PPARgamma agonism inhibits progression of premalignant lesions in a murine lung squamous cell carcinoma model

Analysis of nuclear receptor expression in head and neck cancer

OBJECTIVE

Studies of squamous cell carcinoma of the head and neck (HNSCC) have demonstrated the importance of nuclear receptors and their associated coregulators in the development and treatment of HNSCC. We sought to characterize members of the nuclear receptor super family through interrogation of RNA-Seq and microarray data.

MATERIALS AND METHODS

TCGA RNA-Seq data within the cBioportal platform comparing HNSCC samples (n = 515 patients with RNA-Seq data) to normal tissue (n = 82 patients) was interrogated for significant differences in nuclear receptor expression. Affymetrix microarray analysis of HNSCC tumors relative to normal oral mucosa (41 tumor, 13 normal) was analyzed.

RESULTS

Of the 48 NR genes and 19 NR cofactors examined, 99 % of tumor samples in the TCGA had some form of NR gene 'alteration' compared to normal tissue. These alterations predominantly encompass expression changes. NR genes (PPARG) and (RORC), and the NR cofactor, (NCOA1), were differentially expressed and downregulated in tumors compared to normal tissue.

CONCLUSION

We have discovered significant decreases in PPARG expression with co-occurring changes in genes involved with lipid metabolism and cell cycle progression in HNSCC. We are targeting PPARγ with thiazolidinediones in a series of clinical trials to restore normal signaling via differentiation to hopefully reverse carcinogenesis. We also observed several receptors with differential expression associated with clinical factors that may become the focus of interest in future targeting efforts. These data provide evidence for nuclear receptors playing a role in the dysregulation of gene expression in HNSCC and illustrate the utility of current bioinformatic tools for interrogating complex, high throughput data sets.

Lung cancer progression alters lung and gut microbiomes and lipid metabolism

Despite advances in medical technology, lung cancer still has one of the highest mortality rates among all malignancies. Therefore, efforts must be made to understand the precise mechanisms underlying lung cancer development. In this study, we conducted lung and gut microbiome analyses and a comprehensive lipid metabolome analysis of host tissues to assess their correlation. Alternations in the lung microbiome due to lung cancer, such as a significantly decreased abundance of Firmicutes and Deferribacterota, were observed compared to a mock group. However, mice with lung cancer had significantly lower relative abundances of Actinobacteria and Proteobacteria and higher relative abundances of Cyanobacteria and Patescibacteria in the gut microbiome. The activations of retinol, fatty acid metabolism, and linoleic acid metabolism metabolic pathways in the lung and gut microbiomes was inversely correlated. Additionally, changes occurred in lipid metabolites not only in the lungs but also in the blood, small intestine, and colon. Compared to the mock group, mice with lung cancer showed that the levels of adrenic, palmitic, stearic, and oleic (a ω-9 polyunsaturated fatty acid) acids increased in the lungs. Conversely, these metabolites consistently decreased in the blood (serum) and colon. Leukotriene B4 and prostaglandin E2 exacerbate lung cancer, and were upregulated in the lungs of the mice with lung cancer. However, isohumulone, a peroxisome proliferator-activated receptor gamma activator, and resolvin (an ω-3 polyunsaturated fatty acid) both have anti-cancer effects, and were upregulated in the small intestine and colon. Our multi-omics data revealed that shifts in the microbiome and metabolome occur during the development of lung cancer and are of possible clinical importance. These results reveal one of the gut-lung axis mechanisms related to lung cancer and provide insights into potential new targets for lung cancer treatment and prophylaxis.

From slides to insights: Harnessing deep learning for prognostic survival prediction in human colorectal cancer histology

