Notes

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This study aimed to create a risk score generated from CT-based radiomics signatures that could be used to predict overall survival in patients with non-small cell lung cancer (NSCLC). We retrospectively enrolled three sets of NSCLC patients (including 336, 84, and 157 patients for training, testing, and validation set, respectively). A total of 851 radiomics features for each patient from CT images were extracted for further analyses. The most important features (strongly linked with overall survival) were chosen by pairwise correlation analysis, Least Absolute Shrinkage and Selection Operator (LASSO) regression model, and univariate Cox proportional hazard regression. Multivariate Cox proportional hazard model survival analysis was used to create risk scores for each patient, and Kaplan-Meier was used to separate patients into two groups high-risk and low-risk, respectively. ROC curve assessed the prediction ability of the risk score model for overall survival compared to clinical parameters. The risk scoren set. In conclusion, risk scores developed from ten radiomics signatures models have great potential to predict overall survival in NSCLC patients compared to the clinical parameters. This model was able to stratify NSCLC patients into high-risk and low-risk groups regarding the overall survival prediction.Metabolic disorders in children after hematopoietic stem cell transplantation (HSCT) are poorly characterized. However, it is known that dyslipidemia and insulin resistance are particularly common in these patients. We conducted a prospective study of 27 patients treated with HSCT to assess the possibility of predicting these abnormalities. We measured gene expressions using a microarray technique to identify differences in expression of genes associated with lipid metabolism before and after HSCT. In patients treated with HSCT, total cholesterol levels were significantly higher after the procedure compared with the values before HSCT. Microarray analysis revealed statistically significant differences in expressions of three genes, DPP4, PLAG1, and SCD, after applying the Benjamini-Hochberg procedure (pBH less then 0.05). In multiple logistic regression, the increase of DPP4 gene expression before HCST (as well as its change between pre- and post-HSCT status) was associated with dyslipidemia. In children treated with HSCT, the burden of lipid disorders in short-term follow-up seems to be lower than before the procedure. The expression pattern of DPP4 is linked with dyslipidemia after the transplantation.Luminal breast cancer (LBC) driven by dysregulated estrogen receptor-alpha (ERα) signaling accounts for 70% of the breast cancer cases diagnosed. Although endocrine therapy (ET) is effective against LBC, about one-third of these patients fail to respond to therapy owing to acquired or inherent resistance mechanisms. Aberrant signaling via ERα, oncogenes, growth factor receptors, and mutations in tumor suppressors such as p53 impinge on downstream regulators such as AMPK and mTOR. While both AMPK and mTOR have been reported to play important roles in determining sensitivity of LBC to ET, how the ERα-p53 crosstalk impinges on regulation of AMPK and mTOR, thereby influencing therapeutic efficacy remains unknown. Here, we have addressed this important issue using isogenic breast cancer cell lines, siRNA-mediated RNA knockdown, and different modes of drug treatments. Interaction of p53 with ERα and AMPK was determined by in situ proximity ligation assay (PLA), and endogenous gene transcripts were analyzed by quantitative real-time polymerase chain reaction (qRT-PCR). Further, the effect of concurrent and sequential administration of Fulvestrant-Everolimus combination on colony formation was determined. The studies showed that in cells expressing wild type p53, as well as in cells devoid of p53, ERα represses AMPK, whereas in cells harboring mutant p53, repression of AMPK is sustained even in the absence of ERα. AMPK is a major negative regulator of mTOR, and to our knowledge, this is the first study on the contribution of AMPK-dependent regulation of mTOR by ERα. Furthermore, the studies revealed that independent of the p53 mutation status, combination of Fulvestrant and Everolimus may be a viable first line therapeutic strategy for potentially delaying resistance of ERα+/HER2- LBC to ET.In the present study, the role of a novel protein involved in neurite development and endoplasmic reticulum (ER) stress, canopy homolog 2 (CNPY2), was investigated in mouse and human hepatocarcinogenesis. Firstly, a sensitive quantitative and qualitative detection of protein expression using QSTAR Elite LC-Ms/Ms was performed for the analysis of lysates of microdissected hepatocellular altered foci (AF), adenomas (HCAs), carcinomas (HCCs) and peri-tumoral livers from C57Bl/6J mice treated with diethylnitrosamine (DEN) and then maintained for 27 or 38 weeks on basal diet. Significant overexpression of 18.5 kDa CNPY2 processed form was demonstrated in AF, HCAs and HCCs, while low expression was observed in the livers of DEN-treated and control mice. Furthermore, CNPY2 elevation in AF and tumors was coordinated with accumulation of numerous cytoskeletal proteins, including cytokeratins 8 and 18, actin, non-muscle myosin and septin 9 and those involved in ER and mitochondrial stresses such as calreticulin, prohibproliferation, survival and invasion of liver tumor cells.Radiogenomics use non-invasively obtained imaging data, such as magnetic resonance imaging (MRI), to predict critical biomarkers of patients. Developing an accurate machine learning (ML) technique for MRI requires data from hundreds of patients, which cannot be gathered from any single local hospital. Hence, a model universally applicable to multiple cohorts/hospitals is required. see more We applied various ML and image pre-processing procedures on a glioma dataset from The Cancer Image Archive (TCIA, n = 159). The models that showed a high level of accuracy in predicting glioblastoma or WHO Grade II and III glioma using the TCIA dataset, were then tested for the data from the National Cancer Center Hospital, Japan (NCC, n = 166) whether they could maintain similar levels of high accuracy. Results we confirmed that our ML procedure achieved a level of accuracy (AUROC = 0.904) comparable to that shown previously by the deep-learning methods using TCIA. However, when we directly applied the model to the NCC dataset, its AUROC dropped to 0.383. Introduction of standardization and dimension reduction procedures before classification without re-training improved the prediction accuracy obtained using NCC (0.804) without a loss in prediction accuracy for the TCIA dataset. Furthermore, we confirmed the same tendency in a model for IDH1/2 mutation prediction with standardization and application of dimension reduction that was also applicable to multiple hospitals. Our results demonstrated that overfitting may occur when an ML method providing the highest accuracy in a small training dataset is used for different heterogeneous data sets, and suggested a promising process for developing an ML method applicable to multiple cohorts.
In recent decades, gold nanoparticle (Au NP)-based cancer therapy has been heavily debated. The physico-chemical properties of AuNPs can be exploited in photothermal therapy, making them a powerful tool for selectively killing cancer cells. However, the synthetic side products and capping agents often induce a strong activation of the inflammatory pathways of macrophages, thus limiting their further applications in vivo.

