A proteomic profiling of laser-microdissected lung adenocarcinoma cells of early lepidic-types

Gene expression profiling using targeted RNA-sequencing to elucidate the progression from histologically normal lung tissues to non-invasive lesions in invasive lung adenocarcinoma

Lung adenocarcinoma (LUAD) shows heterogeneous morphological features and the stepwise progression from adenocarcinoma in situ to minimally invasive adenocarcinoma to invasive LUAD. Although multiple genetic alterations have been linked to the progression, the differences between the gene expression profiles of non-invasive lesions (non-ILs) and adjacent histologically normal lung (aNL) tissues within invasive LUAD have not been investigated. Herein, we analyzed differentially expressed genes (DEGs) specific to early-stage carcinogenesis in LUAD. Invasive LUAD tissue samples containing both non-ILs and aNL tissues were obtained from seven patients with pathological stage I LUAD, and each component was subjected to microdissection. Gene expression profiles of each component were determined using targeted RNA-sequencing. In total, 2536 DEGs, including 863 upregulated and 1673 downregulated genes, were identified in non-ILs. In non-ILs, the expression of SLC44A5, a choline transporter-like protein-coding gene, was significantly upregulated, and that of TMEM100, a gene encoding a transmembrane protein, was significantly downregulated. Reportedly, SLC44A5 plays an important role in the development and progression of hepatocellular carcinoma, whereas TMEM100 functions as a tumor suppressor in non-small cell lung cancer. Gene set enrichment analysis showed that DEGs in non-ILs were negatively enriched in cell death and immune response. Immunohistochemical analysis revealed that increased SLC44A5 expression and decreased TMEM100 expression were maintained in ILs. A protein-protein interaction (PPI) network analysis identified several upregulated and downregulated hub genes with high degrees in non-ILs. In conclusion, several new DEGs and key PPI network hub genes were identified in non-ILs, contributing to understanding of early-stage carcinogenesis in LUAD.

Proteomic Analyses Identify Differentially Expressed Proteins and Pathways Between Low-Risk and High-Risk Subtypes of Early-Stage Lung Adenocarcinoma and Their Prognostic Impacts

The histopathological subtype of lung adenocarcinoma (LUAD) is closely associated with prognosis. Micropapillary or solid predominant LUAD tends to relapse after surgery at an early stage, whereas lepidic pattern shows a favorable outcome. However, the molecular mechanism underlying this phenomenon remains unknown. Here, we recruited 31 lepidic predominant LUADs (LR: low-risk subtype group) and 28 micropapillary or solid predominant LUADs (HR: high-risk subtype group). Tissues of these cases were obtained and label-free quantitative proteomic and bioinformatic analyses were performed. Additionally, prognostic impact of targeted proteins was validated using The Cancer Genome Atlas databases (n = 492) and tissue microarrays composed of early-stage LUADs (n = 228). A total of 192 differentially expressed proteins were identified between tumor tissues of LR and HR and three clusters were identified via hierarchical clustering excluding eight proteins. Cluster 1 (65 proteins) showed a sequential decrease in expression from normal tissues to tumor tissues of LR and then to HR and was predominantly enriched in pathways such as tyrosine metabolism and ECM-receptor interaction, and increased matched mRNA expression of 18 proteins from this cluster predicted favorable prognosis. Cluster 2 (70 proteins) demonstrated a sequential increase in expression from normal tissues to tumor tissues of LR and then to HR and was mainly enriched in pathways such as extracellular organization, DNA replication and cell cycle, and high matched mRNA expression of 25 proteins indicated poor prognosis. Cluster 3 (49 proteins) showed high expression only in LR, with high matched mRNA expression of 20 proteins in this cluster indicating favorable prognosis. Furthermore, high expression of ERO1A and FEN1 at protein level predicted poor prognosis in early-stage LUAD, supporting the mRNA results. In conclusion, we discovered key differentially expressed proteins and pathways between low-risk and high-risk subtypes of early-stage LUAD. Some of these proteins could serve as potential biomarkers in prognostic evaluation.