Prognostic survival prediction in colorectal cancer (CRC) plays a crucial role in guiding treatment decisions and improving patient outcomes. In this research, we explore the application of deep learning techniques to predict survival outcomes based on histopathological images of human colorectal cancer. We present a retrospective multicenter study utilizing a dataset of 100,000 nonoverlapping image patches from hematoxylin & eosin-stained histological images of CRC and normal tissue. The dataset includes diverse tissue classes such as adipose, background, debris, lymphocytes, mucus, smooth muscle, normal colon mucosa, cancer-associated stroma, and colorectal adenocarcinoma epithelium. To perform survival prediction, we employ various deep learning architectures, including convolutional neural network, DenseNet201, InceptionResNetV2, VGG16, VGG19, and Xception. These architectures are trained on the dataset using a multicenter retrospective analysis approach. Extensive preprocessing steps are undertaken, including image normalization using Macenko's method and data augmentation techniques, to optimize model performance. The experimental findings reveal promising results, demonstrating the effectiveness of deep learning models in prognostic survival prediction. Our models achieve high accuracy, precision, recall, and validation metrics, showcasing their ability to capture relevant histological patterns associated with prognosis. Visualization techniques are employed to interpret the models' decision-making process, highlighting important features and regions contributing to survival predictions. The implications of this research are manifold. The accurate prediction of survival outcomes in CRC can aid in personalized medicine and clinical decision-making, facilitating tailored treatment plans for individual patients. The identification of important histological features and biomarkers provides valuable insights into disease mechanisms and may lead to the discovery of novel prognostic indicators. The transparency and explainability of the models enhance trust and acceptance, fostering their integration into clinical practice. Research demonstrates the potential of deep learning models for prognostic survival prediction in human colorectal cancer histology. The findings contribute to the understanding of disease progression and offer practical applications in personalized medicine. By harnessing the power of deep learning and histopathological analysis, we pave the way for improved patient care, clinical decision support, and advancements in prognostic prediction in CRC.

TRIM29 knockdown prevented the colon cancer progression through decreasing the ubiquitination levels of KRT5

To investigate the specific role of TRIM29 in colon cancer progression, bioinformatic analysis was performed on TRIM29. Colon cancer tissues were collected and colon cancer cells were cultured for further experiments. Cell viability and proliferation were determined using CCK-8, colony formation, and EDU staining assays. The mRNA and protein levels of TRIM29 and KRT5 were determined using quantitative real-time PCR and western blotting, respectively. The interaction between TRIM29 and KRT5 was detected using a co-immunoprecipitation (CO-IP) assay. Cycloheximide treatment was performed to analyse the stability of KRT5. TRIM29 was upregulated in colon cancer tissues and cells. TRIM29 knockdown decreased the cell viability and proliferation and ubiquitination levels of KRT5 and enhanced the protein stability and expression of KRT5. The CO-IP assay confirmed that TRIM29 and KRT5 binded to each other. KRT5 knockdown neutralises the inhibitory effect of sh-TRIM29 on colon cancer cell growth and TRIM29 knockdown prevented the proliferation of colon cancer cells by decreasing ubiquitination of KRT5, which enhanced the protein stability and expression of KRT5 in cancer cells. Thus, targeting TRIM29-mediated ubiquitination levels of KRT5 might be a new direction for colon cancer therapy.

Repurposing of Chronically Used Drugs in Cancer Therapy: A Chance to Grasp

Despite the advancement in drug discovery for cancer therapy, drug repurposing remains an exceptional opportunistic strategy. This approach offers many advantages (faster, safer, and cheaper drugs) typically needed to overcome increased challenges, i.e., side effects, resistance, and costs associated with cancer therapy. However, not all drug classes suit a patient's condition or long-time use. For that, repurposing chronically used medications is more appealing. This review highlights the importance of repurposing anti-diabetic and anti-hypertensive drugs in the global fight against human malignancies. Extensive searches of all available evidence (up to 30 March 2023) on the anti-cancer activities of anti-diabetic and anti-hypertensive agents are obtained from multiple resources (PubMed, Google Scholar, ClinicalTrials.gov, Drug Bank database, ReDo database, and the National Institutes of Health). Interestingly, more than 92 clinical trials are evaluating the anti-cancer activity of 14 anti-diabetic and anti-hypertensive drugs against more than 15 cancer types. Moreover, some of these agents have reached Phase IV evaluations, suggesting promising official release as anti-cancer medications. This comprehensive review provides current updates on different anti-diabetic and anti-hypertensive classes possessing anti-cancer activities with the available evidence about their mechanism(s) and stage of development and evaluation. Hence, it serves researchers and clinicians interested in anti-cancer drug discovery and cancer management.

