Multifaceted Roles of Caveolin-1 in Lung Cancer: A New Investigation Focused on Tumor Occurrence, Development and Therapy

Caveolin-1 protein expression as a prognostic biomarker of gastrointestinal tumours: A systematic review and meta-analysis

BACKGROUND

Gastrointestinal (GI) cancers remain a major threat worldwide, accounting for over 30% of cancer deaths. The identification of novel prognostic biomarkers remains a challenge despite significant advances in the field. The CAV1 gene, encoding the caveolin-1 protein, remains enigmatic in cancer and carcinogenesis, as it has been proposed to act as both a tumour promoter and a tumour suppressor.

METHODS

To analyse the differential role of caveolin-1 expression in both tumour cells and stroma in relation to prognosis in GI tumours, we performed a systematic review and meta-analysis according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines; PROSPERO registration number: CRD42022299148.

RESULTS

Our analysis showed that high levels of caveolin-1 in tumour cells were associated with poor prognosis and inferior overall survival (OS) in oesophageal and pancreatic cancer and hepatocellular carcinoma (HCC), but not in gastric and colorectal cancer. Importantly, our study showed that higher stromal caveolin-1 expression was associated with significantly longer OS and disease-free survival in colorectal cancer. Analysis of stromal caveolin-1 expression in the remaining tumours showed a similar trend, although it did not reach statistical significance.

CONCLUSIONS

The data suggest that caveolin-1 expression in the tumour cells of oesophageal, pancreatic cancer and HCC and in the stroma of colorectal cancer may be an important novel predictive biomarker for the clinical management of these diseases in a curative setting. However, the main conclusion of our analysis is that caveolin-1 expression should always be assessed separately in stroma and tumour cells.

Molecular pathogenesis, mechanism and therapy of Cav1 in prostate cancer

Prostate cancer is the second incidence of malignant tumors in men worldwide. Its incidence and mortality are increasing year by year. Enhanced expression of Cav1 in prostate cancer has been linked to both proliferation and metastasis of cancer cells, influencing disease progression. Dysregulation of the Cav1 gene shows a notable association with prostate cancer. Nevertheless, there is no systematic review to report about molecular signal mechanism of Cav1 and drug treatment in prostate cancer. This article reviews the structure, physiological and pathological functions of Cav1, the pathogenic signaling pathways involved in prostate cancer, and the current drug treatment of prostate cancer. Cav1 mainly affects the occurrence of prostate cancer through AKT/mTOR, H-RAS/PLCε, CD147/MMPs and other pathways, as well as substance metabolism including lipid metabolism and aerobic glycolysis. Baicalein, simvastatin, triptolide and other drugs can effectively inhibit the growth of prostate cancer. As a biomarker of prostate cancer, Cav1 may provide a potential therapeutic target for the treatment of prostate cancer.

Lung cancer associated with combustion particles and fine particulate matter (PM2.5) - The roles of polycyclic aromatic hydrocarbons (PAHs) and the aryl hydrocarbon receptor (AhR)

Air pollution is the leading cause of lung cancer after tobacco smoking, contributing to 20% of all lung cancer deaths. Increased risk associated with living near trafficked roads, occupational exposure to diesel exhaust, indoor coal combustion and cigarette smoking, suggest that combustion components in ambient fine particulate matter (PM2.5), such as polycyclic aromatic hydrocarbons (PAHs), may be central drivers of lung cancer. Activation of the aryl hydrocarbon receptor (AhR) induces expression of xenobiotic-metabolizing enzymes (XMEs) and increase PAH metabolism, formation of reactive metabolites, oxidative stress, DNA damage and mutagenesis. Lung cancer tissues from smokers and workers exposed to high combustion PM levels contain mutagenic signatures derived from PAHs. However, recent findings suggest that ambient air PM2.5 exposure primarily induces lung cancer development through tumor promotion of cells harboring naturally acquired oncogenic mutations, thus lacking typical PAH-induced mutations. On this background, we discuss the role of AhR and PAHs in lung cancer development caused by air pollution focusing on the tumor promoting properties including metabolism, immune system, cell proliferation and survival, tumor microenvironment, cell-to-cell communication, tumor growth and metastasis. We suggest that the dichotomy in lung cancer patterns observed between smoking and outdoor air PM2.5 represent the two ends of a dose-response continuum of combustion PM exposure, where tumor promotion in the peripheral lung appears to be the driving factor at the relatively low-dose exposures from ambient air PM2.5, whereas genotoxicity in the central airways becomes increasingly more important at the higher combustion PM levels encountered through smoking and occupational exposure.

