Identification and Characterization of Potential Biomarkers by Quantitative Tissue Proteomics of Primary Lung Adenocarcinoma

Omics-based molecular classifications empowering in precision oncology

BACKGROUND

In the past decades, cancer enigmatical heterogeneity at distinct expression levels could interpret disparities in therapeutic response and prognosis. It built hindrances to precision medicine, a tactic to tailor customized treatment informed by the tumors' molecular profile. Single-omics analysis dissected the biological features associated with carcinogenesis to some extent but still failed to revolutionize cancer treatment as expected. Integrated omics analysis incorporated tumor biological networks from diverse layers and deciphered a holistic overview of cancer behaviors, yielding precise molecular classification to facilitate the evolution and refinement of precision medicine.

CONCLUSION

This review outlined the biomarkers at multiple expression layers to tutor molecular classification and pinpoint tumor diagnosis, and explored the paradigm shift in precision therapy: from single- to multi-omics-based subtyping to optimize therapeutic regimens. Ultimately, we firmly believe that by parsing molecular characteristics, omics-based typing will be a powerful assistant for precision oncology.

Multiplex analysis for the identification of plasma protein biomarkers for predicting lung cancer immunotherapy response

Background

Programmed death-ligand (PD-L1) expression serves as a predictive biomarker for immune checkpoint inhibitor (ICI) sensitivity in non-small cell lung cancer (NSCLC). Nevertheless, the development of biomarkers that reliably predict ICI response remains an ongoing endeavor due to imperfections in existing methodologies.

Objectives

ICIs have led to a new paradigm in the treatment of NSCLC. The current companion PD-L1 diagnostics are insufficient in predicting ICI response. Therefore, we sought whether the Olink platform could be applied to predict response to ICIs in NSCLC.

Design

We collected blood samples from patients with NSCLC before ICI treatment and retrospectively analyzed proteomes based on their response to ICI.

Methods

Overall, 76 NSCLC patients' samples were analyzed. Proteomic plasma analysis was performed using the Olink platform. Intraplate reproducibility, validation, and statistical analyses using elastic net regression and generalized linear models with clinical parameters were evaluated.

Results

Intraplate coefficient of variation (CV) assays ranged from 3% to 6%, and the interplate CV was 14%. In addition, the Pearson correlation coefficient of the Olink Normalized Protein eXpression data was validated. No statistical differences were observed in the analyses of progressive disease and response to ICIs. Furthermore, no single proteome showed prognostic value in terms of progression-free survival.

Conclusion

In this study, the proximity extension assay-based approach of the Olink panel could not predict the patient's response to ICIs. Our proteomic analysis failed to achieve predictive value in both response or progression to ICIs and progression-free survival (PFS).

Biological mechanisms and clinical significance of endoplasmic reticulum oxidoreductase 1 alpha (ERO1α) in human cancer

A firm link between endoplasmic reticulum (ER) stress and tumors has been wildly reported. Endoplasmic reticulum oxidoreductase 1 alpha (ERO1α), an ER-resident thiol oxidoreductase, is confirmed to be highly upregulated in various cancer types and associated with a significantly worse prognosis. Of importance, under ER stress, the functional interplay of ERO1α/PDI axis plays a pivotal role to orchestrate proper protein folding and other key processes. Multiple lines of evidence propose ERO1α as an attractive potential target for cancer treatment. However, the unavailability of specific inhibitor for ERO1α, its molecular inter-relatedness with closely related paralog ERO1β and the tightly regulated processes with other members of flavoenzyme family of enzymes, raises several concerns about its clinical translation. Herein, we have provided a detailed description of ERO1α in human cancers and its vulnerability towards the aforementioned concerns. Besides, we have discussed a few key considerations that may improve our understanding about ERO1α in tumors.

Developing technologies and areas of interest in lung cancer screening adjuncts

Lung cancer remains the leading cause of cancer mortality. Screening guidelines have been implemented in the past decade to aid in earlier detection of at-risk groups. Nevertheless, computed tomography (CT) scans, the principal screening modality in use today, are still low yield, with 3.6% of lung cancer confirmed amongst 39.1% of lesions detected over a 3-year period. They also carry relatively high false positive rates, between 9% and 27%, which can bear unnecessary financial and emotional costs to patients. As such, research efforts have been dedicated to the development of lung cancer screening adjuncts to improve detection reliability. We herein review several emerging technologies in this specific arena and their efficacy. These include plasma markers (microDNA, DNA methylation, and tumor-associated antibodies), breath/sputum biomarkers [volatile organic compounds (VOCs) and exhaled breath condensate (EBC)], proteomics, metabolomics, and machine learning, such as radiomics technology. We find that, across the board, they offer promising results in terms of non-invasive diagnostics, genetic sequencing for higher-risk individuals, and accessibility for a diverse cohort of patients. While these screening adjuncts are unlikely to completely replace the current standard of care at the moment, continued research into these technologies is crucial to improve and personalize the identification, treatment, and outcome of lung cancer patients in the near future.

Comprehensive analysis of prognostic value and immune infiltration of Regulator of Chromosome Condensation 2 in lung adenocarcinoma

Background: Lung adenocarcinoma (LUAD) incidence and mortality take the leading place of most malignancies. Previous studies have revealed the regulator of chromosome condensation 1 (RCC1) family members played an essential role during tumorigenesis. However, its biological functions in LUAD still need further investigation. Methods: Several databases were applied to explore potential effects of RCC1 family members on LUAD, such as Oncomine, GEPIA, and cBioPortal. Real-time PCR and immunohistochemistry were used to verify the expression of RCC2 in stage I LUAD. H1975 and A549 were selected to explore the biological function of RCC2 in cellular malignant phenotype. Results: The expressions of RCC1 and RCC2 showed marked differences in malignant tissue compared to lung tissue. The higher the expression levels of RCC1 or RCC2 in LUAD patients, the shorter their overall survival (OS). In normal lung tissues, RCC1 expression was highly enriched in alveolar cells and endothelial cells. Compare with RCC1, RCC2 expression in normal lung tissue was significantly enriched in macrophages, B cells and granulocytes. Additionally, RCC2 expression level was correlated with multiple immune cell infiltration in LUAD. Moreover, the mutation or different sCNA status of RCC2 exerted influence on multiple immune cell infiltration distribution. We found that the upregulation of RCC1 and RCC2 were obviously related to TP53 mutation. GSEA analysis revealed that RCC2 was involved in the process of DNA replication, nucleotide excision repair and cell cycle, which might affect tumor progression through P53 signaling pathway. We further elucidated that downregulation of RCC2 could dramatically repress the migration and invasion of LUAD cells. Conclusions: The study demonstrated that RCC1 and RCC2 expression were markedly increased in early-stage of LUAD. Patients with high expression of RCC1 or RCC2 had a worse prognosis. Based on our analysis, RCC1 and RCC2 might exert influence on LUAD process through DNA replication, nucleotide excision repair and cell cycle, as well as cells migration and invasion. Different from RCC1, RCC2 also involved in immune infiltration. These analyses provided a novel insight into the identification of diagnostic biomarker.

Prognostic biomarker discovery based on proteome landscape of Chinese lung adenocarcinoma

Despite recent innovations in imaging and genomic screening promotes advance in diagnosis and treatment of lung adenocarcinoma (LUAD), there remains high mortality of LUAD and insufficient understanding of LUAD biology. Our previous study performed an integrative multi-omic analysis of LUAD, filling the gap between genomic alterations and their biological proteome effects. However, more detailed molecular characterization and biomarker resources at proteome level still need to be uncovered. In this study, a quantitative proteomic experiment of patient-derived benign lung disease samples was carried out. After that, we integrated the proteomic data with previous dataset of 103 paired LUAD samples. We depicted the proteomic differences between non-cancerous and tumor samples and among diverse pathological subtypes. We also found that up-regulated mitophagy was a significant characteristic of early-stage LUAD. Additionally, our integrative analysis filtered out 75 potential prognostic biomarkers and validated two of them in an independent LUAD serum cohort. This study provided insights for improved understanding proteome abnormalities of LUAD and the novel prognostic biomarker discovery offered an opportunity for LUAD precise management.