Precision Cut Lung Slices as a Preclinical Model for Non-Small Cell Lung Cancer Chemoprevention

Lung cancer chemoprevention is critical to addressing cancer burden in high-risk populations. Chemoprevention clinical trials rely on data from preclinical models; however, in vivo studies have high financial, technical, and staffing requirements. Precision cut lung slices (PCLS) provide an ex vivo model that maintains the structure and function of native tissues. This model can be used for mechanistic investigations and drug screenings and reduces the number of animals and time required to test hypotheses compared with in vivo studies. We tested the use of PCLS for chemoprevention studies, demonstrating recapitulation of in vivo models. Treatment of PCLS with the PPARγ agonizing chemoprevention agent iloprost produced similar effects on gene expression and downstream signaling as in vivo models. This occurred in both wild-type tissue and Frizzled 9 knockout tissue, a transmembrane receptor required for iloprost's preventive activity. We explored new areas of iloprost mechanisms by measuring immune and inflammation markers in PCLS tissue and media, and immune cell presence with immunofluorescence. To demonstrate the potential for drug screening, we treated PCLS with additional lung cancer chemoprevention agents and confirmed activity markers in culture. PCLS offers an intermediate step for chemoprevention research between in vitro and in vivo models that can facilitate drug screening prior to in vivo studies and support mechanistic studies with more relevant tissue environments and functions than in vitro models.

PREVENTION RELEVANCE

PCLS could be a new model for premalignancy and chemoprevention research, and this work evaluates the model with tissue from prevention-relevant genetic and carcinogen exposed in vivo mouse models, in addition to evaluating chemoprevention agents.

The Activation of PPARγ by (2Z,4E,6E)-2-methoxyocta-2,4,6-trienoic Acid Counteracts the Epithelial–Mesenchymal Transition Process in Skin Carcinogenesis

Cutaneous squamous cell carcinoma (cSCC) is the most common UV-induced keratinocyte-derived cancer, and its progression is characterized by the epithelial–mesenchymal transition (EMT) process. We previously demonstrated that PPARγ activation by 2,4,6-octatrienoic acid (Octa) prevents cutaneous UV damage. We investigated the possible role of the PPARγ activators Octa and the new compound (2Z,4E,6E)-2-methoxyocta-2,4,6-trienoic acid (A02) in targeting keratinocyte-derived skin cancer. Like Octa, A02 exerted a protective effect against UVB-induced oxidative stress and DNA damage in NHKs. In the squamous cell carcinoma A431 cells, A02 inhibited cell proliferation and increased differentiation markers’ expression. Moreover, Octa and even more A02 counteracted the TGF-β1-dependent increase in mesenchymal markers, intracellular ROS, the activation of EMT-related signal transduction pathways, and cells’ migratory capacity. Both compounds, especially A02, counterbalanced the TGF-β1-induced cell membrane lipid remodeling and the release of bioactive lipids involved in EMT. In vivo experiments on a murine model useful to study cell proliferation in adult animals showed the reduction of areas characterized by active cell proliferation in response to A02 topical treatment. In conclusion, targeting PPARγ may be useful for the prevention and treatment of keratinocyte-derived skin cancer.

PPAR-γ Partial Agonists in Disease-Fate Decision with Special Reference to Cancer

Peroxisome proliferator-activated receptor-γ (PPAR-γ) has emerged as one of the most extensively studied transcription factors since its discovery in 1990, highlighting its importance in the etiology and treatment of numerous diseases involving various types of cancer, type 2 diabetes mellitus, autoimmune, dermatological and cardiovascular disorders. Ligands are regarded as the key determinant for the tissue-specific activation of PPAR-γ. However, the mechanism governing this process is merely a contradictory debate which is yet to be systematically researched. Either these receptors get weakly activated by endogenous or natural ligands or leads to a direct over-activation process by synthetic ligands, serving as complete full agonists. Therefore, fine-tuning on the action of PPAR-γ and more subtle modulation can be a rewarding approach which might open new avenues for the treatment of several diseases. In the recent era, researchers have sought to develop safer partial PPAR-γ agonists in order to dodge the toxicity induced by full agonists, akin to a balanced activation. With a particular reference to cancer, this review concentrates on the therapeutic role of partial agonists, especially in cancer treatment. Additionally, a timely examination of their efficacy on various other disease-fate decisions has been also discussed.