Identification of Overexpressed Genes in Malignant Pleural Mesothelioma

Enhancement of Classifier Performance with Adam and RanAdam Hyper-Parameter Tuning for Lung Cancer Detection from Microarray Data-In Pursuit of Precision

Microarray gene expression analysis is a powerful technique used in cancer classification and research to identify and understand gene expression patterns that can differentiate between different cancer types, subtypes, and stages. However, microarray databases are highly redundant, inherently nonlinear, and noisy. Therefore, extracting meaningful information from such a huge database is a challenging one. The paper adopts the Fast Fourier Transform (FFT) and Mixture Model (MM) for dimensionality reduction and utilises the Dragonfly optimisation algorithm as the feature selection technique. The classifiers employed in this research are Nonlinear Regression, Naïve Bayes, Decision Tree, Random Forest and SVM (RBF). The classifiers' performances are analysed with and without feature selection methods. Finally, Adaptive Moment Estimation (Adam) and Random Adaptive Moment Estimation (RanAdam) hyper-parameter tuning techniques are used as improvisation techniques for classifiers. The SVM (RBF) classifier with the Fast Fourier Transform Dimensionality Reduction method and Dragonfly feature selection achieved the highest accuracy of 98.343% with RanAdam hyper-parameter tuning compared to other classifiers.

Genes and Pathways Involved in the Progression of Malignant Pleural Mesothelioma: A Meta-analysis of Genome-Wide Expression Studies

Malignant pleural mesothelioma (MPM) is a rare and aggressive neoplasm of the pleural tissue that lines the lungs and is mainly associated with long latency from asbestos exposure. This tumor has no effective therapeutic opportunities nowadays and has a very low five-year survival rate. In this sense, identifying molecular events that trigger the development and progression of this tumor is highly important to establish new and potentially effective treatments. We conducted a meta-analysis of genome-wide expression studies publicly available at the Gene Expression Omnibus (GEO) and ArrayExpress databases. The differentially expressed genes (DEGs) were identified, and we performed functional enrichment analysis and protein-protein interaction networks (PPINs) to gain insight into the biological mechanisms underlying these genes. Additionally, we constructed survival prediction models for selected DEGs and predicted the minimum drug inhibition concentration of anticancer drugs for MPM. In total, 115 MPM tumor transcriptomes and 26 pleural tissue controls were analyzed. We identified 1046 upregulated DEGs in the MPM samples. Cellular signaling categories in tumor samples were associated with the TNF, PI3K-Akt, and AMPK pathways. The inflammatory response, regulation of cell migration, and regulation of angiogenesis were overrepresented biological processes. Expression of SOX17 and TACC1 were associated with reduced survival rates. This meta-analysis identified a list of DEGs in MPM tumors, cancer-related signaling pathways, and biological processes that were overrepresented in MPM samples. Some therapeutic targets to treat MPM are suggested, and the prognostic potential of key genes is shown.

Development of mesothelioma-specific oncolytic immunotherapy enabled by immunopeptidomics of murine and human mesothelioma tumors

Malignant pleural mesothelioma (MPM) is an aggressive tumor with a poor prognosis. As the available therapeutic options show a lack of efficacy, novel therapeutic strategies are urgently needed. Given its T-cell infiltration, we hypothesized that MPM is a suitable target for therapeutic cancer vaccination. To date, research on mesothelioma has focused on the identification of molecular signatures to better classify and characterize the disease, and little is known about therapeutic targets that engage cytotoxic (CD8+) T cells. In this study we investigate the immunopeptidomic antigen-presented landscape of MPM in both murine (AB12 cell line) and human cell lines (H28, MSTO-211H, H2452, and JL1), as well as in patients' primary tumors. Applying state-of-the-art immuno-affinity purification methodologies, we identify MHC I-restricted peptides presented on the surface of malignant cells. We characterize in vitro the immunogenicity profile of the eluted peptides using T cells from human healthy donors and cancer patients. Furthermore, we use the most promising peptides to formulate an oncolytic virus-based precision immunotherapy (PeptiCRAd) and test its efficacy in a mouse model of mesothelioma in female mice. Overall, we demonstrate that the use of immunopeptidomic analysis in combination with oncolytic immunotherapy represents a feasible and effective strategy to tackle untreatable tumors.

