Proteomic analysis enables distinction of early- versus advanced-stage lung adenocarcinomas

Predictive model using four ferroptosis-related genes accurately predicts gastric cancer prognosis

BACKGROUND

Gastric cancer (GC) is a common malignancy of the digestive system. According to global 2018 cancer data, GC has the fifth-highest incidence and the third-highest fatality rate among malignant tumors. More than 60% of GC are linked to infection with Helicobacter pylori (H. pylori), a gram-negative, active, microaerophilic, and helical bacterium. This parasite induces GC by producing toxic factors, such as cytotoxin-related gene A, vacuolar cytotoxin A, and outer membrane proteins. Ferroptosis, or iron-dependent programmed cell death, has been linked to GC, although there has been little research on the link between H. pylori infection-related GC and ferroptosis.

AIM

To identify coregulated differentially expressed genes among ferroptosis-related genes (FRGs) in GC patients and develop a ferroptosis-related prognostic model with discrimination ability.

METHODS

Gene expression profiles of GC patients and those with H. pylori-associated GC were obtained from The Cancer Genome Atlas and Gene Expression Omnibus (GEO) databases. The FRGs were acquired from the FerrDb database. A ferroptosis-related gene prognostic index (FRGPI) was created using least absolute shrinkage and selection operator-Cox regression. The predictive ability of the FRGPI was validated in the GEO cohort. Finally, we verified the expression of the hub genes and the activity of the ferroptosis inducer FIN56 in GC cell lines and tissues.

RESULTS

Four hub genes were identified (NOX4, MTCH1, GABARAPL2, and SLC2A3) and shown to accurately predict GC and H. pylori-associated GC. The FRGPI based on the hub genes could independently predict GC patient survival; GC patients in the high-risk group had considerably worse overall survival than did those in the low-risk group. The FRGPI was a significant predictor of GC prognosis and was strongly correlated with disease progression. Moreover, the gene expression levels of common immune checkpoint proteins dramatically increased in the high-risk subgroup of the FRGPI cohort. The hub genes were also confirmed to be highly overexpressed in GC cell lines and tissues and were found to be primarily localized at the cell membrane. The ferroptosis inducer FIN56 inhibited GC cell proliferation in a dose-dependent manner.

CONCLUSION

In this study, we developed a predictive model based on four FRGs that can accurately predict the prognosis of GC patients and the efficacy of immunotherapy in this population.

Proteomic analyses identify HK1 and ATP5A to be overexpressed in distant metastases of lung adenocarcinomas compared to matched primary tumors

Lung cancer is the leading cause of cancer-related deaths worldwide with lung adenocarcinoma (LUAD) being the most common type. Genomic studies of LUAD have advanced our understanding of its tumor biology and accelerated targeted therapy. However, the proteomic characteristics of LUAD are still insufficiently explored. The prognosis for lung cancer patients is still mostly determined by the stage of disease at the time of diagnosis. Focusing on late-stage metastatic LUAD with poor prognosis, we compared the proteomic profiles of primary tumors and matched distant metastases to identify relevant and potentially druggable differences. We performed high-performance liquid chromatography (HPLC) and electrospray ionization tandem mass spectrometry (ESI-MS/MS) on a total of 38 FFPE (formalin-fixed and paraffin-embedded) samples. Using differential expression analysis and unsupervised clustering we identified several proteins that were differentially regulated in metastases compared to matched primary tumors. Selected proteins (HK1, ATP5A, SRI and ARHGDIB) were subjected to validation by immunoblotting. Thereby, significant differential expression could be confirmed for HK1 and ATP5A, both upregulated in metastases compared to matched primary tumors. Our findings give a better understanding of tumor progression and metastatic spreads in LUAD but also demonstrate considerable inter-individual heterogeneity on the proteomic level.

Optimization of reversed-phase solid-phase extraction for shotgun proteomics analysis

Reversed-phase solid-phase extraction (SPE) is the method of choice for the purification of proteomics samples. Even though the efficacy of SPE methods is sample type-dependent, the manufacturers' protocols are used in most studies. Using an optimized SPE method can lead to a substantial gain in identification and recovery. In this tutorial, we give a brief introduction to the most important parameters influencing SPE performance, and we present a short workflow (16 measurements) for optimizing the SPE procedure. This is complemented by method performance assessment instructions and a short troubleshooting guide to help users further understand and investigate their SPE methods.

In-depth proteomic analysis reveals unique subtype-specific signatures in human small-cell lung cancer

Background

Small‐cell lung cancer (SCLC) molecular subtypes have been primarily characterized based on the expression pattern of the following key transcription regulators: ASCL1 (SCLC‐A), NEUROD1 (SCLC‐N), POU2F3 (SCLC‐P) and YAP1 (SCLC‐Y). Here, we investigated the proteomic landscape of these molecular subsets with the aim to identify novel subtype‐specific proteins of diagnostic and therapeutic relevance.

Methods

Pellets and cell media of 26 human SCLC cell lines were subjected to label‐free shotgun proteomics for large‐scale protein identification and quantitation, followed by in‐depth bioinformatic analyses. Proteomic data were correlated with the cell lines’ phenotypic characteristics and with public transcriptomic data of SCLC cell lines and tissues.