A proteogenomic profile of early lung adenocarcinomas by protein co-expression network and genomic alteration analysis

The tumourigenesis of early lung adenocarcinomas, including adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and lepidic predominant invasive adenocarcinoma (LPA), remains unclear. This study aimed to capture disease-related molecular networks characterising each subtype and tumorigenesis by assessing 14 lung adenocarcinomas (AIS, five; MIA, five; LPA, four). Protein-protein interaction networks significant to the three subtypes were elucidated by weighted gene co-expression network analysis and pairwise G-statistics based analysis. Pathway enrichment analysis for AIS involved extracellular matrix proteoglycans and neutrophil degranulation pathway relating to tumour growth and angiogenesis. Whereas no direct networks were found for MIA, proteins significant to MIA were involved in oncogenic transformation, epithelial-mesenchymal transition, and detoxification in the lung. LPA was associated with pathways of HSF1-mediated heat shock response regulation, DNA damage repair, cell cycle regulation, and mitosis. Genomic alteration analysis suggested that LPA had both somatic mutations with loss of function and copy number gains more frequent than MIA. Oncogenic drivers were detected in both MIA and LPA, and also LPA had a higher degree of copy number loss than MIA. Our findings may help identifying potential therapeutic targets and developing therapeutic strategies to improve patient outcomes.

Current status of clinical proteogenomics in lung cancer

Introduction: Lung cancer is the leading cause of cancer death worldwide. Proteogenomics, a way to integrate genomics, transcriptomics, and proteomics, have emerged as a way to understand molecular causes in cancer tumorigenesis. This understanding will help identify therapeutic targets that are urgently needed to improve individual patient outcomes. Areas covered: To explore underlying molecular mechanisms of lung cancer subtypes, several efforts have used proteogenomic approaches that integrate next generation sequencing (NGS) and mass spectrometry (MS)-based technologies. Expert opinion: A large-scale, MS-based, proteomic analysis, together with both NGS-based genomic data and clinicopathological information, will facilitate establishing extensive databases for lung cancer subtypes that can be used for further proteogenomic analyzes. Proteogenomic strategies will further be understanding of how major driver mutations affect downstream molecular networks, resulting in lung cancer progression and malignancy, and how therapy-resistant cancers resistant are molecularly structured. These strategies require advanced bioinformatics based on a dynamic theory of network systems, rather than statistics, to accurately identify mutant proteins and their affected key networks.

Are There New Biomarkers in Tissue and Liquid Biopsies for the Early Detection of Non-Small Cell Lung Cancer?

Lung cancer is one of the most lethal malignancies worldwide, mainly due to its late diagnoses. The detection of molecular markers on samples provided from routine bronchoscopy including several liquid-based cytology tests (e.g., bronchoaspirate, bronchoalveolar lavage) and/or on easily obtained specimens such as sputum could represent a new approach to improve the sensitivity in lung cancer diagnoses. Recently growing interest has been reported for "noninvasive" liquid biopsy as a valuable source for molecular profiling. Unfortunately, a biomarker and/or composition of biomarkers capable of detecting early-stage lung cancer has yet to be discovered even if in the last few years there has been, through the use of revolutionary new technologies, an explosion of lung cancer biomarkers. Assay sensitivity and specificity need to be improved particularly when new approaches and/or tools are used. We have focused on the most important markers detected in tissue, and on several cytological specimens and liquid biopsies overall.

The imbalance in the complement system and its possible physiological mechanisms in patients with lung cancer

BACKGROUND

The clinical and experimental evidences for complement-cancer relationships are solid, whereas an epidemiological study reporting the imbalance of complement system in patients is still lacking.

METHODS

Using publicly available databases, we jointly compared the levels of complement components in plasma and lung cancer tissues. With iTRAQ proteomics, quantitative RT-PCR and western blotting, we analysed the differences in complement levels in lung cancer tissues and normal control tissues. Complement components are mainly synthesized by the liver and secreted into the blood. Using paired co-cultures of human normal QSG-7701 hepatocytes with lung cancer cells (A549, LTEP-α-2 or NCI-H1703) or human normal bronchial epithelial (HBE) cells, we examined the effects of lung cancer cells on complement synthesis and secretion in QSG-7701 hepatocytes.