Evaluation and Exploration of Machine Learning and Convolutional Neural Network Classifiers in Detection of Lung Cancer from Microarray Gene-A Paradigm Shift

Microarray gene expression-based detection and classification of medical conditions have been prominent in research studies over the past few decades. However, extracting relevant data from the high-volume microarray gene expression with inherent nonlinearity and inseparable noise components raises significant challenges during data classification and disease detection. The dataset used for the research is the Lung Harvard 2 Dataset (LH2) which consists of 150 Adenocarcinoma subjects and 31 Mesothelioma subjects. The paper proposes a two-level strategy involving feature extraction and selection methods before the classification step. The feature extraction step utilizes Short Term Fourier Transform (STFT), and the feature selection step employs Particle Swarm Optimization (PSO) and Harmonic Search (HS) metaheuristic methods. The classifiers employed are Nonlinear Regression, Gaussian Mixture Model, Softmax Discriminant, Naive Bayes, SVM (Linear), SVM (Polynomial), and SVM (RBF). The two-level extracted relevant features are compared with raw data classification results, including Convolutional Neural Network (CNN) methodology. Among the methods, STFT with PSO feature selection and SVM (RBF) classifier produced the highest accuracy of 94.47%.

Omics Overview of the SPARC Gene in Mesothelioma

The SPARC gene plays multiple roles in extracellular matrix synthesis and cell shaping, associated with tumor cell migration, invasion, and metastasis. The SPARC gene is also involved in the epithelial-mesenchymal transition (EMT) process, which is a critical phenomenon leading to a more aggressive cancer cell phenotype. SPARC gene overexpression has shown to be associated with poor survival in the mesothelioma (MESO) cohort from the TCGA database, indicating that this gene may be a powerful prognostic factor in MESO. Its overexpression is correlated with the immunosuppressive tumor microenvironment. Here, we summarize the omics advances of the SPARC gene, including the summary of SPARC gene expression associated with prognosis in pancancer and MESO, the immunosuppressive microenvironment, and cancer cell stemness. In addition, SPARC might be targeted by microRNAs. Notably, despite the controversial functions on angiogenesis, SPARC may directly or indirectly contribute to tumor angiogenesis in MESO. In conclusion, SPARC is involved in tumor invasion, metastasis, immunosuppression, cancer cell stemness, and tumor angiogenesis, eventually impacting patient survival. Strategies targeting this gene may provide novel therapeutic approaches to the treatment of MESO.

A Novel Two-Gene Expression-Based Prognostic Score in Malignant Pleural Mesothelioma

BACKGROUND

Malignant pleural mesothelioma (MPM) is a rare cancer type with an increasing incidence worldwide. We aimed to develop a rational gene expression-based prognostic score in MPM using publicly available datasets.

METHODS

We developed and validated a two-gene prognostic score (2-PS) using three independent publicly available gene expression datasets. The 2-PS was built using the Robust Likelihood-Based Survival Modeling with Microarray Data method.

RESULTS

We narrowed down the model building to the analysis of 179 genes, which have been shown previously to be of importance to MPM development. Our statistical approach showed that a model including two genes (GOLT1B and MAD2L1) was the best predictor for overall survival (OS) (p < 0.0001). The binary score based on the median of the continuous score stratified the patients into low and high score groups and also showed statistical significance in uni- and multivariate models. The 2-PS was validated using two independent transcriptomic datasets. Furthermore, gene set enrichment analysis using training and validation datasets showed that high score patients had distinct gene expression profiles. Our 2-PS also showed a correlation with the estimated infiltration by some immune cell fractions such as CD8+ T cells and M1/2 macrophages. Finally, 2-PS correlated with sensitivity or resistance to some commonly used chemotherapeutic drugs.