CAVIN2/SDPR Functioned as a Tumor Suppressor in Lung Adenocarcinoma from Systematic Analysis of Caveolae-Related Genes and Experimental Validation

Background: Caveolae-Related Genes include caveolins and cavins, which are the main component of the fossa and, play important roles in a variety of physiological and pathological processes. Although increasing evidence indicated that caveolins (CAVs) and cavins (CAVINs) are involved in carcinogenesis and progression, their clinical significance and biological function in lung cancer are still limited. Methods: We investigated the expression of CAVs and CAVINs at transcriptional levels using Oncomine and Gene Expression Profiling Interactive Analysis. The protein and mRNA expression levels of CAVs and CAVINs were determined by the human protein atlas website and our surgically resected samples, respectively. The clinical value of prognostic prediction based on the expression of CAVs and CAVINs was also assessed. cBioPortal, GeneMANIA and STRING were used to analyze the molecular characteristics of CAVs and CAVINs in lung adenocarcinoma (LUAD) comprehensively. Finally, we investigated the effect of CAVIN2/SDPR (serum deprivation protein response) on LUAD cells with biological experiments in vitro. Results: The expression of CAV1/2 and CAVIN1/2/3 were significantly downregulated in LUAD and lung squamous cell carcinoma (LUSC). The patients with high expression of CAV1, CAV2, CAV3, CAVIN1 and CAVIN2/SDPR were tightly correlated with a better prognosis in LUAD, while no statistical significances in LUSC. Further, our results found that CAVIN2/SDPR can be identified as a prognostic biomarker independent of other CAVINs in patients with LUAD. Mechanically, the overexpression of CAVIN2/SDPR inhibited cell proliferation and migration owing to the cell apoptosis induction and cell cycle arrest at S phase in LUAD cells. Conclusions: CAVIN2/SDPR functioned as a tumor suppressor, and was able to serve as prognostic biomarkers in precision medicine of LUAD. Mechanically, overexpression of CAVIN2/SDPR inhibited cell proliferation by inducing cell apoptosis and S phase arrest in LUAD cells.

A novel approach to topological network analysis for the identification of metrics and signatures in non-small cell lung cancer

Non-small cell lung cancer (NSCLC), the primary histological form of lung cancer, accounts for about 25%-the highest-of all cancer deaths. As NSCLC is often undetected until symptoms appear in the late stages, it is imperative to discover more effective tumor-associated biomarkers for early diagnosis. Topological data analysis is one of the most powerful methodologies applicable to biological networks. However, current studies fail to consider the biological significance of their quantitative methods and utilize popular scoring metrics without verification, leading to low performance. To extract meaningful insights from genomic data, it is essential to understand the relationship between geometric correlations and biological function mechanisms. Through bioinformatics and network analyses, we propose a novel composite selection index, the C-Index, that best captures significant pathways and interactions in gene networks to identify biomarkers with the highest efficiency and accuracy. Furthermore, we establish a 4-gene biomarker signature that serves as a promising therapeutic target for NSCLC and personalized medicine. The C-Index and biomarkers discovered were validated with robust machine learning models. The methodology proposed for finding top metrics can be applied to effectively select biomarkers and early diagnose many diseases, revolutionizing the approach to topological network research for all cancers.