Integrative Multianalytical Model Based on Novel Plasma Protein Biomarkers for Distinguishing Lung Adenocarcinoma and Benign Pulmonary Nodules

Given the pressing clinical problem of making a decision in diagnosis for subjects with pulmonary nodules, we aimed to discover novel plasma protein biomarkers for lung adenocarcinoma (LUAD) and benign pulmonary nodules (BPNs) and then develop an integrative multianalytical model to guide the clinical management of LUAD and BPN patients. Through label-free quantitative plasma proteomic analysis (data are available via ProteomeXchange with identifier PXD046731), 12 differentially expressed proteins (DEPs) in LUAD and BPN were screened. The diagnostic abilities of DEPs were validated in two independent validation cohorts. The results showed that the levels of three candidate proteins (PRDX2, PON1, and APOC3) were lower in the plasma of LUAD than in BPN. The three candidate proteins were combined with three promising computed tomography indicators (spiculation, vascular notch sign, and lobulation) and three traditional markers (CEA, CA125, and CYFRA21-1) to construct an integrative multianalytical model, which was effective in distinguishing LUAD from BPN, with an AUC of 0.904, a sensitivity of 81.44%, and a specificity of 90.14%. Moreover, the model possessed impressive diagnostic performance between early LUADs and BPNs, with the AUC, sensitivity, specificity, and accuracy of 0.868, 65.63%, 90.14%, and 82.52%, respectively. This model may be a useful auxiliary diagnostic tool for LUAD and BPN by achieving a better balance of sensitivity and specificity.

Role of immune related genes in predicting prognosis and immune response in patients with hepatocellular carcinoma

Immunotherapy has developed rapidly in recent years. This study aimed to establish a prognostic signature for immune-related genes (IRGs) and explore related potential immunotherapies. The RNA-seq transcriptome profiles and clinicopathological information of patients were obtained from The Cancer Genome Atlas. Differentially expressed IRGs in tumors and normal tissues were screened and a risk score signature was constructed to predict the prognosis in patients with hepatocellular carcinoma (HCC). Receiver operating characteristic curves, survival analyses, and correlation analyses were used to explore the clinical application of this model. We further analyzed the differences in clinical characteristics, immune infiltration, somatic mutations, and treatment sensitivity between the high- and low-risk populations characterized by the prognostic models. The immune cell infiltration score and immune-related pathway activity were calculated using the single sample gene set enrichment analysis (ssGSEA) set enrichment analysis. Gene ontology (GO), Kyoto encyclopedia of genes and genomes, and GSEA were used to explore the underlying mechanisms. We constructed a nine-IRG formula to predict the prognosis in HCC patients. The higher the risk score, the higher the malignancy of the tumor and the worse the prognosis. There were significant differences in immune related processes between the high- and low-risk groups. TP53 and CTNNB1 mutations were significantly different between different risk groups. The expression of model gene was closely related to the sensitivity of tumor cells to chemotherapeutic drugs. This risk score model, which is helpful for the individualized treatment of patients with different risk factors, could be a reliable prognostic tool for HCC patients.

A novel DNA methylation signature revealed GDF6 and RCC1 as potential prognostic biomarkers correlated with cell proliferation in clear cell renal cell carcinoma

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) accounts for the majority (80%-90%) of renal cell carcinoma (RCC) patients at the time of diagnosis, and approximately 15% of ccRCC patients will develop distant metastasis or recurrence during their lifetime. Increasing number of studies have revealed that the aberrant DNA methylations is closely correlated with the tumorigenesis in ccRCC.

RESULTS

In this study, we utilized a LASSO (least absolute shrinkage and selection operator) model to identify a combination of 13 probes-based DNA methylation signature that associated with the progression-free survival (PFS) of ccRCC patients. First, differentially methylated regions (CpGs) related to PFS and phenotypes were identified. Next, prognostic DNA methylation probes were selected from the differentially methylated probes (DMPs) and calculated risk scores to stratify patients with ccRCC. The performance of this signature was validated in an independent testing set using various analyses, including Kaplan-Meier analysis for PFS and receiver operating characteristic (ROC) curve analysis. Based on our 13-DNA methylation probes signature, ccRCC patients were successfully stratified into high- and low-risk groups. Combining DNA methylation signature with clinical variables such as T stage, M stage and tumor grade could further improve the accuracy of prediction. Moreover, we highlight two molecular biomarkers (RCC1 and GDF6) corresponding to our probes. Invitro experiments showed that knockdown of RCC1 or GDF6 in ccRCC cell lines reduced cell proliferation, which indicated that both biomarkers are associated with tumorigenesis.

CONCLUSIONS

The 13-probes-based DNA methylation signature has the potential to serve as an independent tool for survival outcome improvement and treatment strategy selection for ccRCC patients. In addition, our findings suggest that RCC1 and GDF6 may serve as promising markers for ccRCC.

Molecular Mechanisms of IL18 in Disease

Interleukin 18 (IL18) was originally identified as an inflammation-induced cytokine that is secreted by immune cells. An increasing number of studies have focused on its non-immunological functions, with demonstrated functions for IL18 in energy homeostasis and neural stability. IL18 is reportedly required for lipid metabolism in the liver and brown adipose tissue. Furthermore, IL18 (Il18) deficiency in mice leads to mitochondrial dysfunction in hippocampal cells, resulting in depressive-like symptoms and cognitive impairment. Microarray analyses of Il18-/- mice have revealed a set of genes with differential expression in liver, brown adipose tissue, and brain; however, the impact of IL18 deficiency in these tissues remains uncertain. In this review article, we discuss these genes, with a focus on their relationships with the phenotypic disease traits of Il18-/- mice.

Lung proteomics combined with metabolomics reveals molecular characteristics of inflammation-related lung tumorigenesis induced by B(a)P and LPS

Inflammatory microenvironment may take a promoting role in lung tumorigenesis. However, the molecular characteristics underlying inflammation-related lung cancer remains unknown. In this work, the inflammation-related lung tumorigenesis mouse model was established by treated with B(a)P (1 mg/mouse, once a week for 4 weeks), followed by LPS (2.5 μg/mouse, once every 3 weeks for five times), the mice were sacrificed 30 weeks after exposure. TMT-labeled quantitative proteomics and untargeted metabolomics were used to interrogate differentially expressed proteins and metabolites in different mouse cancer tissues, followed by integrated crosstalk between proteomics and metabolomics through Spearman's correlation analysis. The result showed that compared with the control group, 103 proteins and 37 metabolites in B(a)P/LPS group were identified as significantly altered. By searching KEGG pathway database, proteomics pathways such as Leishmaniasis, Asthma and Intestinal immune network for IgA production, metabolomics pathways such as Vascular smooth muscle contraction, Linoleic acid metabolism and cGMP-PKG signaling pathway were enriched. A total of 22 pathways were enriched after conjoint analysis of the proteomic and metabolomics, and purine metabolism pathway, the unique metabolism-related pathway, which included significantly altered protein (adenylate cyclase 4, ADCY4) and metabolites (L-Glutamine, guanosine monophosphate (GMP), adenosine and guanosine) was found. Results suggested purine metabolism may contribute to the inflammation-related lung tumorigenesis, which may provide novel clues for the therapeutic strategies of inflammation-related lung cancer.

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.

Potential biomarkers for predicting immune response and outcomes in lung cancer patients undergoing thermal ablation

Thermal ablation is a promising alternative treatment for lung cancer. It disintegrates cancer cells and releases antigens, followed by the remodeling of local tumor immune microenvironment and the activation of anti-tumor immune responses, enhancing the overall effectiveness of the treatment. Biomarkers can offer insights into the patient's immune response and outcomes, such as local tumor control, recurrence, overall survival, and progression-free survival. Identifying and validating such biomarkers can significantly impact clinical decision-making, leading to personalized treatment strategies and improved patient outcomes. This review provides a comprehensive overview of the current state of research on potential biomarkers for predicting immune response and outcomes in lung cancer patients undergoing thermal ablation, including their potential role in lung cancer management, and the challenges and future directions.

