Identification a novel clinical biomarker in early diagnosis of human non-small cell lung cancer

Proteomics appending a complementary dimension to precision oncotherapy

Recent advances in high-throughput proteomic profiling technologies have facilitated the precise quantification of numerous proteins across multiple specimens concurrently. Researchers have the opportunity to comprehensively analyze the molecular signatures in plentiful medical specimens or disease pattern cell lines. Along with advances in data analysis and integration, proteomics data could be efficiently consolidated and employed to recognize precise elementary molecular mechanisms and decode individual biomarkers, guiding the precision treatment of tumors. Herein, we review a broad array of proteomics technologies and the progress and methods for the integration of proteomics data and further discuss how to better merge proteomics in precision medicine and clinical settings.

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.

Protein glycosylation in lung cancer from a mass spectrometry perspective

Lung cancer is a severe disease for which better diagnostic and therapeutic approaches are urgently needed. Increasing evidence implies that aberrant protein glycosylation plays a crucial role in the pathogenesis and progression of lung cancer. Differences in glycosylation patterns have been previously observed between healthy and cancerous samples as well as between different lung cancer subtypes, which suggests untapped diagnostic potential. In addition, understanding the changes mediated by glycosylation may shed light on possible novel therapeutic targets and personalized treatment strategies for lung cancer patients. Mass spectrometry based glycomics and glycoproteomics have emerged as powerful tools for in-depth characterization of changes in protein glycosylation, providing valuable insights into the molecular basis of lung cancer. This paper reviews the literature on the analysis of protein glycosylation in lung cancer using mass spectrometry, which is dominated by manuscripts published over the past 5 years. Studies analyzing N-glycosylation, O-glycosylation, and glycosaminoglycan patterns in tissue, serum, plasma, and rare biological samples of lung cancer patients are highlighted. The current knowledge on the potential utility of glycan and glycoprotein biomarkers is also discussed.

A novel machine learning algorithm selects proteome signature to specifically identify cancer exosomes

Non-invasive early cancer diagnosis remains challenging due to the low sensitivity and specificity of current diagnostic approaches. Exosomes are membrane-bound nanovesicles secreted by all cells that contain DNA, RNA, and proteins that are representative of the parent cells. This property, along with the abundance of exosomes in biological fluids makes them compelling candidates as biomarkers. However, a rapid and flexible exosome-based diagnostic method to distinguish human cancers across cancer types in diverse biological fluids is yet to be defined. Here, we describe a novel machine learning-based computational method to distinguish cancers using a panel of proteins associated with exosomes. Employing datasets of exosome proteins from human cell lines, tissue, plasma, serum, and urine samples from a variety of cancers, we identify Clathrin Heavy Chain (CLTC), Ezrin, (EZR), Talin-1 (TLN1), Adenylyl cyclase-associated protein 1 (CAP1), and Moesin (MSN) as highly abundant universal biomarkers for exosomes and define three panels of pan-cancer exosome proteins that distinguish cancer exosomes from other exosomes and aid in classifying cancer subtypes employing random forest models. All the models using proteins from plasma, serum, or urine-derived exosomes yield AUROC scores higher than 0.91 and demonstrate superior performance compared to Support Vector Machine, K Nearest Neighbor Classifier and Gaussian Naive Bayes. This study provides a reliable protein biomarker signature associated with cancer exosomes with scalable machine learning capability for a sensitive and specific non-invasive method of cancer diagnosis.

Robust Glycoproteomics Platform Reveals a Tetra-Antennary Site-Specific Glycan Capping with Sialyl-Lewis Antigen for Early Detection of Gastric Cancer

The lack of efficient biomarkers for the early detection of gastric cancer (GC) contributes to its high mortality rate, so it is crucial to discover novel diagnostic targets for GC. Recent studies have implicated the potential of site-specific glycans in cancer diagnosis, yet it is challenging to perform highly reproducible and sensitive glycoproteomics analysis on large cohorts of samples. Here, a highly robust N-glycoproteomics (HRN) platform comprising an automated enrichment method, a stable microflow LC-MS/MS system, and a sensitive glycopeptide-spectra-deciphering tool is developed for large-scale quantitative N-glycoproteome analysis. The HRN platform is applied to analyze serum N-glycoproteomes of 278 subjects from three cohorts to investigate glycosylation changes of GC. It identifies over 20 000 unique site-specific glycans from discovery and validation cohorts, and determines four site-specific glycans as biomarker candidates. One candidate has branched tetra-antennary structure capping with sialyl-Lewis antigen, and it significantly outperforms serum CEA with AUC values > 0.89 compared against < 0.67 for diagnosing early-stage GC. The four-marker panel can provide improved diagnostic performances. Besides, discrimination powers of four candidates are also testified with a verification cohort using PRM strategy. This findings highlight the value of this strong tool in analyzing aberrant site-specific glycans for cancer detection.