RESULTS

An integrated analysis of transcriptome and proteome datasets from 43 previous studies revealed lower mRNA and protein levels of 25 complement and complement-related components in lung cancer tissues than those in normal control tissues; conversely, higher levels of complement proteins were detected in plasma from patients than those in healthy subjects. Our iTRAQ proteome study identified decreased and increased levels of 31 and 2 complement and complement-related proteins, respectively, in lung cancer tissues, of which the reduced levels of 10 components were further confirmed using quantitative RT-PCR and western blotting. Paired co-cultures of QSG-7701 hepatocytes with A549, LTEP-α-2, NCI-H1703 or HBE cells indicated that lung cancer cells increased complement synthesis and secretion in QSG-7701 cells compared to HBE cells.

CONCLUSIONS

The opposite associations between the levels of complement and complement-related components in lung cancer tissues and plasma from patients that have been repeatedly reported by independent publications may indicate the prevalence of an imbalance in the complement system of lung cancer patients. The possible mechanism of the imbalance may be associated not only with the decreased complement levels in lung cancer tissues but also the concurrent lung cancer tissue-induced increase in hepatocyte complement synthesis and plasma secretion in patients. And the imbalance should be accompanied by a suppression of complement-dependent immunity in lung cancer tissues coupled with a burden of complement immunity in the circulation of patients.

Differential Proteomic Analysis between Small Cell Lung Carcinoma (SCLC) and Pulmonary Carcinoid Tumors Reveals Molecular Signatures for Malignancy in Lung Cancer

PURPOSE

The molecular underpinnings that may prognosticate survival and increase our understanding of tumor development and progression are still poorly understood. This study aimed to define the molecular signatures for malignancy in small cell lung carcinoma (SCLC), which is known for its highly aggressive clinical features and poor prognosis.

EXPERIMENTAL DESIGN

Using clinical specimens, the authors perform a comparative proteomic analysis of high-grade SCLCs and low-grade pulmonary carcinoid tumors (PCTs), both of which are types of neuroendocrine tumors. A label-free LC-MS-based quantitative proteomic analysis is applied to tumor cells laser-microdissected from their formalin-fixed paraffin-embedded (FFPE) tissues obtained from six patients each.

RESULTS

Overall, 1991 proteins are identified from tumor cells in the FFPE tissues. Through the protein-protein interaction network analysis of 201 proteins significantly, the authors find that SCLC is functionally characterized by activation of molecular pathways for spliceosome, RNA transport, and DNA replication and cell cycle. Particularly, 11 proteins involved in tumor proliferation (MCM2, 4, 6, 7, and MSH2), metastasis (RCC2, CORO1C, CHD4, and IPO9), and cancer metabolism (PHGDH and TYMP) are identified as SCLC-specific proteins. Furthermore, their prognostic significances are demonstrated by online Kaplan-Meier survival analysis.

CONCLUSIONS AND CLINICAL RELEVANCE

These clinical tissue proteomic approach for SCLC reveals the proteins associated with aggressiveness and poor prognosis. The identified SCLC-specific proteins represent potential therapeutic targets. Moreover, MCMs and PHGDH can be poor prognostic factors for lung cancer.

Clinical value of miR-145-5p in NSCLC and potential molecular mechanism exploration: A retrospective study based on GEO, qRT-PCR, and TCGA data