Inhibition of HSP90 in Driver Oncogene-Defined Lung Adenocarcinoma Cell Lines: Key Proteins Underpinning Therapeutic Efficacy

The use of 90 kDa heat shock protein (HSP90) inhibition as a therapy in lung adenocarcinoma remains limited due to moderate drug efficacy, the emergence of drug resistance, and early tumor recurrence. The main objective of this research is to maximize treatment efficacy in lung adenocarcinoma by identifying key proteins underlying HSP90 inhibition according to molecular background, and to search for potential biomarkers of response to this therapeutic strategy. Inhibition of the HSP90 chaperone was evaluated in different lung adenocarcinoma cell lines representing the most relevant molecular alterations (EGFR mutations, KRAS mutations, or EML4-ALK translocation) and wild-type genes found in each tumor subtype. The proteomic technique iTRAQ was used to identify proteomic profiles and determine which biological pathways are involved in the response to HSP90 inhibition in lung adenocarcinoma. We corroborated the greater efficacy of HSP90 inhibition in EGFR mutated or EML4-ALK translocated cell lines. We identified proteins specifically and significantly deregulated after HSP90 inhibition for each molecular alteration. Two proteins, ADI1 and RRP1, showed independently deregulated molecular patterns. Functional annotation of the altered proteins suggested that apoptosis was the only pathway affected by HSP90 inhibition across all molecular subgroups. The expression of ADI1 and RRP1 could be used to monitor the correct inhibition of HSP90 in lung adenocarcinoma. In addition, proteins such as ASS1, ITCH, or UBE2L3 involved in pathways related to the inhibition of a particular molecular background could be used as potential response biomarkers, thereby improving the efficacy of this therapeutic approach to combat lung adenocarcinoma.

The Comparison of Serum Exosome Protein Profile in Diagnosis of NSCLC Patients

A thorough study of the exosomal proteomic cargo may enable the identification of proteins that play an important role in cancer development. The aim of this study was to compare the protein profiles of the serum exosomes derived from non-small lung cancer (NSCLC) patients and healthy volunteers (control) using the high-performance liquid chromatography coupled to mass spectrometry (HPLC-MS) method to identify potentially new diagnostic and/or prognostic protein biomarkers. Proteins exclusively identified in NSCLC and control groups were analyzed using several bioinformatic tools and platforms (FunRich, Vesiclepedia, STRING, and TIMER2.0) to find key protein hubs involved in NSCLC progression and the acquisition of metastatic potential. This analysis revealed 150 NSCLC proteins, which are significantly involved in osmoregulation, cell-cell adhesion, cell motility, and differentiation. Among them, 3 proteins: Interleukin-34 (IL-34), HLA class II histocompatibility antigen, DM alpha chain (HLA-DMA), and HLA class II histocompatibility antigen, DO beta chain (HLA-DOB) were shown to be significantly involved in the cancer-associated fibroblasts (CAFs) and tumor-associated macrophages (TAMs) infiltration processes. Additionally, detected proteins were analyzed according to the presence of lymph node metastasis, showing that differences in frequency of detection of protein FAM166B, killer cell immunoglobulin-like receptor 2DL1, and olfactory receptor 52R1 correlate with the N feature according to the TNM Classification of Malignant Tumors. These results prove their involvement in NSCLC lymph node spread and metastasis. However, this study requires further investigation.

Quantitative proteomics identifies tumour matrisome signatures in patients with non-small cell lung cancer

Introduction

The composition and remodelling of the extracellular matrix (ECM) are important factors in the development and progression of cancers, and the ECM is implicated in promoting tumour growth and restricting anti-tumour therapies through multiple mechanisms. The characterisation of differences in ECM composition between normal and diseased tissues may aid in identifying novel diagnostic markers, prognostic indicators and therapeutic targets for drug development.

Methods

Using tissue from non-small cell lung cancer (NSCLC) patients undergoing curative intent surgery, we characterised quantitative tumour-specific ECM proteome signatures by mass spectrometry.

Results

We identified 161 matrisome proteins differentially regulated between tumour tissue and nearby non-malignant lung tissue, and we defined a collagen hydroxylation functional protein network that is enriched in the lung tumour microenvironment. We validated two novel putative extracellular markers of NSCLC, the collagen cross-linking enzyme peroxidasin and a disintegrin and metalloproteinase with thrombospondin motifs 16 (ADAMTS16), for discrimination of malignant and non-malignant lung tissue. These proteins were up-regulated in lung tumour samples, and high PXDN and ADAMTS16 gene expression was associated with shorter survival of lung adenocarcinoma and squamous cell carcinoma patients, respectively.

Discussion

These data chart extensive remodelling of the lung extracellular niche and reveal tumour matrisome signatures in human NSCLC.

Prediction-oriented prognostic biomarker discovery with survival machine learning methods

Identifying novel and reliable prognostic biomarkers for predicting patient survival outcomes is essential for deciding personalized treatment strategies for diseases such as cancer. Numerous feature selection techniques have been proposed to address the high-dimensional problem in constructing prediction models. Not only does feature selection lower the data dimension, but it also improves the prediction accuracy of the resulted models by mitigating overfitting. The performances of these feature selection methods when applied to survival models, on the other hand, deserve further investigation. In this paper, we construct and compare a series of prediction-oriented biomarker selection frameworks by leveraging recent machine learning algorithms, including random survival forests, extreme gradient boosting, light gradient boosting and deep learning-based survival models. Additionally, we adapt the recently proposed prediction-oriented marker selection (PROMISE) to a survival model (PROMISE-Cox) as a benchmark approach. Our simulation studies indicate that boosting-based approaches tend to provide superior accuracy with better true positive rate and false positive rate in more complicated scenarios. For demonstration purpose, we applied the proposed biomarker selection strategies to identify prognostic biomarkers in different modalities of head and neck cancer data.

Micro-ribonucleic acids (miRNAs) and a proteomic profile in lung adenocarcinoma cases with brain metastasis

Background

Brain metastasis (BM) is the main cause of death of individuals with lung adenocarcinoma (LAC). Biomarkers with high sensitivity and specificity for the early detection and treatment of BM of LAC urgently need to be identified. In this study, we analyzed the pathogenesis of LAC-induced BM by detecting micro-ribonucleic acid (miRNA) and proteome expression differences between primary LAC lesion and BM tissue specimens to identify biomarkers of LAC-associated BM and develop potential therapeutic targets.

Methods

The miRNA and protein profiles of non-metastatic primary LAC and BM cases were examined to further explore the mechanism of BM. The roles and interactions of differential miRNAs and proteins were subject to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The interactions of differential miRNAs and proteins were analyzed by R software and depicted using Cytoscape.

Results

Compared to the LAC tissue specimens, 16 and 4 miRNAs showed increased and reduced levels, respectively, in the BM tissue specimens, and 53 proteins were upregulated, and 35 proteins were downregulated. The enrichment pathway analysis showed the nuclear factor kappa B (NF-κB) signaling and the primary immunodeficiency pathways played important roles in the pathogenetic mechanisms of BM in LAC.

Conclusions

This study extended understandings of the regulatory network of miRNAs and proteins and provided novel insights into the pathogenic mechanisms of BM in LAC at the miRNA and protein levels.

The Role of Genomics and Proteomics in Lung Cancer Early Detection and Treatment

Lung cancer is the leading cause of cancer-related death worldwide, with non-small-cell lung cancer (NSCLC) being the primary type. Unfortunately, it is often diagnosed at advanced stages, when therapy leaves patients with a dismal prognosis. Despite the advances in genomics and proteomics in the past decade, leading to progress in developing tools for early diagnosis, targeted therapies have shown promising results; however, the 5-year survival of NSCLC patients is only about 15%. Low-dose computed tomography or chest X-ray are the main types of screening tools. Lung cancer patients without specific, actionable mutations are currently treated with conventional therapies, such as platinum-based chemotherapy; however, resistances and relapses often occur in these patients. More noninvasive, inexpensive, and safer diagnostic methods based on novel biomarkers for NSCLC are of paramount importance. In the current review, we summarize genomic and proteomic biomarkers utilized for the early detection and treatment of NSCLC. We further discuss future opportunities to improve biomarkers for early detection and the effective treatment of NSCLC.

