COL2A1 Is a Novel Biomarker of Melanoma Tumor Repopulating Cells

Hybrid AI models allow label-free identification and classification of pancreatic tumor repopulating cell population

Human pancreatic cancer cell lines harbor a small population of tumor repopulating cells (TRCs). Soft 3D fibrin gel allows efficient selection and growth of these tumorigenic TRCs. However, rapid and high-throughput identification and classification of pancreatic TRCs remain technically challenging. Here, we developed deep learning (DL) models paired with machine learning (ML) models to readily identify and classify 3D fibrin gel-selected TRCs into sub-types. Using four different human pancreatic cell lines, namely, MIA PaCa-2, PANC-1, CFPAC-1, and HPAF-II, we classified 3 main sub-types to be present within the TRC population. Our best model was an Inception-v3 convolutional neural network (CNN) used as a feature extractor paired with a Support Vector Machine (SVM) classifier with radial basis function (rbf) kernel which obtained a test accuracy of 90%. In addition, we compared this hybrid method of supervised classification with other methods of supervised classifications and showed that our working model outperforms others. With the help of unsupervised machine learning algorithms, we also validated that the pancreatic TRC subpopulation can be clustered into 3 sub-types. Collectively, our robust model can detect and readily classify tumorigenic TRC subpopulation label-free in a high-throughput fashion which can be very beneficial in clinical settings.

Defining a Unique Gene Expression Profile in Mature and Developing Keloids

Keloids are benign, fibroproliferative dermal tumors that typically form owing to abnormal wound healing. The current standard of care is generally ineffective and does not prevent recurrence. To characterize keloid scars and better understand the mechanism of their formation, we performed transcriptomic profiling of keloid biopsies from a total of 25 subjects of diverse racial and ethnic origins, 15 of whom provided a paired nonlesional sample, a longitudinal sample, or both. The transcriptomic signature of nonlesional skin biopsies from subjects with keloids resembled that of control skin at baseline but shifted to closely match that of keloid skin after dermal trauma. Peripheral keloid skin and rebiopsied surrounding normal skin both showed upregulation of epithelial-mesenchymal transition markers, extracellular matrix organization, and collagen genes. These keloid signatures strongly overlapped those from healthy wound healing studies, usually with greater perturbations, reinforcing our understanding of keloids as dysregulated and exuberant wound healing. In addition, 219 genes uniquely regulated in keloids but not in normal injured or uninjured skin were also identified. This study provides insights into mature and developing keloid signatures that can act as a basis for further validation and target identification in the search for transformative keloid treatments.

SFRP2 Overexpression Induces an Osteoblast-like Phenotype in Prostate Cancer Cells

Prostate cancer bone metastasis is still one of the most fatal cancer diagnoses for men. Survival of the circulating prostate tumor cells and their adaptation strategy to survive in the bone niche is the key point to determining metastasis in early cancer stages. The promoter of SFRP2 gene, encoding a WNT signaling modulator, is hypermethylated in many cancer types including prostate cancer. Moreover, SFRP2 can positively regulate osteogenic differentiation in vitro and in vivo. Here, we showed SFRP2 overexpression in the prostate cancer cell line PC3 induces an epithelial mesenchymal transition (EMT), increases the attachment, and modifies the transcriptome towards an osteoblast-like phenotype (osteomimicry) in a collagen 1-dependent manner. Our data reflect a novel molecular mechanism concerning how metastasizing prostate cancer cells might increase their chance to survive within bone tissue.

Transcription profiling of feline mammary carcinomas and derived cell lines reveals biomarkers and drug targets associated with metabolic and cell cycle pathways

The molecular heterogeneity of feline mammary carcinomas (FMCs) represents a prognostic and therapeutic challenge. RNA-Seq-based comparative transcriptomic profiling serves to identify recurrent and exclusive differentially expressed genes (DEGs) across sample types and molecular subtypes. Using mass-parallel RNA-Seq, we identified DEGs and performed comparative function-based analysis across 15 tumours (four basal-like triple-negative [TN], eight normal-like TN, and three luminal B fHER2 negative [LB fHER2-]), two cell lines (CL, TiHo-0906, and TiHo-1403) isolated from the primary tumours (LB fHER2-) of two cats included in this study, and 13 healthy mammary tissue controls. DEGs in tumours were predominantly upregulated; dysregulation of CLs transcriptome was more extensive, including mostly downregulated genes. Cell-cycle and metabolic-related DEGs were upregulated in both tumours and CLs, including therapeutically-targetable cell cycle regulators (e.g. CCNB1, CCNB2, CDK1, CDK4, GTSE1, MCM4, and MCM5), metabolic-related genes (e.g. FADS2 and SLC16A3), heat-shock proteins (e.g. HSPH1, HSP90B1, and HSPA5), genes controlling centrosome disjunction (e.g. RACGAP1 and NEK2), and collagen molecules (e.g. COL2A1). DEGs specifically upregulated in basal-like TN tumours were involved in antigen processing and presentation, in normal-like TN tumours encoded G protein-coupled receptors (GPCRs), and in LB fHER2- tumours were associated with lysosomes, phagosomes, and endosomes formation. Downregulated DEGs in CLs were associated with structural and signalling cell surface components. Hence, our results suggest that upregulation of genes enhancing proliferation and metabolism is a common feature among FMCs and derived CLs. In contrast, the dissimilarities observed in dysregulation of membrane components highlight CLs' disconnection with the tumour microenvironment. Furthermore, recurrent and exclusive DEGs associated with dysregulated pathways might be useful for the development of prognostically and therapeutically-relevant targeted panels.