MicroRNAs have been reported to be involved in various biological processes. Here, we performed a systematic analysis to explore the clinical value and potential molecular mechanism of miR-145-5p in non-small cell lung cancer. First, a meta-analysis was performed with eligible literature, followed by microRNA microarrays in the Gene Expression Omnibus database, to verify the diagnostic and prognostic values of miR-145-5p. A cohort of 125 clinical paired non-small cell lung cancer samples was next used to detect the level of miR-145-5p and to explore the relationship of miR-145-5p with clinicopathological parameters. The Cancer Genome Atlas database was additionally applied to investigate the role of miR-145-5p in non-small cell lung cancer. The potential targets of miR-145-5p were predicted using 12 online prediction databases to explore the prospective molecular mechanism of miR-145-5p in non-small cell lung cancer. The expression of miR-145-5p in non-small cell lung cancer was significantly lower than that in healthy tissues. And miR-145-5p tended to show better diagnostic performance in lung squamous cell carcinoma than in lung adenocarcinoma. Furthermore, the expression of miR-145-5p was closely associated with lymph node metastasis in non-small cell lung cancer. Gene ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analysis revealed that the target genes were mainly enriched with enzyme-linked receptor protein signaling pathways, SH3 domain binding, cell leading edge, and adherens junction. The protein-protein interaction network showed that eight hub genes (SMAD4, SMAD2, IRS1, FOXO1, ERBB4, NRAS, ACTB, and ACTG1) might be the key target genes of miR-145-5p in non-small cell lung cancer. The information we obtained might offer new perspectives for clinical diagnosis and treatment for non-small cell lung cancer.

Recent mass spectrometry-based proteomics for biomarker discovery in lung cancer, COPD, and asthma

INTRODUCTION

Lung cancer and related diseases have been one of the most common causes of deaths worldwide. Genomic-based biomarkers may hardly reflect the underlying dynamic molecular mechanism of functional protein interactions, which is the center of a disease. Recent developments in mass spectrometry (MS) have made it possible to analyze disease-relevant proteins expressed in clinical specimens by proteomic challenges. Areas covered: To understand the molecular mechanisms of lung cancer and its subtypes, chronic obstructive pulmonary disease (COPD), asthma and others, great efforts have been taken to identify numerous relevant proteins by MS-based clinical proteomic approaches. Since lung cancer is a multifactorial disease that is biologically associated with asthma and COPD among various lung diseases, this study focused on proteomic studies on biomarker discovery using various clinical specimens for lung cancer, COPD, and asthma. Expert commentary: MS-based exploratory proteomics utilizing clinical specimens, which can incorporate both experimental and bioinformatic analysis of protein-protein interaction and also can adopt proteogenomic approaches, makes it possible to reveal molecular networks that are relevant to a disease subgroup and that could differentiate between drug responders and non-responders, good and poor prognoses, drug resistance, and so on.

Developments of mass spectrometry-based technologies for effective drug development linked with clinical proteomes

A strong demand in drug discovery and development today is to overcome "Big Gaps" encountered by differences in species and races, to accelerate effective developments in cost and time, and to meet medical needs. Moreover, drugs of various types have emerged which cover middle-size molecules and polymers rather than conventional small molecules. Upon those challenges, mass spectrometry (MS)-based technologies, which will be described in this paper, will play an increasingly important role, among which the liquid chromatography-tandem mass spectrometry (LC/MS/MS) platform will be powerful as rapid and molecule-based analysis more than ever. nanoPore Optical Interferometry (nPOI) newly introduced can detect even weak interactions in protein-protein and protein-compound, and can be connected directly to LC/MS/MS for identification of binding molecular species, which will be quite useful for affinity ranking and high-throughput interaction screening. Imaging MS provides the molecular information and spatial distribution of targeted molecules within a tissue specimen. MS-based clinical proteomics utilizing clinical specimens and empowered by advanced bioinformatics can attain both key protein-protein interaction (PPI) networks with major protein players responsible for functional mechanisms of a disease subtype. An integration of those MS-based technologies will deliver a seamless platform of drug development from molecules identified in human clinical specimens.

The development of precision medicine in clinical practice

Precision medicine allows a dramatic expansion of biological data, while there is still an urgent need to understand and insight the exact meaning of those data to human health and disease. This has led to an increasing wealth of data unanalyzed. The concept of precision medicine is about the customization of healthcare, with decisions and practices tailored to an individual patient based on their intrinsic biology in addition to clinical “signs and symptoms”. Construction of a standardized model for the integration of data from various platforms is the central mission of the ‘New Disease Management Model’. The model is helpful for the development of new taxonomy of diseases and subtypes, to personalize therapy based on patient genetic profiles. A rapid progression of precision therapy has been made recently. Clinical trials have shown the therapeutic efficacy of discovered and developed therapeutic agents has improved. However, next-generation drugs would be designed for disease subtypes with more specificity, efficacy and lower toxicity.