Advances in the application of proteomics in lung cancer

Although the incidence and mortality of lung cancer have decreased significantly in the past decade, it is still one of the leading causes of death, which greatly impairs people’s life and health. Proteomics is an emerging technology that involves the application of techniques for identifying and quantifying the overall proteins in cells, tissues and organisms, and can be combined with genomics, transcriptomics to form a multi-omics research model. By comparing the content of proteins between normal and tumor tissues, proteomics can be applied to different clinical aspects like diagnosis, treatment, and prognosis, especially the exploration of disease biomarkers and therapeutic targets. The applications of proteomics have promoted the research on lung cancer. To figure out potential applications of proteomics associated with lung cancer, we summarized the role of proteomics in studies about tumorigenesis, diagnosis, prognosis, treatment and resistance of lung cancer in this review, which will provide guidance for more rational application of proteomics and potential therapeutic strategies of lung cancer.

Comparative Assessment of Quantification Methods for Tumor Tissue Phosphoproteomics

With increasing sensitivity and accuracy in mass spectrometry, the tumor phosphoproteome is getting into reach. However, the selection of quantitation techniques best-suited to the biomedical question and diagnostic requirements remains a trial and error decision as no study has directly compared their performance for tumor tissue phosphoproteomics. We compared label-free quantification (LFQ), spike-in-SILAC (stable isotope labeling by amino acids in cell culture), and tandem mass tag (TMT) isobaric tandem mass tags technology for quantitative phosphosite profiling in tumor tissue. Compared to the classic SILAC method, spike-in-SILAC is not limited to cell culture analysis, making it suitable for quantitative analysis of tumor tissue samples. TMT offered the lowest accuracy and the highest precision and robustness toward different phosphosite abundances and matrices. Spike-in-SILAC offered the best compromise between these features but suffered from a low phosphosite coverage. LFQ offered the lowest precision but the highest number of identifications. Both spike-in-SILAC and LFQ presented susceptibility to matrix effects. Match between run (MBR)-based analysis enhanced the phosphosite coverage across technical replicates in LFQ and spike-in-SILAC but further reduced the precision and robustness of quantification. The choice of quantitative methodology is critical for both study design such as sample size in sample groups and quantified phosphosites and comparison of published cancer phosphoproteomes. Using ovarian cancer tissue as an example, our study builds a resource for the design and analysis of quantitative phosphoproteomic studies in cancer research and diagnostics.

Interleukin-11: A Potential Biomarker and Molecular Therapeutic Target in Non-Small Cell Lung Cancer

Non-small cell lung cancer (NSCLC) accounts for 85% of lung cancer and is a fast progressive disease when left untreated. Identification of potential biomarkers in NSCLC is an ongoing area of research that aims to detect, diagnose, and prognosticate patients early to optimize treatment. We review the role of interleukin-11 (IL11), a stromal-cell derived pleiotropic cytokine with profibrotic and cellular remodeling properties, as a potential biomarker in NSCLC. This review identifies the need for biomarkers in NSCLC, the potential sources of IL11, and summarizes the available information leveraging upon published literature, publicly available datasets, and online tools. We identify accumulating evidence suggesting IL11 to be a potential biomarker in NSCLC patients. Further in-depth studies into the pathophysiological effects of IL11 on stromal-tumor interaction in NSCLC are warranted and current available literature highlights the potential value of IL11 detection as a diagnostic and prognostic biomarker in NSCLC.

ERO1L promotes the proliferation and metastasis of lung adenocarcinoma via the Wnt2/β-catenin signaling pathway

PURPOSE

This study aimed to explore the role and underlying mechanism of action of Endoplasmic reticulum oxidoreductin-1 L (ERO1L) in lung adenocarcinoma (LUAD).

MATERIALS AND METHODS

The Gene expression profiling interactive analysis database was used to analyze the expression of ERO1L in LUAD cases. The expression of ERO1L and Wnt2 in LUAD tissue was evaluated using immunohistochemistry. We also used western blotting to assess the expression of ERO1L or Wnt2 and the phosphorylation of β-catenin in LUAD cell lines. Plasmid transfection and small interfering RNA were used for overexpression and knockdown of ERO1L in LUAD cells, respectively. The proliferation, invasion and migration of LUAD cells were analyzed using cell viability, Transwell invasion and wound healing assays. The growth of LUAD tumors in animal models was assessed using tumor xenograft experiments.

RESULTS

This study revealed that elevated ERO1L expression was associated with a poor prognosis in LUAD patients. In addition, ERO1L expression was significantly associated with lymph-node metastasis, TNM stage and tumor size. The expression of Wnt2 was positively associated with ERO1L expression in LUAD tissue samples and cell lines. ERO1L overexpression upregulated the expression of Wnt2 and β-catenin phosphorylation in vitro. Additionally, ERO1L was essential for the ubiquitination of Wnt2. Last, ERO1L promoted the proliferation and metastasis of LUAD via the Wnt2 signaling pathway in vitro and in vivo.

CONCLUSION

These findings suggest that ERO1L was highly expressed in LUAD tissue, and it promoted the proliferation and metastasis of LUAD by activating the Wnt2/β-catenin signaling pathway.

Analysis of lung cancer-related genetic changes in long-term and low-dose polyhexamethylene guanidine phosphate (PHMG-p) treated human pulmonary alveolar epithelial cells

BACKGROUND

Lung injury elicited by respiratory exposure to humidifier disinfectants (HDs) is known as HD-associated lung injury (HDLI). Current elucidation of the molecular mechanisms related to HDLI is mostly restricted to fibrotic and inflammatory lung diseases. In our previous report, we found that lung tumors were caused by intratracheal instillation of polyhexamethylene guanidine phosphate (PHMG-p) in a rat model. However, the lung cancer-related genetic changes concomitant with the development of these lung tumors have not yet been fully defined. We aimed to discover the effect of long-term exposure of PHMG-p on normal human lung alveolar cells.

METHODS

We investigated whether PHMG-p could increase distorted homeostasis of oncogenes and tumor-suppressor genes, with long-term and low-dose treatment, in human pulmonary alveolar epithelial cells (HPAEpiCs). Total RNA sequencing was performed with cells continuously treated with PHMG-p and harvested after 35 days.

RESULTS

After PHMG-p treatment, genes with transcriptional expression changes of more than 2.0-fold or less than 0.5-fold were identified. Within 10 days of exposure, 2 protein-coding and 5 non-coding genes were selected, whereas in the group treated for 27-35 days, 24 protein-coding and 5 non-coding genes were identified. Furthermore, in the long-term treatment group, 11 of the 15 upregulated genes and 9 of the 14 downregulated genes were reported as oncogenes and tumor suppressor genes in lung cancer, respectively. We also found that 10 genes of the selected 24 protein-coding genes were clinically significant in lung adenocarcinoma patients.

CONCLUSIONS

Our findings demonstrate that long-term exposure of human pulmonary normal alveolar cells to low-dose PHMG-p caused genetic changes, mainly in lung cancer-associated genes, in a time-dependent manner.

Detection of Prognostic Biomarkers for Hepatocellular Carcinoma through CircRNA-associated CeRNA Analysis

BACKGROUND AND AIMS

The prognosis of hepatocellular carcinoma (HCC) is extremely poor; therefore, there is an urgent need for novel prognostic molecular biomarkers of HCC. The current investigation utilized circular (circ)RNA-associated competing endogenous (ce)RNAs analysis in order to identify significant prognostic biomarkers of HCC.

METHODS

CircRNAs and mRNAs that were differentially expressed between normal and HCC tissues were identified. Their respective functions were predicted with Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. A nomogram was used for model verification.

RESULTS

A ceRNA network composed of differentially expressed circRNAs and mRNAs was constructed. Significant hub nodes in the ceRNA network were hsa_circ_0004662, hsa_circ_0005735, hsa_circ_0006990, hsa_circ_0018403 and hsa_circ_0100609. By using this information, a prognostic risk assessment tool was developed based on the expressions of seven genes (PLOD2, TARS, RNF19B, CCT2, RAN, C5orf30 and MCM10). Furthermore, multivariate Cox regression analysis revealed risk and T-stage parameters as independent prognostic factors. The nomograms that were constructed from risk and T-stage groups were used to further assess the prediction of HCC patient survival rates. The nomogram, which consisted of risk and T-stage scores assessment models, was found to be an independent factor for predicting prognosis of HCC.

