Identification of transcriptional subtypes in lung adenocarcinoma and squamous cell carcinoma through integrative analysis of microarray and RNA sequencing data

Correlation of CT features of lung adenocarcinoma with sex and age

This study aimed to retrospectively examine the computed tomography (CT) features of lung adenocarcinoma across different demographic groups. Preoperative chest CT findings from 1266 surgically resected lung adenocarcinoma cases were retrospectively analyzed. Lung adenocarcinomas were categorized based on CT characteristics into pure ground glass (pGGO), nodule-containing ground glass opacity (mGGO), and pure solid without containing ground glass opacity (pSD). These categories were correlated with sex, age, EGFR status, and five histopathological subtypes. The diameters of pGGO, mGGO, and pSD significantly increased across all patient groups (P < 0.05). Males exhibited a significantly higher proportion of pSD than females (P = 0.002). The mean diameters of pGGO and pSD were significantly larger in males than in females (P = 0.0017 and P = 0.043, respectively). The frequency of pGGO was higher in the younger age group (≤ 60 years) compared to the older group (> 60 years) for both males (P = 0.002) and females (P = 0.027). The frequency of pSD was higher in the older age group for both sexes. A linear correlation between age and diameter was observed in the entire cohort as well as in the male and female groups (P < 0.0001 for all groups). EGFR mutations were less frequent in pSD compared to pGGO (P = 0.0002) and mGGO (P < 0.0001). The frequency of lesions containing micropapillary components increased from pGGO to mGGO and pSD (P < 0.0001 for all). The frequency of lesions containing solid components also increased from pGGO to mGGO and pSD (P = 0.045, P < 0.0001, and P < 0.0001, respectively). The CT features of lung adenocarcinoma exhibit differences across genders and age groups. Male gender and older age are risk factors for lung adenocarcinoma growth.

Transcriptomic Profiling for Prognostic Biomarkers in Early-Stage Squamous Cell Lung Cancer (SqCLC)

Squamous cell lung carcinoma (SqCLC) is associated with high mortality and limited treatment options. Identification of therapeutic targets and prognostic biomarkers is still lacking. This research aims to analyze the transcriptomic profile of SqCLC samples and identify the key genes associated with tumorigenesis, overall survival (OS), and a profile of the tumor-infiltrating immune cells. Differential gene expression analysis, pathway enrichment analysis, and Gene Ontology analysis on RNA-seq data obtained from FFPE tumor samples (N = 23) and healthy tissues (N = 3) were performed (experimental cohort). Validation of the results was conducted on publicly available gene expression data using TCGA LUSC (N = 225) and GTEx healthy donors' cohorts (N = 288). We identified 1133 upregulated and 644 downregulated genes, common for both cohorts. The most prominent upregulated genes were involved in cell cycle and proliferation regulation pathways (MAGEA9B, MAGED4, KRT, MMT11/13), while downregulated genes predominately belonged to immune-related pathways (DEFA1B, DEFA1, DEFA3). Results of the survival analysis, conducted on the validation cohort and commonly deregulated genes, indicated that overexpression of HOXC4 (p < 0.001), LLGL1 (p = 0.0015), and SLC4A3 (p = 0.0034) is associated with worse OS in early-stage SqCLC patients. In contrast, overexpression of GSTZ1 (p = 0.0029) and LILRA5 (p = 0.0086) was protective, i.e., associated with better OS. By applying a single-sample gene-set enrichment analysis (ssGSEA), we identified four distinct immune subtypes. Immune cell distribution suggests that the memory T cells (central and effector) and follicular helper T cells could serve as important stratification parameters.

Navigating the Frontiers of Machine Learning in Neurodegenerative Disease Therapeutics

Recent advances in machine learning hold tremendous potential for enhancing the way we develop new medicines. Over the years, machine learning has been adopted in nearly all facets of drug discovery, including patient stratification, lead discovery, biomarker development, and clinical trial design. In this review, we will discuss the latest developments linking machine learning and CNS drug discovery. While machine learning has aided our understanding of chronic diseases like Alzheimer's disease and Parkinson's disease, only modest effective therapies currently exist. We highlight promising new efforts led by academia and emerging biotech companies to leverage machine learning for exploring new therapies. These approaches aim to not only accelerate drug development but to improve the detection and treatment of neurodegenerative diseases.