A novel machine learning algorithm selects proteome signature to specifically identify cancer exosomes

Non-invasive early cancer diagnosis remains challenging due to the low sensitivity and specificity of current diagnostic approaches. Exosomes are membrane-bound nanovesicles secreted by all cells that contain DNA, RNA, and proteins that are representative of the parent cells. This property, along with the abundance of exosomes in biological fluids makes them compelling candidates as biomarkers. However, a rapid and flexible exosome-based diagnostic method to distinguish human cancers across cancer types in diverse biological fluids is yet to be defined. Here, we describe a novel machine learning-based computational method to distinguish cancers using a panel of proteins associated with exosomes. Employing datasets of exosome proteins from human cell lines, tissue, plasma, serum and urine samples from a variety of cancers, we identify Clathrin Heavy Chain (CLTC), Ezrin, (EZR), Talin-1 (TLN1), Adenylyl cyclase-associated protein 1 (CAP1) and Moesin (MSN) as highly abundant universal biomarkers for exosomes and define three panels of pan-cancer exosome proteins that distinguish cancer exosomes from other exosomes and aid in classifying cancer subtypes employing random forest models. All the models using proteins from plasma, serum, or urine-derived exosomes yield AUROC scores higher than 0.91 and demonstrate superior performance compared to Support Vector Machine, K Nearest Neighbor Classifier and Gaussian Naive Bayes. This study provides a reliable protein biomarker signature associated with cancer exosomes with scalable machine learning capability for a sensitive and specific non-invasive method of cancer diagnosis.

Overexpression of SERPINA3 suppresses tumor progression by modulating SPOP/NF‑κB in lung cancer

The pathogenesis mechanism of lung cancer is very complex, with high incidence and mortality. Serpin family A member 3 (SERPINA3) expression levels were reduced in the sera of patients with lung cancer and may be a candidate diagnostic and prognostic survival biomarker in lung cancer, as previously reported. However, the detailed biological functions of SERPINA3 in the pathogenesis of lung cancer remain unknown. In the present study, it was aimed to explore the effects of SERPINA3 on the occurrence of lung cancer. SERPINA3 expression was assessed using bioinformatics database analysis and experimental detection. Then, the biological effects of SERPINA3 were investigated in a cell culture system and a xenograft model of human lung cancer. The potential regulatory mechanism of SERPINA3 in lung cancer was explored by data‑independent acquisition mass spectrometry (DIA‑MS) detection and further validated by western blotting (WB). The results indicated that SERPINA3 expression levels were significantly downregulated in lung cancer tissues and cell lines. At the cellular level, it was revealed that overexpressed SERPINA3 inhibited cell growth, proliferation, migration and invasion and promoted the apoptosis of lung cancer cells. Moreover, overexpressed SERPINA3 enhanced the sensitivity of lung cancer cells to osimertinib. In vivo, a xenograft model of human lung cancer was established with BALB/c nude mice. After the injection of A549 cells, the tumor growth of the tumor‑bearing mice in the SERPINA3‑overexpressing group increased more slowly, and the tumor volume was smaller than that in the empty‑vector group. Mechanistically, a total of 65 differentially expressed proteins were identified. It was found that the speckle‑type POZ protein (SPOP) was significantly upregulated in SERPINA3‑overexpressing H157 cells using DIA‑MS detection and analysis. WB validation showed that SPOP expression increased, and NF‑kappaB (NF‑κB) p65 was inhibited in cell lines and tumor tissues of mice when SERPINA3 was overexpressed. The present findings suggest that SERPINA3 is involved in the development of lung cancer and has an antineoplastic role in lung cancer.