CONCLUSION

This is the first study to demonstrate good performance of only two-gene expression-based prognostic scores in MPM. Our initial approach for features selection allowed for an increased likelihood for the predictive value of the developed score, which we were also able to demonstrate.

OncoRTT: Predicting novel oncology-related therapeutic targets using BERT embeddings and omics features

Late-stage drug development failures are usually a consequence of ineffective targets. Thus, proper target identification is needed, which may be possible using computational approaches. The reason being, effective targets have disease-relevant biological functions, and omics data unveil the proteins involved in these functions. Also, properties that favor the existence of binding between drug and target are deducible from the protein’s amino acid sequence. In this work, we developed OncoRTT, a deep learning (DL)-based method for predicting novel therapeutic targets. OncoRTT is designed to reduce suboptimal target selection by identifying novel targets based on features of known effective targets using DL approaches. First, we created the “OncologyTT” datasets, which include genes/proteins associated with ten prevalent cancer types. Then, we generated three sets of features for all genes: omics features, the proteins’ amino-acid sequence BERT embeddings, and the integrated features to train and test the DL classifiers separately. The models achieved high prediction performances in terms of area under the curve (AUC), i.e., AUC greater than 0.88 for all cancer types, with a maximum of 0.95 for leukemia. Also, OncoRTT outperformed the state-of-the-art method using their data in five out of seven cancer types commonly assessed by both methods. Furthermore, OncoRTT predicts novel therapeutic targets using new test data related to the seven cancer types. We further corroborated these results with other validation evidence using the Open Targets Platform and a case study focused on the top-10 predicted therapeutic targets for lung cancer.

Case report: Mesothelioma and BAP1 tumor predisposition syndrome: Implications for public health

BRCA-1 associated protein 1 (BAP1) tumour predisposition syndrome (TPDS) is a hereditary condition characterised by germline mutation of the tumour suppressor BAP1. This disorder is associated with the development of various benign and malignant tumours, mainly involving the skin, eyes, kidneys, and mesothelium. In this article, we report the case of a man recruited through the Apulia (Southern Italy) Mesothelioma Regional Operational Centre of the National Register of Mesotheliomas, who suffered from uveal melanoma, renal cancer, and mesothelioma, and a familial cluster of BAP1 germline mutations demonstrated by molecular analyses. The family members of the proband developed multiple malignancies. As tumours arising in this context have specific peculiarities in terms of clinical behaviour, identification of this condition through appropriate genetic counselling should be considered for adequate primary, secondary, and tertiary prevention measures for offspring.

Identification of Novel Diagnostic Markers for Malignant Pleural Mesothelioma Using a Reverse Translational Approach Based on a Rare Synchronous Tumor

Although the routine use of immunohistochemistry has improved the accuracy of histopathologic diagnosis in clinical practice, new methods for discovering novel diagnostic markers are still needed. We sought new diagnostic markers for malignant pleural mesothelioma (MPM) using a reverse translational approach with limited archival tissues from a very rare case. Total RNA extracted from formalin-fixed paraffin-embedded (FFPE) tissues of a synchronous collision tumor consisting of MPM and pulmonary adenocarcinoma (PAC) was employed for gene expression profiling (GEP) analysis. Among the 54 genes selected by GEP analysis, we finally identified the following two candidate MPM marker genes: PHGDH and TRIM29. Immunohistochemical analysis of 48 MM and 20 PAC cases showed that both PHGDH and TRIM29 had sensitivity and specificity almost equivalent to those of calretinin (sensitivity 50% and 46% vs. 63%, and specificity 95% and 100% vs. 100%, respectively). Importantly, of the 23 epithelioid MMs, all 3 calretinin-negative cases were positive for TRIM29. These two markers may be diagnostically useful for immunohistochemical distinction between MPMs and PACs. This successful reverse translational approach based on FFPE samples from one very rare case encourages the further use of such samples for the development of novel diagnostic markers.