Results

Our quantitative proteomic data highlighted that four molecular subtypes are clearly distinguishable at the protein level. The cell lines exhibited diverse neuroendocrine and epithelial–mesenchymal characteristics that varied by subtype. A total of 367 proteins were identified in the cell pellet and 34 in the culture media that showed significant up‐ or downregulation in one subtype, including known druggable proteins and potential blood‐based markers. Pathway enrichment analysis and parallel investigation of transcriptomics from SCLC cell lines outlined unique signatures for each subtype, such as upregulated oxidative phosphorylation in SCLC‐A, DNA replication in SCLC‐N, neurotrophin signalling in SCLC‐P and epithelial–mesenchymal transition in SCLC‐Y. Importantly, we identified the YAP1‐driven subtype as the most distinct SCLC subgroup. Using sparse partial least squares discriminant analysis, we identified proteins that clearly distinguish four SCLC subtypes based on their expression pattern, including potential diagnostic markers for SCLC‐Y (e.g. GPX8, PKD2 and UFO).

Conclusions

We report for the first time, the protein expression differences among SCLC subtypes. By shedding light on potential subtype‐specific therapeutic vulnerabilities and diagnostic biomarkers, our results may contribute to a better understanding of SCLC biology and the development of novel therapies.

Proteomic Analysis of Lung Cancer Types—A Pilot Study

Lung cancer is the leading cause of tumor-related mortality, therefore significant effort is directed towards understanding molecular alterations occurring at the origin of the disease to improve current treatment options. The aim of our pilot-scale study was to carry out a detailed proteomic analysis of formalin-fixed paraffin-embedded tissue sections from patients with small cell or non-small cell lung cancer (adenocarcinoma, squamous cell carcinoma, and large cell carcinoma). Tissue surface digestion was performed on relatively small cancerous and tumor-adjacent normal regions and differentially expressed proteins were identified using label-free quantitative mass spectrometry and subsequent statistical analysis. Principal component analysis clearly distinguished cancerous and cancer adjacent normal samples, while the four lung cancer types investigated had distinct molecular profiles and gene set enrichment analysis revealed specific dysregulated biological processes as well. Furthermore, proteins with altered expression unique to a specific lung cancer type were identified and could be the targets of future studies.

Domont G, C. S. Nogueira F, Marko-Varga G, Sanchez A. Mapping the Melanoma Plasma Proteome (MPP) Using Single-Shot Proteomics Interfaced with the WiMT Database

Plasma analysis by mass spectrometry-based proteomics remains a challenge due to its large dynamic range of 10 orders in magnitude. We created a methodology for protein identification known as Wise MS Transfer (WiMT). Melanoma plasma samples from biobank archives were directly analyzed using simple sample preparation. WiMT is based on MS1 features between several MS runs together with custom protein databases for ID generation. This entails a multi-level dynamic protein database with different immunodepletion strategies by applying single-shot proteomics. The highest number of melanoma plasma proteins from undepleted and unfractionated plasma was reported, mapping >1200 proteins from >10,000 protein sequences with confirmed significance scoring. Of these, more than 660 proteins were annotated by WiMT from the resulting ~5800 protein sequences. We could verify 4000 proteins by MS1t analysis from HeLA extracts. The WiMT platform provided an output in which 12 previously well-known candidate markers were identified. We also identified low-abundant proteins with functions related to (i) cell signaling, (ii) immune system regulators, and (iii) proteins regulating folding, sorting, and degradation, as well as (iv) vesicular transport proteins. WiMT holds the potential for use in large-scale screening studies with simple sample preparation, and can lead to the discovery of novel proteins with key melanoma disease functions.

Proteomic analysis enables distinction of early- versus advanced-stage lung adenocarcinomas

Background

A gel‐free proteomic approach was utilized to perform in‐depth tissue protein profiling of lung adenocarcinoma (ADC) and normal lung tissues from early and advanced stages of the disease. The long‐term goal of this study is to generate a large‐scale, label‐free proteomics dataset from histologically well‐classified lung ADC that can be used to increase further our understanding of disease progression and aid in identifying novel biomarkers.

Methods and results

Cases of early‐stage (I‐II) and advanced‐stage (III‐IV) lung ADCs were selected and paired with normal lung tissues from 22 patients. The histologically and clinically stratified human primary lung ADCs were analyzed by liquid chromatography‐tandem mass spectrometry. From the analysis of ADC and normal specimens, 4863 protein groups were identified. To examine the protein expression profile of ADC, a peak area‐based quantitation method was used. In early‐ and advanced‐stage ADC, 365 and 366 proteins were differentially expressed, respectively, between normal and tumor tissues (adjusted P‐value < .01, fold change ≥ 4). A total of 155 proteins were dysregulated between early‐ and advanced‐stage ADCs and 18 were suggested as early‐specific stage ADC. In silico functional analysis of the upregulated proteins in both tumor groups revealed that most of the enriched pathways are involved in mRNA metabolism. Furthermore, the most overrepresented pathways in the proteins that were unique to ADC are related to mRNA metabolic processes.

Conclusions

Further analysis of these data may provide an insight into the molecular pathways involved in disease etiology and may lead to the identification of biomarker candidates and potential targets for therapy. Our study provides potential diagnostic biomarkers for lung ADC and novel stage‐specific drug targets for rational intervention.