Uniformly shaped harmonization combines human transcriptomic data from different platforms while retaining their biological properties and differential gene expression patterns

Introduction: Co-normalization of RNA profiles obtained using different experimental platforms and protocols opens avenue for comprehensive comparison of relevant features like differentially expressed genes associated with disease. Currently, most of bioinformatic tools enable normalization in a flexible format that depends on the individual datasets under analysis. Thus, the output data of such normalizations will be poorly compatible with each other. Recently we proposed a new approach to gene expression data normalization termed Shambhala which returns harmonized data in a uniform shape, where every expression profile is transformed into a pre-defined universal format. We previously showed that following shambhalization of human RNA profiles, overall tissue-specific clustering features are strongly retained while platform-specific clustering is dramatically reduced. Methods: Here, we tested Shambhala performance in retention of fold-change gene expression features and other functional characteristics of gene clusters such as pathway activation levels and predicted cancer drug activity scores. Results: Using 6,793 cancer and 11,135 normal tissue gene expression profiles from the literature and experimental datasets, we applied twelve performance criteria for different versions of Shambhala and other methods of transcriptomic harmonization with flexible output data format. Such criteria dealt with the biological type classifiers, hierarchical clustering, correlation/regression properties, stability of drug efficiency scores, and data quality for using machine learning classifiers. Discussion: Shambhala-2 harmonizer demonstrated the best results with the close to 1 correlation and linear regression coefficients for the comparison of training vs validation datasets and more than two times lesser instability for calculation of drug efficiency scores compared to other methods.

miRNA-Seq Tissue Diagnostic Signature: A Novel Model for NSCLC Subtyping

Non-small cell lung cancer (NSCLC) encompasses distinct histopathological subtypes, namely adenocarcinoma (AC) and squamous cell lung carcinoma (SCC), which require precise differentiation for effective treatment strategies. In this study, we present a novel molecular diagnostic model that integrates tissue-specific expression profiles of microRNAs (miRNAs) obtained through next-generation sequencing (NGS) to discriminate between AC and SCC subtypes of NSCLC. This approach offers a more comprehensive and precise molecular characterization compared to conventional methods such as histopathology or immunohistochemistry. Firstly, we identified 31 miRNAs with significant differential expression between AC and SCC cases. Subsequently, we constructed a 17-miRNA signature through rigorous multistep analyses, including LASSO/elastic net regression. The signature includes both upregulated miRNAs (hsa-miR-326, hsa-miR-450a-5p, hsa-miR-1287-5p, hsa-miR-556-5p, hsa-miR-542-3p, hsa-miR-30b-5p, hsa-miR-4728-3p, hsa-miR-450a-1-3p, hsa-miR-375, hsa-miR-147b, hsa-miR-7705, and hsa-miR-653-3p) and downregulated miRNAs (hsa-miR-944, hsa-miR-205-5p, hsa-miR-205-3p, hsa-miR-149-5p, and hsa-miR-6510-3p). To assess the discriminative capability of the 17-miRNA signature, we performed receiver operating characteristic (ROC) curve analysis, which demonstrated an impressive area under the curve (AUC) value of 0.994. Our findings highlight the exceptional diagnostic performance of the miRNA signature as a stratifying biomarker for distinguishing between AC and SCC subtypes in lung cancer. The developed molecular diagnostic model holds promise for providing a more accurate and comprehensive molecular characterization of NSCLC, thereby guiding personalized treatment decisions and improving clinical management and prognosis for patients.

Transcriptomic Harmonization as the Way for Suppressing Cross-Platform Bias and Batch Effect

(1) Background: Emergence of methods interrogating gene expression at high throughput gave birth to quantitative transcriptomics, but also posed a question of inter-comparison of expression profiles obtained using different equipment and protocols and/or in different series of experiments. Addressing this issue is challenging, because all of the above variables can dramatically influence gene expression signals and, therefore, cause a plethora of peculiar features in the transcriptomic profiles. Millions of transcriptomic profiles were obtained and deposited in public databases of which the usefulness is however strongly limited due to the inter-comparison issues; (2) Methods: Dozens of methods and software packages that can be generally classified as either flexible or predefined format harmonizers have been proposed, but none has become to the date the gold standard for unification of this type of Big Data; (3) Results: However, recent developments evidence that platform/protocol/batch bias can be efficiently reduced not only for the comparisons of limited transcriptomic datasets. Instead, instruments were proposed for transforming gene expression profiles into the universal, uniformly shaped format that can support multiple inter-comparisons for reasonable calculation costs. This forms a basement for universal indexing of all or most of all types of RNA sequencing and microarray hybridization profiles; (4) Conclusions: In this paper, we attempted to overview the landscape of modern approaches and methods in transcriptomic harmonization and focused on the practical aspects of their application.

Protocol for using Ciclops to build models trained on cross-platform transcriptome data for clinical outcome prediction

Designing robust, generalizable models based on cross-platform data to predict clinical outcomes remains challenging. Building explainable models is important because models may perform differently depending on the conditions of the samples. Here, we describe the use of Ciclops (cross-platform training in clinical outcome predictions), freely available software that can build explainable models to deliver across cross-platform datasets for predicting clinical outcomes. This protocol also utilizes SHAP, a post-training analysis allowing for assessing potential biomarkers of the clinical outcome under study.

For complete details on the use and execution of this protocol, please refer to Zhang et al. (2022).

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Build robust clinical outcome prediction models using cross-platform transcriptome data

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Applicable to datasets from different studies measuring different clinical outcomes

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Perform key preprocessing steps of imputation and cross-platform quantile normalization

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Analyze feature importance in LightGBM, XGBoost, and Random Forest models with SHAP

Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.