A panel of emerging EMT genes identified in malignant mesothelioma

Malignant mesothelioma (MESO) is a highly aggressive cancer with poor prognosis. Epithelial-mesenchymal transition (EMT) is a critical process in malignancies involved in tumor angiogenesis, progression, invasion and metastasis, immunosuppressive microenvironment and therapy resistance. However, there is a lack of specific biomarkers to identify EMT in MESO. Biphasic MESO with dual phenotypes could be an optimal model to study EMT process. Using a powerful EMTome to investigate EMT gene signature, we identified a panel of EMT genes COL5A2, ITGAV, SPARC and ACTA2 in MESO. In combination with TCGA database, Timer2.0 and other resources, we observed that overexpression of these emerging genes is positively correlated with immunosuppressive infiltration, and an unfavorable factor to patient survival in MESO. The expression of these genes was confirmed in our patients and human cell lines. Our findings suggest that these genes may be novel targets for therapeutics and prognosis in MESO and other types of cancers.

Kinesin family member 23 exerts a protumor function in breast cancer via stimulation of the Wnt/β-catenin pathway

Kinesin family member 23 (KIF23) has been described as one of the main genes that are associated with malignant transformation in numerous cancers. However, the exact significance of KIF23 in breast cancer has not been well-addressed. The present study was dedicated to the comprehensive investigation of KIF23 in breast cancer. Initial expression analysis through The Cancer Genome Atlas (TCGA) demonstrated high KIF23 levels in breast cancer compared with normal controls. These in silico data showing high levels of KIF23 in breast cancer were verified by assessing clinical specimens using real-time quantitative PCR and immunoblot assays. Moreover, a high KIF23 level was correlated with adverse clinical outcomes in breast cancer patients. Cellular functional experiments showed that the down-regulation of KIF23 affected the malignant behaviors of breast cancer cells in vitro, whereas the forced expression of KIF23 stimulated them. Mechanistic studies revealed that KIF23 restraint down-regulated the levels of phosphorylated glycogen synthetase kinase-3β (GSK-3β), β-catenin, cyclin D1 and c-myc in breast cancer cells, showing an inhibitory effect on the Wnt/β-catenin pathway. The suppression of GSK-3β was able to reverse KIF23-silencing-induced inactivation of the Wnt/β-catenin pathway. Inhibition of the Wnt/β-catenin pathway abolished KIF23 overexpression-mediated protumor effects in breast cancer. A xenograft assay confirmed the in vivo antitumor function of KIF23 inhibition. In conclusion, these findings suggest that KIF23 may exert a protumor function in breast cancer by stimulating the Wnt/β-catenin pathway. This work suggests that KIF23 has potential values for targeted therapy and prognosis in breast cancer.

Pathological Characterization of Tumor Immune Microenvironment (TIME) in Malignant Pleural Mesothelioma

Malignant pleural mesothelioma (MPM) is a rare and highly aggressive disease that arises from pleural mesothelial cells, characterized by a median survival of approximately 13-15 months after diagnosis. The primary cause of this disease is asbestos exposure and the main issues associated with it are late diagnosis and lack of effective therapies. Asbestos-induced cellular damage is associated with the generation of an inflammatory microenvironment that influences and supports tumor growth, possibly in association with patients' genetic predisposition and tumor genomic profile. The chronic inflammatory response to asbestos fibers leads to a unique tumor immune microenvironment (TIME) composed of a heterogeneous mixture of stromal, endothelial, and immune cells, and relative composition and interaction among them is suggested to bear prognostic and therapeutic implications. TIME in MPM is known to be constituted by immunosuppressive cells, such as type 2 tumor-associated macrophages and T regulatory lymphocytes, plus the expression of several immunosuppressive factors, such as tumor-associated PD-L1. Several studies in recent years have contributed to achieve a greater understanding of the pathogenetic mechanisms in tumor development and pathobiology of TIME, that opens the way to new therapeutic strategies. The study of TIME is fundamental in identifying appropriate prognostic and predictive tissue biomarkers. In the present review, we summarize the current knowledge about the pathological characterization of TIME in MPM.