Methylation pattern of caveolin-1 in prostate cancer as potential cfDNA biomarker

High prevalence and mortality of prostate cancer (PCa) are well known global health issues. Novel biomarkers for better identifying patients with PCa are the subject of extensive research. Prostate specific antigen (PSA) shows low specificity in screening and diagnostics, leading to unnecessary biopsies and health costs. Eighty patients with PCa and benign prostate hyperplasia (BPH) were included in the study. We analyzed CAV1 gene expression and methylation in tissue. CAV1 cfDNA methylation from blood and seminal plasma was accessed as a potential PCa biomarker. Although methylation in blood plasma did not differ between PCa and BPH patients, methylation in seminal plasma showed better PCa biomarker performances than tPSA (AUC 0.63 vs. AUC 0.52). Discrimination of BPH and Gleason grade group 1 PCa patients from patients with higher Gleason grade groups revealed very good performance as well (AUC 0.72). CAV1 methylation is useful biomarker with potential for further seminal plasma cfDNA research, but its diagnostic accuracy should be improved, as well as general knowledge about cfDNA in seminal plasma.

Tumor-derived Cav-1 promotes pre-metastatic niche formation and lung metastasis in breast cancer

Rationale: Breast cancer (BC), as one of the most frequently diagnosed cancer, has a poor prognosis due to the development of distant metastasis. Among the BC metastatic sites, lung is one of the most common sites. Caveolin-1 (Cav-1) is a functional membrane protein that plays a vital role in tumor metastasis. Although studies have revealed that Cav-1 levels were elevated in patients with advanced cancer, whether Cav-1 affects BC lung metastasis by influencing the formation of pre-metastatic niche (PMN) through exosomes has not been explored. Methods: Differential ultracentrifugation, transmission electron microscopy and nanoparticle tracking analysis were used to verify the presence of exosomes. Transwell assays were used to examine the biological effects of exosomes containing Cav-1. Both in vitro cell cultures and mammary tumor cell-induced mouse models were used to assess the lung metastasis. The regulatory mechanisms of PMN formation were revealed using western blot, flow cytometry, RT-qPCR, immunofluorescence assays, gene overexpression assays and RNA interference assays. Results: Exosomes have critical functions in transporting Cav-1 between primary BC and metastatic organ microenvironments. Cav-1 in BC-derived exosomes can act as a signaling molecule to mediate intercellular communication and regulate the PMN before lung metastasis by regulating the expression of PMN marker genes and inflammatory chemokines in lung epithelial cells, promoting the secretion of tenascin-C (TnC) in lung fibroblasts to cause extracellular matrix (ECM) deposition, and inhibiting the PTEN/CCL2/VEGF-A signaling pathway in lung macrophages to facilitate their M2-type polarization and angiogenesis. Conclusion: Our study investigated the mechanisms of lung PMN formation induced by Cav-1 in BC-derived exosomes. Our data may provide new directions for exploring the mechanisms and developing treatment strategies of BC lung metastasis.

Shear stress enhances anoikis resistance of cancer cells through ROS and NO suppressed degeneration of Caveolin-1

Circulating tumor cells (CTCs) acquire enhanced anti-anoikis abilities after experiencing flow shear stress in the circulatory system. Our previous study demonstrated that low shear stress (LSS) promotes anoikis resistance of human breast carcinoma cells via caveolin-1 (Cav-1)-dependent extrinsic and intrinsic apoptotic pathways. However, the underlying mechanism how LSS enhanced Cav-1 expression in suspended cancer cells remains unclear. Herein, we found that LSS induced redox signaling was involved in the regulation of Cav-1 level and anoikis resistance in suspension cultured cancer cells. Exposure of human breast carcinoma MDA-MB-231 cells to LSS (2 dyn/cm²) markedly induced ROS and ^•NO generation, which promoted the cell viability and reduced the cancer cell apoptosis. Furthermore, ROS and ^•NO scavenging inhibited the upregulation of Cav-1 by interfering ubiquitination, and suppressed the anoikis resistance of suspended tumor cells. These findings provide new insight into the mechanism by which LSS-stimulated ROS and ^•NO generation increases Cav-1 stabilization in suspended cancer cells through inhibition of ubiquitination and proteasomal degradation, which could be a potential target for therapy of metastatic tumors.