Here, we described a green method to obtain stable polyphenol-capped AuNPs (Au NPs@polyphenols), as polyphenols are known for their anti-inflammatory and anticancer properties. These NPs were used in human macrophages to test key inflammation-related markers, such as NF-κB, TNF-α, and interleukins-6 and 8. The results were compared with similar NPs obtained by a traditional chemical route (without the polyphenol coating), proving the potential of Au NPs@polyphenols to strongly promote the shutdown of inflammation. This was useful in developing them for use as heat-synergized tools in the thermal treatment of two types of cancer cells, namely, breast cancer (MCF-7) and neuroblastoma (SH-SY5Y) cells. The cell viability, calcium release, oxidative stress, HSP-70 expression, mitochondrial, and DNA damage, as well as cytoskeleton alteration, were evaluated.

Our results clearly demonstrate that the combined strategy markedly exerts anticancer effects against the tested cancer cell, while neither of the single treatments (only heat or only NPs) induced significant changes.

Au NP@polyphenols may be powerful agents in cancer treatment.
Au NP@polyphenols may be powerful agents in cancer treatment.Tumor or target heterogeneity (TH) implies presence of variable cellular populations having different genomic characteristics within the same tumor, or in different tumor sites of the same patient. The challenge is to identify this heterogeneity, as it has emerged as the most common cause of 'treatment resistance', to current therapeutic agents. We have focused our discussion on 'Prostate Cancer' and 'Neuroendocrine Tumors', and looked at the established methods for demonstrating heterogeneity, each with its advantages and drawbacks. Also, the available theranostic radiotracers targeting PSMA and somatostatin receptors combined with targeted systemic agents, have been described. Lu-177 labeled PSMA and DOTATATE are the 'standard of care' radionuclide therapeutic tracers for management of progressive treatment-resistant prostate cancer and NET. These approved therapies have shown reasonable benefit in treatment outcome, with improvement in quality of life parameters. Various biomarkers and predictors of response to radionuclide therapies targeting TH which are currently available and those which can be explored have been elaborated in details. Imaging-based features using artificial intelligence (AI) need to be developed to further predict the presence of TH. Also, novel theranostic tools binding to newer targets on surface of cancer cell should be explored to overcome the treatment resistance to current treatment regimens.Glyoxalase 1 (GLO1) is an enzyme involved in the detoxification of methylglyoxal (MG), a reactive oncometabolite formed in the context of energy metabolism as a result of high glycolytic flux. Prior clinical evidence has documented GLO1 upregulation in various tumor types including prostate cancer (PCa). However, GLO1 expression has not been explored in the context of PCa progression with a focus on high-grade prostatic intraepithelial neoplasia (HGPIN), a frequent precursor to invasive cancer. Here, we have evaluated GLO1 expression by immunohistochemistry in archival tumor samples from 187 PCa patients (stage 2 and 3). Immunohistochemical analysis revealed GLO1 upregulation during tumor progression, observable in HGPIN and PCa versus normal prostatic tissue. GLO1 upregulation was identified as a novel hallmark of HGPIN lesions, displaying the highest staining intensity in all clinical patient specimens. GLO1 expression correlated with intermediate-high risk Gleason grade but not with patient age, biochemical recurrence, or pathological stage.
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