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

Simple Summary

The standard method for the diagnosis and monitoring of osteosarcoma is biopsy and tumor imaging, which causes discomfort to patients and is difficult to repeat. A blood sample can be used as a non-invasive method for monitoring tumor material. Vimentin and ezrin show clinical significance in samples obtained from OS patients but need circulating tumor cell purification, since they are expressed in leukocytes. Due to the low-temperature storage of the samples, it proved impossible to perform purification to remove the contamination. We propose that novel or OS-specific biomarkers using differential gene expression from the Gene Expression Omnibus (GEO) database is a promising approach for developing diagnostic and tumor progression strategies. Seven genes from the database showed significant expression in OS cell lines/primary cells compared to a normal blood donor, together with ezrin and VIM. The expression of the five candidate genes together with ezrin and vimentin were quantified by qRT-PCR and analyzed using a mathematical model with high efficiency to discriminate between OS patients and normal samples, resulting in the selection of three candidate genes: COL5A2 (one of the five from the database) as well as ezrin and VIM. Our study demonstrates that these genes in retrospective samples could serve as tools of OS detection and predictors of disease progression.

Abstract

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.

Identification of a novel glycolysis-related gene signature for predicting the survival of patients with colon adenocarcinoma

BACKGROUND

The most frequent histologic subtype of colon cancer is colon adenocarcinoma (COAD). A major problem in the diagnosis and treatment of COAD is that there is lack of new biomarkers to indicate the early stage of COAD. Compared with normally differentiated cells, the glycolytic pathways of tumor cells are more active, thus making them more adaptable to the hypoxic environment of solid tumors, which is known as the Warburg effect. Therefore, establishing a diagnostic and prognostic model based on glycolysis-related genes may provide guidance for the precise treatment of colon cancer.

METHODS

The Cancer Genome Atlas (TCGA) mRNA data were used to identify differentially expressed genes (DEGs). The glycolysis-related DEGs were identified using Gene Set Enrichment Analysis (GSEA) with HALLMARK gene sets. Combined with clinical data, we identified prognostic genes in glycolysis-related DEGs based on Cox regression analysis. Four glycolysis-related genes were identified and a predictive model was developed using univariate and multivariate Cox regression analysis. cBioPortal investigated the chromosomal variations of these genes. Following that, survival analysis and receiver operating characteristic (ROC) curve validation were carried out. The correlations between glycolysis-related gene signatures and molecular features and cancer subtypes were analyzed.

RESULTS

We discovered five genes (SPAG4, P4HA1, STC2, ENO3, and GPC1) that are associated with COAD patients' prognosis. The risk score was more accurate in predicting prognosis when based on this gene signature in COAD patients. Furthermore, multivariate Cox regression analysis demonstrated that the glycolysis-related gene signature's predictive value was independent of clinical variables.

CONCLUSION

We identified a glycolysis-related five-gene signature and developed a risk staging model potentially valuable for the clinical management of COAD patients. Our results suggest that prognostic markers based on glycolysis-related genes may be a reliable predictive tool for the prognosis of COAD patients.

Prognostic immunologic signatures in epithelial ovarian cancer

Epithelial Ovarian Cancer (EOC) is a deadly gynecologic malignancy in which patients frequently develop recurrent disease following initial platinum-taxane chemotherapy. Analogous to many other cancer subtypes, EOC clinical trials have centered upon immunotherapeutic approaches, most notably programmed cell death 1 (PD-1) inhibitors. While response rates to these immunotherapies in EOC patients have been low, evidence suggests that ovarian tumors are immunogenic and that immune-related genomic profiles can serve as prognostic markers. This review will discuss recent advances in the development of immune-based prognostic signatures in EOC that predict patient clinical outcomes, as well as emphasize specific research areas that need to be addressed to drive this field forward.

Cytoskeletal prestress: The cellular hallmark in mechanobiology and mechanomedicine

Increasing evidence demonstrates that mechanical forces, in addition to soluble molecules, impact cell and tissue functions in physiology and diseases. How living cells integrate mechanical signals to perform appropriate biological functions is an area of intense investigation. Here, we review the evidence of the central role of cytoskeletal prestress in mechanotransduction and mechanobiology. Elevating cytoskeletal prestress increases cell stiffness and reinforces cell stiffening, facilitates long-range cytoplasmic mechanotransduction via integrins, enables direct chromatin stretching and rapid gene expression, spurs embryonic development and stem cell differentiation, and boosts immune cell activation and killing of tumor cells whereas lowering cytoskeletal prestress maintains embryonic stem cell pluripotency, promotes tumorigenesis and metastasis of stem cell-like malignant tumor-repopulating cells, and elevates drug delivery efficiency of soft-tumor-cell-derived microparticles. The overwhelming evidence suggests that the cytoskeletal prestress is the governing principle and the cellular hallmark in mechanobiology. The application of mechanobiology to medicine (mechanomedicine) is rapidly emerging and may help advance human health and improve diagnostics, treatment, and therapeutics of diseases.