Identification of candidate hub genes correlated with the pathogenesis, diagnosis, and prognosis of prostate cancer by integrated bioinformatics analysis

Background

Prostate cancer (PCa) has the second highest morbidity and mortality rates in men. Concurrently, novel diagnostic and prognostic biomarkers of PCa remain crucial.

Methods

This study utilized integrated bioinformatics method to identify and validate the potential hub genes with high diagnostic and prognostic value for PCa.

Results

Four Gene Expression Omnibus (GEO) datasets including 123 PCa samples and 76 normal samples were screened and a total of 368 differentially expressed genes (DEGs), including 120 up-regulated DEGs and 248 down-regulated DEGs, were identified. Subsequent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that the DEGs were majorly enriched in focal adhesion, chemical carcinogenesis, drug metabolism, and cytochrome P450 pathways. Then, 11 hub genes were identified from the protein-protein interaction (PPI) network of the DEGs; 7 of the 11 genes showed the ability of distinguishing PCa from normal prostate based on receiver operating characteristic (ROC) curve analysis. And 5 of the 11 genes were correlated with clinical attributes. Lower CAV1, KRT5, SNAI2 and MYLK expression level were associated with higer Gleason score, advanced pathological T stage and N stage. Lower KRT5 and MYLK expression level were significantly correlated with poor disease-free survival, and lower KRT5 and PTGS2 expression level were significantly related to biochemical recurrence (BCR) status of PCa patients.

Conclusions

In conclusion, CAV1, KRT5, SNAI2, and MYLK show potential clinical diagnostic and prognostic value and could be used as novel candidate biomarkers and therapeutic targets for PCa.

Protective role of Cav-1 in pneumolysin-induced endothelial barrier dysfunction

Pneumolysin (PLY) is a bacterial pore forming toxin and primary virulence factor of Streptococcus pneumonia, a major cause of pneumonia. PLY binds cholesterol-rich domains of the endothelial cell (EC) plasma membrane resulting in pore assembly and increased intracellular (IC) Ca2+ levels that compromise endothelial barrier integrity. Caveolae are specialized plasmalemma microdomains of ECs enriched in cholesterol. We hypothesized that the abundance of cholesterol-rich domains in EC plasma membranes confers cellular susceptibility to PLY. Contrary to this hypothesis, we found increased PLY-induced IC Ca2+ following membrane cholesterol depletion. Caveolin-1 (Cav-1) is an essential structural protein of caveolae and its regulation by cholesterol levels suggested a possible role in EC barrier function. Indeed, Cav-1 and its scaffolding domain peptide protected the endothelial barrier from PLY-induced disruption. In loss of function experiments, Cav-1 was knocked-out using CRISPR-Cas9 or silenced in human lung microvascular ECs. Loss of Cav-1 significantly enhanced the ability of PLY to disrupt endothelial barrier integrity. Rescue experiments with re-expression of Cav-1 or its scaffolding domain peptide protected the EC barrier against PLY-induced barrier disruption. Dynamin-2 (DNM2) is known to regulate caveolar membrane endocytosis. Inhibition of endocytosis, with dynamin inhibitors or siDNM2 amplified PLY induced EC barrier dysfunction. These results suggest that Cav-1 protects the endothelial barrier against PLY by promoting endocytosis of damaged membrane, thus reducing calcium entry and PLY-dependent signaling.

Gemcitabine-Resistant Biomarkers in Bladder Cancer are Associated with Tumor-Immune Microenvironment