CONCLUSIONS

Five circRNAs, including hsa_circ_0004662, hsa_circ_0005735, hsa_circ_0006990, hsa_circ_0018403 and hsa_circ_0100609, that may play key roles in the progression of HCC were identified. Seven gene signatures were identified, which were associated with the aforementioned circRNAs, including PLOD2, TARS, RNF19B, CCT2, RAN, C5orf30 and MCM10, all of which were significant genes involved in the pathophysiology of HCC. These genes may be used as a prognosticating tool in HCC patients.

The intricate roles of RCC1 in normal cells and cancer cells

RCC1 (regulator of chromosome condensation 1) is a highly conserved chromatin-binding protein and the only known guanine-nucleotide exchange factor of Ran (a nuclear Ras homolog). RCC1 plays an essential role in the regulation of cell cycle-related activities such as nuclear envelope formation, nuclear pore complex and spindle assembly, and nucleocytoplasmic transport. Over the last decade, increasing evidence has emerged highlighting the potential relevance of RCC1 to carcinogenesis, especially cervical, lung, and breast cancer. In this review, we briefly discuss the roles of RCC1 in both normal and tumor cells based on articles published in recent years, followed by a brief overview of future perspectives in the field.

Prognosis-related autophagy genes in female lung adenocarcinoma

To screen the prognosis-related autophagy genes of female lung adenocarcinoma by the transcriptome data and clinical data from The Cancer Genome Atlas (TCGA) database.In this study, screen meaningful female lung adenocarcinoma differential genes in TCGA, use univariate Cox proportional regression model to select genes related to prognosis, and establish the best risk model. In this study, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes were applied for carrying out bioinformatics analysis of gene function.The gene expression and clinical data of 264 female lung adenocarcinoma patient samples were downloaded from TCGA. Twelve down-regulated genes: NRG3, DLC1, NLRC4, DAPK2, HSPB8, PPP1R15A, FOS, NRG1, PRKCQ, GRID1, MAP1LC3C, GABARAPL1. Up-regulated 15 genes: PARP1, BNIP3, P4HB, ATIC, IKBKE, ITGB4, VMP1, PTK6, EIF4EBP1, GAPDH, ATG9B, ERO1A, TMEM74, CDKN2A, BIRC5. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis showed that these genes were significantly associated with autophagy and mitochondria (animals). Multifactor Cox analysis of autophagy-related genes showed that ITGA6, ERO1A, FKBP1A, BAK1, CCR2, FADD, EDEM1, ATG10, ATG4A, DLC1, VAMP7, ST13 were identified as independent prognostic indicators. According to the multivariate Cox proportional hazard regression model, there was a significant difference in the survival rate observed between the high-risk group (n = 124) and the low-risk group (n = 126) during the 10-year follow-up (P < .05). Univariate Cox analysis showed that tumor stage, T, M, and N stages, and risk score were all related to the survival rate of female lung adenocarcinoma patients. Multivariate Cox analysis found that autophagy-related risk scores were independent predictors, with an area under curve (AUC) value of 0.842. At last, there is autophagy genes differentially expressed among various clinicopathological parameters: ATG4A, BAK1, CCR2, DLC1, ERO1A, FKBP1A, ITGA6.The risk score can be used as an independent prognostic indicator for female patients with lung adenocarcinoma. The autophagy genes ITGA6, ERO1A, FKBP1A, BAK1, CCR2, FADD, EDEM1, ATG10, ATG4A, DLC1, VAMP7, ST13 were identified as prognostic genes in female lung adenocarcinoma, which may be the targets of treatment in the future.

RCC1 Expression as a Prognostic Marker in Colorectal Liver Oligometastases

Introduction: Regulator of chromatin condensation 1 (RCC1) is a major guanine-nucleotide exchange factor for Ran GTPase, and it plays key roles in various biological processes. Previous studies have found that RCC1 may play a role in the development of tumors, but little is known about the relationship between RCC1 and colorectal liver oligometastases (CLOs).

Methods: One hundred and twenty-nine pairs of matched human CLO samples, including both primary tumor and its liver metastasis specimens, were subjected to immunohistochemistry to determine the location and expression levels of RCC1. Associations between RCC1 and survival as well as gene expression profiling were explored.

Results: In this study, we first observed that RCC1 was mildly increased in CLO tumor tissues compared with normal tissues, and the localization was primarily nuclear. In addition, our study found that high RCC1 expression in liver oligometastases was an independent prognostic marker for unfavorable recurrence-free survival and overall survival (p = 0.036 and p = 0.016). Gene expression profiles generated from microarray analysis showed that RCC1 was involved in pathways including “Myc targets,” “E2F targets” and “DNA repair” pathways.

Conclusion: Our data indicated that RCC1 was expressed mainly in the nucleus, and strong and significant associations were found between RCC1 expression levels and the survival of CLO patients. These findings indicated that RCC1 may play a role in CLO development.

tRNA Metabolism and Lung Cancer: Beyond Translation

Lung cancer, one of the most malignant tumors, has extremely high morbidity and mortality, posing a serious threat to global health. It is an urgent need to fully understand the pathogenesis of lung cancer and provide new ideas for its treatment. Interestingly, accumulating evidence has identified that transfer RNAs (tRNAs) and tRNA metabolism–associated enzymes not only participate in the protein translation but also play an important role in the occurrence and development of lung cancer. In this review, we summarize the different aspects of tRNA metabolism in lung cancer, such as tRNA transcription and mutation, tRNA molecules and derivatives, tRNA-modifying enzymes, and aminoacyl-tRNA synthetases (ARSs), aiming at a better understanding of the pathogenesis of lung cancer and providing new therapeutic strategies for it.

Integrative and Comprehensive Pancancer Analysis of Regulator of Chromatin Condensation 1 (RCC1)

Regulator of Chromatin Condensation 1 (RCC1) is the only known guanine nucleotide exchange factor that acts on the Ras-like G protein Ran and plays a key role in cell cycle regulation. Although there is growing evidence to support the relationship between RCC1 and cancer, detailed pancancer analyses have not yet been performed. In this genome database study, based on The Cancer Genome Atlas, Genotype-Tissue Expression and Gene Expression Omnibus databases, the potential role of RCC1 in 33 tumors' entities was explored. The results show that RCC1 is highly expressed in most human malignant neoplasms in contrast to healthy tissues. RCC1 expression is closely related to the prognosis of a broad variety of tumor patients. Enrichment analysis showed that some tumor-related pathways such as "cell cycle" and "RNA transport" were involved in the functional mechanism of RCC1. In particular, the conducted analysis reveals the relation of RCC1 to multiple immune checkpoint genes and suggests that the regulation of RCC1 is closely related to tumor infiltration of cancer-associated fibroblasts and CD8⁺ T cells. Coherent data demonstrate the association of RCC1 with the tumor mutation burden and microsatellite instability in various tumors. These findings provide new insights into the role of RCC1 in oncogenesis and tumor immunology in various tumors and indicate its potential as marker for therapy prognosis and targeted treatment strategies.

Phosphorylation of RCC1 on Serine 11 Facilitates G1/S Transition in HPV E7-Expressing Cells

Persistent infection of high-risk human papillomavirus (HR-HPV) plays a causal role in cervical cancer. Regulator of chromosome condensation 1 (RCC1) is a critical cell cycle regulator, which undergoes a few post-translational modifications including phosphorylation. Here, we showed that serine 11 (S11) of RCC1 was phosphorylated in HPV E7-expressing cells. However, S11 phosphorylation was not up-regulated by CDK1 in E7-expressing cells; instead, the PI3K/AKT/mTOR pathway promoted S11 phosphorylation. Knockdown of AKT or inhibition of the PI3K/AKT/mTOR pathway down-regulated phosphorylation of RCC1 S11. Furthermore, S11 phosphorylation occurred throughout the cell cycle, and reached its peak during the mitosis phase. Our previous data proved that RCC1 was necessary for the G1/S cell cycle progression, and in the present study we showed that the RCC1 mutant, in which S11 was mutated to alanine (S11A) to mimic non-phosphorylation status, lost the ability to facilitate G1/S transition in E7-expressing cells. Moreover, RCC1 S11 was phosphorylated by the PI3K/AKT/mTOR pathway in HPV-positive cervical cancer SiHa and HeLa cells. We conclude that S11 of RCC1 is phosphorylated by the PI3K/AKT/mTOR pathway and phosphorylation of RCC1 S11 facilitates the abrogation of G1 checkpoint in HPV E7-expressing cells. In short, our study explores a new role of RCC1 S11 phosphorylation in cell cycle regulation.

