Plasma metabolomics are powerful for characterizing metabolic disturbances. Differences in small-molecule metabolites may reflect underlying CAD and serve as biomarkers for CAD progression.

A patient’s response to immune checkpoint inhibitors (ICIs) is a complex quantitative trait, and determined by multiple intrinsic and extrinsic factors. Three currently FDA-approved predictive biomarkers (progra1mmed cell death ligand-1 (PD-L1); microsatellite instability (MSI); tumor mutational burden (TMB)) are routinely used for patient selection for ICI response in clinical practice. Although clinical utility of these biomarkers has been demonstrated in ample clinical trials, many variables involved in using these biomarkers have poised serious challenges in daily practice. Furthermore, the predicted responders by these three biomarkers only have a small percentage of overlap, suggesting that each biomarker captures different contributing factors to ICI response. Optimized use of currently FDA-approved biomarkers and development of a new generation of predictive biomarkers are urgently needed. In this review, we will first discuss three widely used FDA-approved predictive biomarkers and their optimal use. Secondly, we will review four novel gene signature biomarkers: T-cell inflamed gene expression profile (GEP), T-cell dysfunction and exclusion gene signature (TIDE), melanocytic plasticity signature (MPS) and B-cell focused gene signature. The GEP and TIDE have shown better predictive performance than PD-L1, and PD-L1 or TMB, respectively. The MPS is superior to PD-L1, TMB, and TIDE. The B-cell focused gene signature represents a previously unexplored predictive biomarker to ICI response. Thirdly, we will highlight two combined predictive biomarkers: TMB+GEP and MPS+TIDE. These integrated biomarkers showed improved predictive outcomes compared to a single predictor. Finally, we will present a potential nucleic acid biomarker signature, allowing DNA and RNA biomarkers to be analyzed in one assay. This comprehensive signature could represent a future direction of developing robust predictive biomarkers, particularly for the cold tumors, for ICI response.

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Purpose: This work aims to identify differential metabolites and predicting molecular subtypes of breast cancer (BC).Methods: Plasma samples were collected from 96 BC patients and 79 normal participants. Metabolic profiles were determined by liquid chromatography-mass spectrometry and gas chromatography-mass spectrometry based on multivariate statistical data analysis.Results Conclusion: Human plasma metabolomics is useful in identifying differential metabolites and predicting breast cancer subtypes.

Transforming growth factor-β1 (TGF-β1) plays an important role on fibrogenesis in heart disease. MicroRNAs have exhibited as crucial regulators of cardiac homeostasis and remodeling in various heart diseases. MiR-19a-3p/19b-3p expresses with low levels in the plasma of heart failure patients. The purpose of our study is to determine the role of MiR-19a-3p/19b-3p in regulating autophagy-mediated fibrosis of human cardiac fibroblasts. We elucidate our hypothesis in clinical samples and human cardiac fibroblasts (HCF) to provide valuable basic information. TGF-β1 promotes collagen I α2 and fibronectin synthesis in HCF and that is paralleled by autophagic activation in these cells. Pharmacological inhibition of autophagy by 3-methyladenine decreases the fibrotic response, while autophagy induction of rapamycin increases the response. BECN1 knockdown and Atg5 over-expression either inhibits or enhances the fibrotic effect of TGF-β1 in experimental HCF. Furthermore, miR-19a-3p/19b-3p mimics inhibit epithelial mesenchymal transition (EMT) and extracellular matrix (ECM) prodution and invasion of HCF. Functional studies suggest that miR-19a-3p/19b-3p inhibits autophagy of HCF through targeting TGF-β R II mRNA. Moreover, enhancement of autophagy rescues inhibition effect of miR-19a-3p/19b-3p on Smad 2 and Akt phosphorylation through TGF-β R II signaling. Our study uncovers a novel mechanism that miR-19a-3p/19b-3p inhibits autophagy-mediated fibrogenesis by targeting TGF-β R II.

MicroRNAs (miRs) are small, non-protein coding transcripts involved in many cellular functions. Many miRs have emerged as important cancer biomarkers. In the present study, we investigated whether miR levels in breast tumors are predictive of breast cancer local recurrence (LR). Sixty-eight women who were diagnosed with breast cancer at the Lombardi Comprehensive Cancer Center were included in this study. Breast cancer patients with LR and those without LR were matched on year of surgery, age at diagnosis, and type of surgery. Candidate miRs were identified by screening the expression levels of 754 human miRs using miR arrays in 16 breast tumor samples from 8 cases with LR and 8 cases without LR. Eight candidate miRs that showed significant differences between tumors with and without LR were further verified in 52 tumor samples using real-time PCR. Higher expression of miR-9 was significantly associated with breast cancer LR in all cases as well as the subset of estrogen receptor (ER) positive cases (p = 0.02). The AUCs (Area Under Curve) of receiver operating characteristic (ROC) curves of miR-9 for all tumors and ER positive tumors are 0.68 (p = 0.02) and 0.69 (p = 0.02), respectively. In ER positive cases, Kaplan-Meier analysis showed that patients with lower miR-9 levels had significantly better 10-year LR-free survival (67.9% vs 30.8%, p = 0.02). Expression levels of miR-9 and another miR candidate, miR-375, were also strongly associated with ER status (p<0.001 for both). The potential of miR-9 as a biomarker for LR warrants further investigation with larger sample size.

Osteosarcoma (OS) is the most common primary tumor of bone. MicroRNAs (miRNAs) are a class of endogenously expressed small non-coding RNAs that are strongly implicated in cancerous processes. However, our current understanding of the biological role of miRNAs in OS remains incomplete. In the present study, miR-144 was markedly downregulated in OS cell lines and clinical specimens. Low-level expression of miR-144 was significantly associated with distant metastasis and poor prognosis. Functional studies demonstrated that ectopic expression of miR-144 suppresses tumor cell proliferation and metastasis in vitro as well as in vivo. Furthermore, we identified Rho-associated kinases 1 and 2 (ROCK1 and ROCK2) as direct targets for miR-144 binding, resulting in suppression of their expression. Exogenous expression of ROCK1 or ROCK2 in 143B-miR-144 cells partially restored miR-144-inhibited cell proliferation and invasion. In clinical OS specimens, ROCK1 and ROCK2 levels were elevated, relative to that in paired normal bone tissues, and inversely correlated with miR-144 expression. Taken together, miR-144 suppresses OS progression by directly downregulating ROCK1 and ROCK2 expression, and may be a promising therapeutic target for OS.

RNA sequencing (RNAseq) is one of the most commonly used techniques in life sciences, and has been widely used in cancer research, drug development, and cancer diagnosis and prognosis. Driven by various biological and technical questions, the techniques of RNAseq have progressed rapidly from bulk RNAseq, laser-captured micro-dissected RNAseq, and single-cell RNAseq to digital spatial RNA profiling, spatial transcriptomics, and direct in situ sequencing. These different technologies have their unique strengths, weaknesses, and suitable applications in the field of clinical oncology. To guide cancer researchers to select the most appropriate RNAseq technique for their biological questions, we will discuss each of these technologies, technical features, and clinical applications in cancer. We will help cancer researchers to understand the key differences of these RNAseq technologies and their optimal applications.