To identify key biomarkers in gemcitabine (GEM)-resistant bladder cancer (BCa) and investigate their associations with tumor-infiltrating immune cells in a tumor immune microenvironment, we performed the present study on the basis of large-scale sequencing data. Expression profiles from the Gene Expression Omnibus GSE77883 dataset and The Cancer Genome Atlas BLCA dataset were analyzed. Both BCa development and GEM-resistance were identified to be immune-related through evaluating tumor-infiltrating immune cells. Eighty-two DEGs were obtained to be related to GEM-resistance. Functional enrichment analysis demonstrated they were related to regulation of immune cells proliferation. Protein–protein interaction network selected six key genes (CAV1, COL6A2, FABP4, FBLN1, PCOLCE, and CSPG4). Immunohistochemistry confirmed the down-regulation of the six key genes in BCa. Survival analyses revealed the six key genes were significantly associated with BCa overall survival. Correlation analyses revealed the six key genes had high infiltration of most immune cells. Gene set enrichment analysis further detected the key genes might regulate GEM-resistance through immune response and drug metabolism of cytochrome P450. Next, microRNA-gene regulatory network identified three key microRNAs (hsa-miR-124-3p, hsa-miR-26b-5p, and hsa-miR-192-5p) involved in GEM-resistant BCa. Connectivity Map analysis identified histone deacetylase inhibitors might circumvent GEM-resistance. In conclusion, CAV1, COL6A2, FABP4, FBLN1, PCOLCE, and CSPG4 were identified to be critical biomarkers through regulating the immune cell infiltration in an immune microenvironment of GEM-resistance and could act as promising treatment targets for GEM-resistant muscle-invasive BCa.

Systematic interaction of plasma albumin with the efficacy of chemotherapeutic drugs

Albumin, as the most abundant plasma protein, plays an integral role in the transport of a variety of exogenous and endogenous ligands in the bloodstream and extravascular spaces. For exogenous drugs, especially chemotherapeutic drugs, binding to and being delivered by albumin can significantly affect their efficacy. Meanwhile, albumin can also bind to many endogenous ligands, such as fatty acids, with important physiological significance that can affect tumor proliferation and metabolism. In this review, we summarize how albumin with unique properties affects chemotherapeutic drugs efficacy from the aspects of drug outcome in blood, toxicity, tumor accumulation and direct or indirect interactions with fatty acids, plus application of albumin-based carriers for anti-tumor drug delivery.

Plasma exosomal caveolin-1 predicts Poor Prognosis in Ovarian Cancer

Objective: In this study, we aimed to evaluate the levels of plasma exosomal caveolin-1(CAV1) and determine its prognostic value in ovarian cancer patients.

Patients and Methods: Exosome-rich fractions were isolated from the plasma of 155 patients with ovarian cancer. TEM, NTA and western blot analysis were used to confirm the exosome integrity and purification.

Results: Compared with healthy controls, plasma exosomal CAV1 levels in ovarian cancer patient were significantly down-regulated (P < 0.001). The low plasma levels of exosomal CAV1 in ovarian cancer patient plasma were related to FIGO stages, grades and lymph node metastasis (all P < 0.01). Among all ovarian cancer patients, DFS was worse in patients who had low plasma exosomal CAV1 levels compared with that in patients with high plasma exosomal CAV1 levels (P < 0.001). The OS of patients with low plasma exosomal CAV1 levels was shorter than that in patients with high plasma exosomal CAV1 levels (P < 0.001). The AUROC of plasma exosomal CAV1 was 0.76 (95% CI: 0.68-0.82) for DFS prediction in ovarian cancer patients, with a sensitivity 52.9 (95% CI: 42.8-62.9) and a specificity 88.7 (95% CI: 77.0-95.7). For OS prediction in ovarian cancer patients, the AUROC of plasma exosomal CAV1 was 0.78 (95% CI: 0.70-0.84), with a sensitivity 65.1 (95% CI: 49.1-79.0) and a specificity 81.2 (95% CI: 72.8-88.0).

Conclusions: Low exosomal CAV1 levels were closely related to the FIGO stages I/II, low grade, lymph node metastasis and prognosis of ovarian cancer patients. Plasma exosomal CAV1 may be a potential biomarker for the prognosis in ovarian cancer patients.

Decreased HLF Expression Predicts Poor Survival in Lung Adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is a type of non-small cell carcinoma. Its pathogenesis is being explored and there is no cure for the disease.