Apolipoprotein C1 promotes glioblastoma tumorigenesis by reducing KEAP1/NRF2 and CBS-regulated ferroptosis

Glioblastoma (GBM), a malignant brain tumor, is a world-wide health problem because of its poor prognosis and high rates of recurrence and mortality. Apolipoprotein C1 (APOC1) is the smallest of apolipoproteins, implicated in many diseases. Recent studies have shown that APOC1 promotes tumorigenesis and development of several types of cancer. In this study we investigated the role of APOC1 in GBM tumorigenesis. Using in silico assays we showed that APOC1 was highly expressed in GBM tissues and its expression was closely related to GBM progression. We showed that APOC1 protein expression was markedly increased in four GBM cell lines (U251, U138, A172 and U87) compared to the normal brain glia cell lines (HEB, HA1800). In U251 cells, overexpression of APOC1 promoted cell proliferation, migration, invasion and colony information, which was reversed by APOC1 knockdown. APOC1 knockdown also markedly inhibited the growth of GBM xenografts in the ventricle of nude mice. We further demonstrated that APOC1 reduced ferroptosis by inhibiting KEAP1, promoting nuclear translocation of NRF2 and increasing expression of HO-1 and NQO1 in GBM cells. APOC1 also induced ferroptosis resistance by increasing cystathionine beta-synthase (CBS) expression, which promoted trans-sulfuration and increased GSH synthesis, ultimately leading to an increase in glutathione peroxidase-4 (GPX4). Thus, APOC1 plays a key role in GBM tumorigenesis, conferring resistance to ferroptosis, and may be a promising therapeutic target for GBM.

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.

Recent advancements in glycoproteomic studies: Glycopeptide enrichment and derivatization, characterization of glycosylation in SARS CoV2, and interacting glycoproteins

Proteomics studies allow for the determination of the identity, amount, and interactions of proteins under specific conditions that allow the biological state of an organism to ultimately change. These conditions can be either beneficial or detrimental. Diseases are due to detrimental changes caused by either protein overexpression or underexpression caused by as a result of a mutation or posttranslational modifications (PTM), among other factors. Identification of disease biomarkers through proteomics can be potentially used as clinical information for diagnostics. Common biomarkers to look for include PTM. For example, aberrant glycosylation of proteins is a common marker and will be a focus of interest in this review. A common way to analyze glycoproteins is by glycoproteomics involving mass spectrometry. Due to factors such as micro- and macroheterogeneity which result in a lower abundance of each version of a glycoprotein, it is difficult to obtain meaningful results unless rigorous sample preparation procedures are in place. Microheterogeneity represents the diversity of glycans at a single site, whereas macroheterogeneity depicts glycosylation levels at each site of a protein. Enrichment and derivatization of glycopeptides help to overcome these limitations. Over the time range of 2016 to 2020, several methods have been proposed in the literature and have contributed to drastically improve the outcome of glycosylation analysis, as presented in the sampling surveyed in this review. As a current topic in 2020, glycoproteins carried by pathogens can also cause disease and this is seen with SARS CoV2, causing the COVID-19 pandemic. This review will discuss glycoproteomic studies of the spike glycoprotein and interacting proteins such as the ACE2 receptor.

Alpha-1-antichymotrypsin as a novel biomarker for diagnosis, prognosis, and therapy prediction in human diseases

The glycoprotein alpha-1-antichymotrypsin (AACT), a serine protease inhibitor, is mainly synthesized in the liver and then secreted into the blood and is involved in the acute phase response, inflammation, and proteolysis. The dysregulation of AACT and its glycosylation levels are associated with tumor progression and recurrence, and could be used as a biomarker for tumor monitoring. In this review, we summarized the expression level, glycosylation modification, and biological characteristics of AACT during inflammation, neurodegenerative or other elderly diseases, and tumorigenesis, as well as, focused on the biological roles of AACT in cancer. The aberrant expression of AACT in cancer might be due to genetic alterations and/or immune by bioinformatics analysis. Moreover, AACT may serve as a diagnostic or prognostic biomarker or therapeutic target in tumors. Furthermore, we found that the expression of AACT was associated with the overall survival of patients with human cancers. Decreased AACT expression was associated with poor survival in patients with liver cancer, increased AACT expression was associated with shorter survival in patients with pancreatic cancer, and decreased AACT expression was associated with shorter survival in patients with early lung cancer. The review confirmed the key roles of AACT in tumorigenesis, suggesting that the glycoprotein AACT may serve as a biomarker for tumor diagnosis and prognosis, and could be a potential therapeutic target for human diseases.