Development of metastasis-associated seven gene signature for predicting lung adenocarcinoma prognosis using single-cell RNA sequencing data

Metastasis is the primary cause of lung adenocarcinoma (LUAD)-related death. This study evaluated the metastasis-associated genes (MAGs) in single-cell RNA sequencing (scRNA-seq) data from LUAD tissues and developed a MAG signature to predict overall survival (OS) of LUAD patients. The LUAD scRNA-seq data was downloaded from the Gene Expression Omnibus (GEO) Database and MAGs were identified from LUAD scRNA-seq data. The LUAD transcriptomic and clinical data were obtained from The Cancer Genome Atlas (TCGA). Cox and LASSO regression analyses were performed to identify differentially expressed MAGs (DEMAGs) with prognostic value that were then used to construct a MAG signature and MAG-nomogram model. Finally, a functional enrichment analysis was performed via Gene Set Enrichment Analysis (GSEA). 414 MAGs and 22 prognostic DEMAGs were revealed in the study. Multivariate Cox proportional hazards regression analysis was utilized to construct a 7-MAG signature for predicting the OS of LUAD patients. Patients with high risk scores had a significantly worse OS than those with low risk scores in the training group (n = 236), and the 7-MAG signature was successfully confirmed in the testing group (n = 232) and the entire TCGA-LUAD cohort (n = 468). Furthermore, univariate and multivariate Cox regression suggested that the 7-MAG signature was an independent prognostic indicator. Additionally, based on the 7-MAG signature, a nomogram was established that could more intuitively help to predict the OS of LUAD patients. The GSEA revealed the underlying molecular mechanisms of the 7-MAG signature in LUAD metastasis. In conclusion, a 7-MAG signature was developed based on LUAD scRNA-seq data that could effectively predict LUAD patient prognosis and provide novel insights for therapeutic targets and the potential molecular mechanism of metastatic LUAD.

Identification of Potential Biomarkers and Biological Pathways for Poor Clinical Outcome in Mucinous Colorectal Adenocarcinoma

Simple Summary

Patients with mucinous adenocarcinoma (MAC) have been considered to have a faster disease progression than patients with traditional adenocarcinoma (TAC) in colorectal cancer (CRC). However, to date, the roles of MAC in long-term survival remain controversial due to a small sample size and the nature of its relatively rare occurrence, although it potentially represents entities with different aggressiveness and prognoses. Here, using large-scale population data, we found that the patients with the MAC subtype had a significantly worse overall survival rate and a tendency of worse disease-specific survival rate in stage II compared with the patients with the TAC subtype. Furthermore, key gene signatures were identified using the established predictive models for the disease-specific survival of stage II mucinous CRC.

Abstract

Colorectal cancer (CRC) comprises several histological subtypes, but the influences of the histological subtypes on prognosis remains unclear. We sought to evaluate the prognosis of mucinous adenocarcinoma (MAC), compared to that of traditional adenocarcinoma (TAC). This study used the data of patients diagnosed with CRC between 2004 and 2016, as obtained from the Surveillance, Epidemiology, and End Results database. We established a predictive model for disease-specific survival using conditional survival forest, model, non-linear Cox proportional hazards, and neural multi-task logistic regression model and identified the gene signatures for predicting poor prognosis based on the arrayexpress datasets. In total, 9096 (42.1%) patients with MAC and 12,490 (58.9%) patients with TAC were included. Those with the MAC subtype were more likely to have a poorer overall survival rate compared to those with the TAC subtype in stage II CRC (p = 0.002). The eight major genes including RPS18, RPL30, NME2, USP33, GAB2, RPS3A, RPS25, and CEP57 were found in the interacting network pathway. MAC was found to have a poorer prognosis compared to TAC, especially in Stage II CRC. In addition, our findings suggest that identifying potential biomarkers and biological pathways can be useful in CRC prognosis.

LncRNA PCIR Is an Oncogenic Driver via Strengthen the Binding of TAB3 and PABPC4 in Triple Negative Breast Cancer

Long non-coding RNAs (LncRNA) as the key regulators in all stages of tumorigenesis and metastasis. However, the underlying mechanisms are largely unknown. Here, we report a lncRNA RP11-214F16.8, which renamed Lnc-PCIR, is upregulated and higher RNA level of Lnc-PCIR was positively correlated to the poor survival of patients with triple negative breast cancer (TNBC) tissues. Lnc-PCIR overexpression significantly promoted cell proliferation, migration, and invasion in vitro and in vivo. RNA pulldown, RNA immunoprecipitation (RIP) and RNA transcriptome sequencing technology (RNA-seq) was performed to identify the associated proteins and related signaling pathways. Mechanistically, higher Lnc-PCIR level of blocks PABPC4 proteasome-dependent ubiquitination degradation; stable and highly expressed PABPC4 can further increase the stability of TAB3 mRNA, meanwhile, overexpression of Lnc-PCIR can disrupt the binding status of TAB3 and TAB2 which lead to activate the TNF-α/NF-κB pathway in TNBC cells. Our findings suggest that Lnc-PCIR promotes tumor growth and metastasis via up-regulating the mRNA/protein level of TAB3 and PABPC4, activating TNF-α/NF-κB signaling pathway in TNBC.

Exploration of identifying novel serum biomarkers for malignant mesothelioma using iTRAQ combined with 2D-LC-MS/MS

Malignant mesothelioma (MM) is an aggressive cancer linked to asbestos exposure. Its poor prognosis makes early diagnosis extremely important, which would provide an opportunity for early treatment and potentially changing outcomes. This study aimed to explore the underlying mechanisms of MM and discover novel noninvasive biomarkers for the diagnosis of malignant mesothelioma. Using Isobaric tags for relative and absolute quantitation (iTRAQ) combined with two-dimensional liquid chromatography/tandem mass spectrometry (2D LC-MS/MS), a total of 145 differentially expressed serum proteins were identified between MM patients and healthy controls. The identified proteins were further analyzed by bioinformatics, out of which three candidate biomarkers (Filamin A (FLNA), Fibulin 1 (FBLN1) and Thrombospondin-1 (TSP-1)) were validated in large cohorts of patients with asbestos-related diseases including MM patients by ELISA assay. Receiver operating characteristic (ROC) curve analysis showed that serum FLNA, FBLN1 and TSP-1 had high diagnostic values in distinguishing MM patients from healthy controls, individuals with asbestos exposure (AE), and patients with pleural plaques (PP) or asbestosis. Meanwhile, serum FBLN1 and TSP-1 possessed good diagnostic values in distinguishing asbestosis patients from healthy controls and individuals with AE. The combination of FLNA, FBLN1, and TSP-1 proteins had higher sensitivity and specificity in discriminating patients with MM, PP and asbestosis. Our findings indicated that analysis of serum proteome using iTRAQ is a feasible strategy for biomarker discovery, and serum FLNA, FBLN1 and TSP-1 may be promising candidates for diagnosis of malignant mesothelioma and screening of at-risk individuals.

Roles of Aminoacyl-tRNA Synthetases in Cancer

Aminoacyl-tRNA synthetases (ARSs) catalyze the ligation of amino acids to their cognate transfer RNAs (tRNAs), thus playing an important role in protein synthesis. In eukaryotic cells, these enzymes exist in free form or in the form of multi-tRNA synthetase complex (MSC). The latter contains nine cytoplasmic ARSs and three ARS-interacting multifunctional proteins (AIMPs). Normally, ARSs and AIMPs are regarded as housekeeping molecules without additional functions. However, a growing number of studies indicate that ARSs are involved in a variety of physiological and pathological processes, especially tumorigenesis. Here, we introduce the roles of ARSs and AIMPs in certain cancers, such as colon cancer, lung cancer, breast cancer, gastric cancer and pancreatic cancer. Furthermore, we particularly focus on their potential clinical applications in cancer, aiming at providing new insights into the pathogenesis and treatment of cancer.