Material/Methods

The Gene Expression Omnibus (GEO) was searched to obtain data on expression of messenger RNA. GEO2R, an interactive web tool, was used to calculate the differentially expressed genes (DEGs) in LUAD. All the DEGs from different datasets were imported into VENNY 2.1 (https://bioinfogp.cnb.csic.es/tools/venny/index.html) to identify the intersection of the DEGs. An online analysis tool, the Database for Annotation, Visualization, and Integrated Discovery (DAVID), was used to help understand the biological meaning of DEG enrichment in LUAD. Cytoscape 3.7.2 was used to perform centrality analysis and visualize hub genes and related networks. Furthermore, the prognostic value of the hub genes was evaluated with the Kaplan-Meier plotter survival analysis tool.

Results

The GEO database was used to obtain RNA sequencing information for LUAD and normal tissue from the GSE118370, GSE136043, and GSE140797 datasets. A total of 376 DEGs were identified from GSE118370, 248 were identified from GSE136403, and 718 DEGs were identified from GSE140797. The 10 genes with the highest degrees of expression – the hub genes – were CAV1, TEK, SLIT2, RHOJ, DGSX, HLF, MEIS1, PTPRD, FOXF1, and ADRB2. In addition, Kaplan-Meier survival evaluation showed that CAV1, TEK, SLIT2, HLF, MEIS1, PTPRD, FOXF1, and ADRB2 were associated with favorable outcomes for LUAD.

Conclusions

CAV1, TEK, SLIT2, HLF, MEIS1, PTPRD, FOXF1, and ADRB2 are hub genes in the DEG interaction network for LUAD and are involved in the development of and prognosis for the disease. The mechanisms underlying these genes should be the subject of further studies.

Lentinan Attenuated the PM2.5 Exposure-Induced Inflammatory Response, Epithelial-Mesenchymal Transition and Migration by Inhibiting the PVT1/miR-199a-5p/caveolin1 Pathway in Lung Cancer

PM2.5 plays an important role in the physiological and pathological progression of lung cancer. Lentinan exerts antitumor activity in many kinds of human cancers. Plasmacytoma variant translocation 1 (PVT1) exerts antitumor activity in many kinds of human cancers. However, the role and underlying molecular mechanism of PVT1 in the role of lentinan in PM2.5-exposed lung cancer are still largely unknown. Our study confirmed that PM2.5 exposure induced the production of inflammatory factors, epithelial-mesenchymal transition (EMT) and migration of lung cancer cells. Lentinan exerted antitumor effects by inhibiting the production of inflammatory factors, EMT, and migration of lung cancer cells. Lentinan suppressed PM2.5 exposure-induced cellular progression by inhibiting the PM2.5 exposure-induced elevation of PVT1 expression. PVT1 absorbed miR-199a, and miR-199a inhibited caveolin1 expression and thus formed the PVT1/miR-199a/caveolin1 signaling pathway in lung cancer cells. Our study revealed that silencing of the PVT1/miR-199a/caveolin1 signaling pathway affected the role of lentinan in PM2.5-exposed lung cancer cells. Thus, this study first investigated the role of lentinan in PM2.5-exposed lung cancer cells and further displayed the underlying molecular mechanism, providing a potential treatment for PM2.5-exposed lung cancer.

Females and Males Show Differences in Early-Stage Transcriptomic Biomarkers of Lung Adenocarcinoma and Lung Squamous Cell Carcinoma

The incidence and mortality rates of lung cancers are different between females and males. Therefore, sex information should be an important part of how to train and optimize a diagnostic model. However, most of the existing studies do not fully utilize this information. This study carried out a comparative investigation between sex-specific models and sex-independent models. Three feature selection algorithms and five classifiers were utilized to evaluate the contribution of the sex information to the detection of early-stage lung cancers. Both lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) showed that the sex-specific models outperformed the sex-independent detection of early-stage lung cancers. The Venn plots suggested that females and males shared only a few transcriptomic biomarkers of early-stage lung cancers. Our experimental data suggested that sex information should be included in optimizing disease diagnosis models.