Sensitive Assay for the Lactonase Activity of Serum Paraoxonase 1 (PON1) by Harnessing the Fluorescence Turn-On Characteristics of Bioorthogonally Synthesized and Geometrically Controlled Chemical Probes

The lactonase activity of paraoxonase 1 (PON1) has a crucial antiatherogenic function, and also serves as an important biochemical marker in human blood because the aberrant lactonase activity of PON1 is a key indicator for a number of diverse human diseases. However, no sensitive fluorescence assays that detect PON1 lactonase activity are available. We report the synthesis of two fluorescence turn-on chemical probes 16a and 16b (16) able to quantify PON1 lactonase activity. The chemical probes were constructed utilizing a disulfide-containing bicyclononyne, derivatives of rhodamine B and carboxyfluorescein, and reactions including copper-free azide–alkyne cycloaddition. Fluorescence quenching in 16 was characterized by spectroscopic studies and was mainly attributed to the effect of contact quenching. Kinetic analysis of 16b confirmed the outstanding reactivity and specificity of 16b with thiols in the presence of general base catalysts. The 16b-based assay was employed to determine PON1 lactonase activity, with a linear range of 10.8–232.1 U L⁻¹ and detection limit (LOD) of 10.8 U L⁻¹, to quantify serum PON1 activity in human sera, and to determine the K_i of 20.9 μM for the 2-hydroxyquinoline inhibition of PON1 lactonase. We are employing 16b to develop high-throughput assays for PON1 lactonase activity.

Identification of novel combined biomarkers in the diagnosis of multiple myeloma

PURPOSE

Multiple myeloma (MM) is a haematological malignant disease with a clonal proliferation of plasma cells, and timely surveillance is helpful to improve the survival rate of patients with MM. However, there is a lack of simple and effective biomarkers for the diagnosis, prognosis, and residual disease evaluation of MM.

MATERIAL & METHODS

In the detection cohort, we used the samples from six newly diagnosed MM patients and six control subjects. Plasma proteins were labelled with dimethyl reagents and enriched by lectin AANL6, then the deglycosylated peptides were identified by LC-MS/MS. Differentially expressed proteins were used for further exploration. In the validation cohort, we used 90 newly diagnosed patients with MM and 70 cases of unrelated diseases as controls. The diagnosis performance was analysed by ROC analysis using SPSS.

RESULTS

In this study, we show, using lectin blots with AANL6, that glycosylation levels were higher in MM patients than in controls. After AANL6 enrichment, we detected 58 differentially expressed proteins using quantitative proteomics. We further validated one candidate Fibulin-1 (FBLN1). Using an Elisa assay, we showed that FBLN1 expression was increased in plasma of 90 cases of MM, and which was significantly correlated with DKK1 expression. ROC analysis showed that these two markers had a 95.7% specificity for determining the diagnosis of MM.

CONCLUSION

These data suggest that the MM cases display increased glycosylation after AANL6 enrichment and that the combined expression of FBLN1 and DKK1 can be used as an effective diagnostic biomarker.

The effects of edaravone, a free-radical scavenger in lung injury induced by valproic acid demonstrated via different biochemical parameters

In this study, we aimed to evaluate whether edaravone (EDA) has a protective role against valproic acid (VPA)-induced lung damage via its antioxidative activity. Male Sprague-Dawley rats were split into four groups. Control (n = 8) rats; rats given EDA (30 mg kg^-1  day^-1 ; n = 10); rats given only (VPA, 500 mg kg^-1  day^-1 ; n = 10); rats given VPA + EDA (in the same dose and time) for 7 days. EDA and VPA were applied intraperitoneally. After 8 days, lung tissues were immediately taken from the rats. In lung homogenates, reduced glutathione, total antioxidant status levels, and superoxide dismutase, glutathione peroxidase, sodium/potassium ATPase, paraoxonase1, and carbonic anhydrase activities significantly abated, whereas catalase, glutathione reductase, glutathione-S-transferase activities insignificantly decreased in the VPA-treated group. In contrast, lipid peroxidation, reactive oxygen species, and total oxidant status levels, glycoprotein and protein carbonyl contents, nitric oxide, hydroxyproline levels, and xanthine oxidase, lactate dehydrogenase, arginase, and prolidase activities significantly increased in the VPA-given group. Administration of EDA caused the reverse effects. As a consequence, EDA prevented oxidative stress-mediated lung injury via its robust antioxidant effects.