NAD(P)-dependent steroid dehydrogenase-like protein and neutral cholesterol ester hydrolase 1 serve as novel markers for early detection of gastric cancer identified using quantitative proteomics

BACKGROUND

Gastric cancer (GC) is the third most common cause of cancer deaths worldwide. In the present study, we aimed to identify novel GC biomarkers by integrating isobaric tags of relative and absolute quantitation (iTRAQ) for aberrantly expressed proteins in GC patients.

METHODS

Using stable isotope tags, we labeled an initial discovery group comprising four paired gastric cancer and adjacent gastric tissue samples, and subjected them to LC-ESI-MS/MS. We used a validation set comprising 129 paired gastric cancer and adjacent gastric tissues from patients and benign healthy controls to validate the candidate targets.

RESULTS

We identified two proteins, NAD(P)-dependent steroid dehydrogenase-like (NSDHL) and neutral cholesterol ester hydrolase 1 (NCEH1), that were significantly overexpressed in GC tissues. The sensitivity and specificity of NSDHL were 80.6% and 74.4%, respectively, in GC compared with a sensitivity of 25.6% in adjacent tissues and 24% in benign healthy controls. The area under the ROC curve (AUC) for NSDHL was 0.810 for GC detection. Overexpression of NSDHL in GC was significantly correlated with local tumor invasion. The sensitivity and specificity of NCEH1 were 77.5% and 73.6%, respectively, in GC compared with a sensitivity of 26.4% in adjacent tissues and 20% in benign controls. The AUC for NSDHL was 0.792. Overexpression of NCEH1 was significantly associated with tumor histological classification and local invasion. Moreover, a combined analysis of NSDHL and NCEH1 achieved a sensitivity and specificity of 85.7% and 83%, respectively, and the AUC was 0.872. The combined analysis of NSDHL and NCEH1 was significantly correlated with histological grade and TNM Ⅱ-Ⅳ staging.

CONCLUSIONS

iTRAQ-labeled quantitative proteomics represents a powerful method to identify novel cancer biomarkers. The present study identified NSDHL and NCEH1 as useful biomarkers for screening, diagnosis, and prognosis of patients with gastric cancer.

A panel of 8 miRNAs as a novel diagnostic biomarker in pancreatic cancer

Pancreatic cancer (PC) is one of the major causes of cancer mortality in developed countries. Therefore, there is an urgent need to derive biomarkers for early diagnosis of PC patients at high risk.This study was designed to identify a panel of miRNAs that might serve as biomarkers for the early diagnosis of PC.The data containing both PC and control samples were extracted from the Gene Expression Omnibus (GEO) database. EdgeR was applied to identify the differentially expressed miRNAs and genes between PC patients and healthy controls. Then a miRNA-mRNA network was constructed based on the differentially expressed miRNAs and genes. The miRNAs-based biomarker for PC was finally constructed by random forest. Finally, AUC was used to evaluate the performance of miRNAs to classify PC and control samples.A total of 33 differentially expressed miRNAs, 753 differentially expressed genes, and 8 miRNAs (hsa-mir-139, hsa-mir-31, hsa-mir-196b, hsa-mir-221, hsa-mir-203b, hsa-mir-215, hsa-mir-144, and hsa-mir-4433b) that play important roles in PC were identified. The target genes of these miRNAs were found to be mainly enriched in negative regulation of acute inflammatory response cell-substrate responses, and o-glycan processing pathways. The constructed biomarkers based on these 8 miRNAs could distinguish samples coming from PC and healthy controls.We identified a panel of eight-miRNAs that would serve as early diagnostic biomarkers for PC patients.

The Multifaceted Roles of RCC1 in Tumorigenesis

RCC1 (regulator of chromosome condensation 1) is the only known guanine nucleotide exchange factor of Ran, a nuclear Ras-like G protein. RCC1 combines with chromatin and Ran to establish a concentration gradient of RanGTP, thereby participating in a series of cell physiological activities. In this review, we discuss the structure of RCC1 and describe how RCC1 affects the formation and function of the nuclear envelope, spindle formation, and nuclear transport. We mainly focus on the effect of RCC1 on the cell cycle during tumorigenesis and the recent research progress that has been made in relation to different tumor types.

ERO1L promotes NSCLC development by modulating cell cycle-related molecules

Lung cancer is the leading cause of cancer‐related death worldwide. Previous studies revealed that endoplasmic reticulum oxidoreductase 1 alpha (ERO1L) played critical roles in the malignant behaviors of several cancer types, but its role in non‐small cell lung cancer (NSCLC) remained unclear. In this study, we identified 26 upregulated and 102 downregulated genes in NSCLC using bioinformatics analyses, and these genes were enriched in the biological processes of the cell cycle. ERO1L was remarkably upregulated in NSCLC and overexpression of ERO1L was associated with poor prognosis of NSCLC. ERO1L deficiency markedly suppressed NSCLC cell proliferation, colony formation, migration, and invasion. ERO1L depletion caused a dramatically decreased expression of cell cycle‐related factors in NSCLC cells. Collectively, our data validated that ERO1L could function as a tumor promoter in NSCLC, indicating the potential of targeting ERO1L for the treatment of NSCLC.

Clinical Application of Mass Spectrometry-Based Proteomics in Lung Cancer Early Diagnosis

The current knowledge on proteomic biomarker analysis for the early diagnosis of lung cancer is summarized, underlining the diversity among the results and the current interest in translating research results into clinical practice. A MEDLINE/PubMed literature search to retrieve all the papers published in the last 10 years is performed. Proteomics studies on lung cancer have gathered evidence on the potential role of biomarkers in early diagnosis. Although promising, none of them have proved to be sufficiently reliable to achieve validation. Future research should evolve toward a multipanel analysis of proteins, considering the possibility that individual biomarkers might not be specific enough to diagnose lung cancer, but could be related to oncological conditions.

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.

OSluca: An Interactive Web Server to Evaluate Prognostic Biomarkers for Lung Cancer

Lung cancer is the principal cause of leading cancer-related incidence and mortality in the world. Various studies have excavated the potential prognostic biomarkers for cancer patients based on gene expression profiles. However, most of these reported biomarkers lack independent validation in multiple cohorts. Herein, we collected 35 datasets with long-term follow-up clinical information from TCGA (2 cohorts), GEO (32 cohorts), and Roepman study (1 cohort), and developed a web server named OSluca (Online consensus Survival for Lung Cancer) to assess the prognostic value of genes in lung cancer. The input of OSluca is an official gene symbol, and the output web page of OSluca displays the survival analysis summary with a forest plot and a survival table from Cox proportional regression in each cohort and combined cohorts. To test the performance of OSluca, 104 previously reported prognostic biomarkers in lung carcinoma were evaluated in OSluca. In conclusion, OSluca is a highly valuable and interactive prognostic web server for lung cancer. It can be accessed at http:// bioinfo.henu.edu.cn/LUCA/LUCAList.jsp.

Integrative Omics Analysis Reveals Soluble Cadherin-3 as a Survival Predictor and an Early Monitoring Marker of EGFR Tyrosine Kinase Inhibitor Therapy in Lung Cancer

PURPOSE

EGFR tyrosine kinase inhibitors (EGFR-TKI) benefit patients with advanced lung adenocarcinoma (ADC) harboring activating EGFR mutations. We aimed to identify biomarkers to monitor and predict the progression of patients receiving EGFR-TKIs via a comprehensive omic analysis.

EXPERIMENTAL DESIGN

We applied quantitative proteomics to generate the TKI resistance-associated pleural effusion (PE) proteome from patients with ADC with or without EGFR-TKI resistance. Candidates were selected from integrated genomic and proteomic datasets. The PE (n = 33) and serum (n = 329) levels of potential biomarkers were validated with ELISAs. Western blotting was applied to detect protein expression in tissues, PEs, and a cell line. Gene knockdown, TKI treatment, and proliferation assays were used to determine EGFR-TKI sensitivity. Progression-free survival (PFS) and overall survival (OS) were assessed to evaluate the prognostic values of the potential biomarkers.