Macrocybin, a Natural Mushroom Triglyceride, Reduces Tumor Growth In Vitro and In Vivo through Caveolin-Mediated Interference with the Actin Cytoskeleton

Mushrooms have been used for millennia as cancer remedies. Our goal was to screen several mushroom species from the rainforests of Costa Rica, looking for new antitumor molecules. Mushroom extracts were screened using two human cell lines: A549 (lung adenocarcinoma) and NL20 (immortalized normal lung epithelium). Extracts able to kill tumor cells while preserving non-tumor cells were considered “anticancer”. The mushroom with better properties was Macrocybe titans. Positive extracts were fractionated further and tested for biological activity on the cell lines. The chemical structure of the active compound was partially elucidated through nuclear magnetic resonance, mass spectrometry, and other ancillary techniques. Chemical analysis showed that the active molecule was a triglyceride containing oleic acid, palmitic acid, and a more complex fatty acid with two double bonds. The synthesis of all possible triglycerides and biological testing identified the natural compound, which was named Macrocybin. A xenograft study showed that Macrocybin significantly reduces A549 tumor growth. In addition, Macrocybin treatment resulted in the upregulation of Caveolin-1 expression and the disassembly of the actin cytoskeleton in tumor cells (but not in normal cells). In conclusion, we have shown that Macrocybin constitutes a new biologically active compound that may be taken into consideration for cancer treatment.

Predicting Deep Learning Based Multi-Omics Parallel Integration Survival Subtypes in Lung Cancer Using Reverse Phase Protein Array Data

Mortality attributed to lung cancer accounts for a large fraction of cancer deaths worldwide. With increasing mortality figures, the accurate prediction of prognosis has become essential. In recent years, multi-omics analysis has emerged as a useful survival prediction tool. However, the methodology relevant to multi-omics analysis has not yet been fully established and further improvements are required for clinical applications. In this study, we developed a novel method to accurately predict the survival of patients with lung cancer using multi-omics data. With unsupervised learning techniques, survival-associated subtypes in non-small cell lung cancer were first detected using the multi-omics datasets from six categories in The Cancer Genome Atlas (TCGA). The new subtypes, referred to as integration survival subtypes, clearly divided patients into longer and shorter-surviving groups (log-rank test: p = 0.003) and we confirmed that this is independent of histopathological classification (Chi-square test of independence: p = 0.94). Next, an attempt was made to detect the integration survival subtypes using only one categorical dataset. Our machine learning model that was only trained on the reverse phase protein array (RPPA) could accurately predict the integration survival subtypes (AUC = 0.99). The predicted subtypes could also distinguish between high and low risk patients (log-rank test: p = 0.012). Overall, this study explores novel potentials of multi-omics analysis to accurately predict the prognosis of patients with lung cancer.

Elevated Expression of CAV1 is Associated with Unfavorable Prognosis of Patients with Breast Cancer Who Undergo Surgery and Neoadjuvant Chemotherapy

Introduction

Neoadjuvant chemotherapy (NACT), which is standard treatment for locally advanced breast cancer, improves the resectability of patients with early breast cancer and reduces the extent of breast and axillary surgery. Caveolin-1 (CAV1) is implicated in human cancers, although its utility for cancer prognosis is unknown. Here, we investigated the expression of CAV1 in breast cancer tissues to evaluate its prognostic significance on patients with breast cancer administered NACT.

Methods

CAV1 expression in 80 breast cancer tissue samples was evaluated using immunohistochemistry (IHC). The association between CAV1 levels and clinical factors was analyzed using the chi-square test and that between CAV1 and prognosis was evaluated using multivariate Cox regression and Kaplan–Meier analyses.

Results

High levels of CAV1 were significantly associated with survival, and patients with overexpression of CAV1 had a poor prognosis. Adjusted multivariate Cox regression analyses revealed that a high level of CAV1 expression was an independent, significant prognostic factor for patients with breast cancer treated with NACT.

Discussion

Overexpression of CAV1 in patients with breast cancer administered NACT was associated with shorter disease-free survival and overall survival. Therefore, high levels of CAV1 may serve as a prognostic biomarker for such patients.