR (qRT-PCR) to detect OS-specific genes, including VIM and ezrin in the buffy coat, which were obtained from the whole blood of OS patients and healthy donors. Furthermore, the diagnostic model for OS detection was generated by utilizing binary logistic regression with a multivariable fractional polynomial (MFP) algorithm. The model incorporating VIM, ezrin, and COL5A2 genes exhibited outstanding discriminative ability, as determined by the receiver operating characteristic curve (AUC = 0.9805, 95% CI 0.9603, 1.000). At the probability cut-off value of 0.3366, the sensitivity and the specificity of the model for detecting OS were 98.63% (95% CI 90.5, 99.7) and 94.94% (95% CI 87.5, 98.6), respectively. Bioinformatic analysis and qRT-PCR, in our study, identified three candidate genes that are potential diagnostic and prognostic genes for OS.

Molecular Classification Models for Triple Negative Breast Cancer Subtype Using Machine Learning.

Triple negative breast cancer (TNBC) lacks well-defined molecular targets and is highly heterogenous, making treatment challenging. Using gene expression analysis, TNBC has been classified into four different subtypes: basal-like immune-activated (BLIA), basal-like immune-suppressed (BLIS), mesenchymal (MES), and luminal androgen receptor (LAR). However, there is currently no standardized method for classifying TNBC subtypes. We attempted to define a gene signature for each subtype, and to develop a classification method based on machine learning (ML) for TNBC subtyping. In these experiments, gene expression microarray data for TNBC patients were downloaded from the Gene Expression Omnibus database. Differentially expressed genes unique to 198 known TNBC cases were identified and selected as a training gene set to train in seven different classification models. We produced a training set consisting of 719 DEGs selected from uniquely expressed genes of all four subtypes. The highest average accuracy of classification of the BLIA, BLIS, MES, and LAR subtypes was achieved by the SVM algorithm (accuracy 95-98.8%; AUC 0.99-1.00). For model validation, we used 334 samples of unknown TNBC subtypes, of which 97 (29.04%), 73 (21.86%), 39 (11.68%) and 59 (17.66%) were predicted to be BLIA, BLIS, MES, and LAR, respectively. However, 66 TNBC samples (19.76%) could not be assigned to any subtype. These samples contained only three upregulated genes (EN1, PROM1, and CCL2). Each TNBC subtype had a unique gene expression pattern, which was confirmed by identification of DEGs and pathway analysis. These results indicated that our training gene set was suitable for development of classification models, and that the SVM algorithm could classify TNBC into four unique subtypes. Accurate and consistent classification of the TNBC subtypes is essential for personalized treatment and prognosis of TNBC.