RESULTS

Fifteen proteins were identified as potential biomarkers of EGFR-TKI resistance. Cadherin-3 (CDH3) was overexpressed in ADC tissues compared with normal tissues. CDH3 knockdown enhanced EGFR-TKI sensitivity in ADC cells. The PE level of soluble CDH3 (sCDH3) was increased in patients with resistance. The altered sCDH3 serum level reflected the efficacy of EGFR-TKI after 1 month of treatment (n = 43). Baseline sCDH3 was significantly associated with PFS and OS in patients with ADC after EGFR-TKI therapy (n = 76). Moreover, sCDH3 was positively associated with tumor stage in non-small cell lung cancer (n = 272).

CONCLUSIONS

We provide useful marker candidates for drug resistance studies. sCDH3 is a survival predictor and real-time indicator of treatment efficacy in patients with ADC treated with EGFR-TKIs.

Proteomic and ultrastructural analysis of Clara cell and type II alveolar epithelial cell-type lung cancer cells

BACKGROUND

Currently, the identification of Clara cell and type II alveolar epithelial cell-type cancer cells requires electron microscopy, which is a time-consuming and expensive process involving a complicated tissue sampling procedure. The aim of this study was to identify unique biomarkers for Clara cell and type II alveolar epithelial cell-type lung cancer cells, respectively, with proteomic profiling.

METHODS

Six human lung adenocarcinoma cell lines (A549, NCI-H358, NCI-H1650, HCC827, NCI-H1395, and NCI-H1975) were investigated for their ultrastructural characteristics. The differentially expressed proteins (DEPs) were screened between NCI-H358 cells (Clara cell type) and A549 cells (type II alveolar epithelial cell type) using two-dimensional difference gel electrophoresis (2D-DIGE) and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS/MS), and then they were validated by western blot. The protein expression levels of endoplasmic reticulum oxidoreductin 1-α (ERO1L), Clara cell 10-kD protein (CC10), and surfactant protein C (SP-C) were also determined in the six cell lines assayed.

RESULTS

NCI-H358 cells featured Clara cell differentiation; A549, NCI-H1975, and HCC827 cells had characteristics of type II alveolar epithelial cells; and NCI-H1395 and NCI-H1650 cells had no differentiation characteristics of any lung adenocarcinoma cell type. Five DEPs including ubiquitin carboxyl-terminal hydrolase isozyme L1 (UCHL1), cytokeratin 19 (CK19), cytokeratin 8 (CK8), ERO1L, and peroxiredoxin 2 (PRDX2) between NCI-H358 and A549 cells were identified for further validation; however, none of them showed suitability as an effective biomarker. Similarly, CC10 and SP-C were not appropriate biomarkers.

CONCLUSIONS

Cytological subtypes of NCI-H1975 and HCC827 cells were identified, but no promising biomarker was discovered in the present study.

Development of an autophagy-related gene prognostic signature in lung adenocarcinoma and lung squamous cell carcinoma

Purpose

There is plenty of evidence showing that autophagy plays an important role in the biological process of cancer. The purpose of this study was to establish a novel autophagy-related prognostic marker for lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC).

Methods

The mRNA microarray and clinical data in The Cancer Genome Atlas (TCGA) were analyzed by using a univariate Cox proportional regression model to select candidate autophagy-related prognostic genes. Bioinformatics analysis of gene function using the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) platforms was performed. A multivariate Cox proportional regression model helped to develop a prognostic signature from the pool of candidate genes. On the basis of this prognostic signature, we could divide LUAD and LUSC patients into high-risk and low-risk groups. Further survival analysis demonstrated that high-risk patients had significantly shorter disease-free survival (DFS) than low-risk patients. The signature which contains six autophagy-related genes (EIF4EBP1, TP63, BNIP3, ATIC, ERO1A and FADD) showed good performance for predicting the survival of LUAD and LUSC patients by having a better Area Under Curves (AUC) than other clinical parameters. Its efficacy was also validated by data from the Gene Expression Omnibus (GEO) database.

Conclusion

Collectively, the prognostic signature we proposed is a promising biomarker for monitoring the outcomes of LUAD and LUSC.

ILK predicts the efficacy of chemoradiotherapy and the prognosis of patients with esophageal squamous cell carcinoma

Isobaric tags for relative and absolute quantitation (iTRAQ) technology was adopted to screen differentially-expressed proteins in the serum that predict the effects of chemoradiotherapy on esophageal squamous cell carcinoma (ESCC). Thus, significantly related proteins can be functionally identified at the cellular level. A total of 60 patients diagnosed with locally advanced and advanced ESCC were recruited and treated with chemoradiotherapy. The iTRAQ technique was used to screen serum differentially expressed proteins associated with chemoradiotherapeutic efficacy. Functional identification of significantly related proteins was performed at the cellular level. Cell proliferation was detected using MTT, clonogenic and fluorescence assays, and apoptosis was assessed using flow cytometry. Transwell and wound-healing assays were used to detect the invasion and migration properties of cancer cells. Proteomics results revealed that prior to chemoradiotherapy, the expression level of integrin-linked kinase (ILK) was significantly upregulated in patients with ESCC, compared with that of the control group [ratio (r)=4.386; P<0.05], and significantly downregulated in the chemoradiotherapy-sensitive group, compared with the chemoradiotherapy-resistant group (r=0.587; P<0.05). At the cellular level, the proliferation rate of cells in the experimental group was significantly inhibited (P<0.05), and the number of cell colonies was decreased (P<0.01), while the number of apoptotic cells was significantly increased (P<0.01). The invasive ability of TE-1 cells in the shILK group was significantly inhibited (P<0.01), and the migration rate was significantly retarded at 8 and 24 h (P<0.01). The present study highlighted the potential value of ILK in predicting the efficacy of chemoradiotherapeutic treatment in patients with ESCC, and that ILK gene-silencing inhibits the progression of ESCC.

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.

miR-192-5p Silencing by Genetic Aberrations Is a Key Event in Hepatocellular Carcinomas with Cancer Stem Cell Features

Various cancer stem cell (CSC) biomarkers have been identified for hepatocellular carcinoma (HCC), but little is known about the implications of heterogeneity and shared molecular networks within the CSC population. Through miRNA profile analysis in an HCC cohort (n = 241) for five groups of CSC⁺ HCC tissues, i.e., EpCAM⁺, CD90⁺, CD133⁺, CD44⁺, and CD24⁺ HCC, we identified a 14-miRNA signature commonly altered among these five groups of CSC⁺ HCC. miR-192-5p, the top-ranked CSC miRNA, was liver-abundant and -specific and markedly downregulated in all five groups of CSC⁺ HCC from two independent cohorts (n = 613). Suppressing miR-192-5p in HCC cells significantly increased multiple CSC populations and CSC-related features through targeting PABPC4. Both TP53 mutation and hypermethylation of the mir-192 promoter impeded transcriptional activation of miR-192-5p in HCC cell lines and primary CSC⁺ HCC. This study reveals the circuit from hypermethylation of the mir-192 promoter through the increase in PABPC4 as a shared genetic regulatory pathway in various groups of primary CSC⁺ HCC. This circuit may be the driver that steers liver cells toward hepatic CSC cells, leading to hepatic carcinogenesis. SIGNIFICANCE: miR-192-5p and its regulatory pathway is significantly abolished in multiple groups of HCC expressing high levels of CSC markers, which may represent a key event for hepatic carcinogenesis.

Cellular stress and redox activity proteins are involved in gastric carcinogenesis associated with Helicobacter pylori infection expressing high levels of thioredoxin-1

Helicobacter pylori infection is related to the development of gastric diseases. Our previous studies showed that high thioredoxin-1 (Trx1) expression in H. pylori can promote gastric carcinogenesis. To explore the underlying molecular mechanisms, we performed an isobaric tags for relative and absolute quantitation (iTRAQ)-based quantitative proteomic analysis of stomach tissues from Mongolian gerbil infected with H. pylori expressing high and low Trx1. Differences in the profiles of the expressed proteins were analyzed by bioinformatics and verified using Western blot analysis. We found three candidate proteins, 14-3-3α/β, glutathione-S-transferase (GST), and heat shock protein 70 (HSP70), in high Trx1 tissues compared with low Trx1 tissues and concluded that cellular stress and redox activity-related proteins were involved in the pathogenesis of gastric cancer associated with H. pylori Trx1.