[Screening and Identification of the Peptides Specifically Binding to Human Non-small Cell Lung Cancer NCI-H1299 Cells]

BACKGROUND

Non-small cell lung cancer (NSCLC) is the most common histological type of lung cancer, and one of the malignant tumor with the highest mortality. As the main part of the optical molecular imaging probe, peptide can realize the early screening and diagnosis of tumor and improve the survival rate of patients. The aim of this study was to screen the small-molecule peptide that highly binds to NSCLC NCI-H1299 cells using in vivo phage display technology and to identify their binding specificity by in vitro experiment.

METHODS

To prepare a tumor-bearing nude mouse model of NCI-H1299 cells, after 3 rounds of in vivo screening with Ph.D.-C7CTM Peptide Library, phage clones were randomly picked, using immunohistochemistry and enzyme-linked immunosorbent assay (ELISA) to identify the affinity of phage clones to NCI-H1299 cells. The positive monoclonal phages DNA was extracted and sequenced to obtain the amino acid sequence of the peptides. The peptides with the highest repetition rate was chemically synthesized and labeled with fluorescein (FITC) to prepare optical molecular probe. We preliminary identified the specificity of the probe binding to lung cancer cells by in vitro experiment.

RESULTS

After three rounds of in vivo screening, the phages enrichment rate was 341.3 times compared with the first round. Immunohistochemical staining showed that with the increase of screening times, the phages binding to tumor tissues continued to increase, and the binding amount was significantly higher than normal tissues; ELISA results showed that 20 clones among the 30 randomly selected phage clones were positive. After sequencing, the peptide with the highest repetition rate was synthesized and named NSP1; Methyl thiazolyl tetrazolium assay (MTT) and would healing assay showed that NSP1 will not affect cell proliferation and migration. Flow cytometry and immunofluorescence showed specific binding of NSP1 to NCI-H1299 cells.

CONCLUSIONS

We successfully obtained the peptide NSP1 that specifically binds to lung cancer NCI-H1299 cells by in vivo phage display, which provide a theoretical basis for NSCLC early diagnosis and targeted therapy.

Lysosomal dysfunction and autophagy blockade contribute to autophagy-related cancer suppressing peptide-induced cytotoxic death of cervical cancer cells through the AMPK/mTOR pathway

Background

Autophagy is an intracellular process through which intracellular components are recycled in response to nutrient or growth factor deficiency to maintain homeostasis. We identified the peptide autophagy-related cancer-suppressing peptide (ARCSP), a potential antitumor peptide that disrupts intracellular homeostasis by blocking autophagic flux and causes cytotoxic death.

Methods

The proliferative ability of ARCSP-treated cervical cancer cells was examined by the CCK8, EdU, and colony formation assays. The TUNEL assay was used to detect apoptosis. Mitochondrial function was evaluated based on the mitochondrial membrane potential. Autophagic flux was detected by immunofluorescence and confocal microscopy. The autophagy-related proteins AMPK, Raptor, mTOR, p62, LC3B, atg7, Rab7, LAMP1, LAMP2, and cathepsin D were detected by Immunoblotting. The antitumor effect of ARCSP was explored in vivo by establishing a transplant tumor model in nude mice.

Results

The results demonstrated that ARCSP induced cell death and inhibited proliferation. ARCSP induced AMPK/mTOR activation, resulting in the accumulation of the proteins LC3B, p62 and Atg7. ARCSP also blocked autophagosome-lysosome fusion by inhibiting endosomal maturation and increasing the lysosomal pH. The accumulation of nonfused autophagosomes exacerbated cytotoxic death, whereas knocking down Atg7 reversed the cytotoxic death induced by ARCSP. ARCSP-treated cells exhibited increased cytotoxic death after cotreatment with an autophagy inhibitor (Chloroquine CQ). Furthermore, the tumors of ARCSP-treated nude mice were significantly smaller than those of untreated mice.

Conclusions

Our findings demonstrate that ARCSP, a novel lethal nonfused autophagosome inducer, might cause mitochondrial dysfunction and autophagy-related cytotoxic death and is thus a prospective agent for cancer therapy.

A comprehensive analysis of metabolomics and transcriptomics in non-small cell lung cancer

Non-small cell lung cancer (NSCLC) remains a leading cause of cancer death globally. More accurate and reliable diagnostic methods/biomarkers are urgently needed. Joint application of metabolomics and transcriptomics technologies possesses the high efficiency of identifying key metabolic pathways and functional genes in lung cancer patients. In this study, we performed an untargeted metabolomics analysis of 142 NSCLC patients and 159 healthy controls; 35 identified metabolites were significantly different between NSCLC patients and healthy controls, of which 6 metabolites (hypoxanthine, inosine, L-tryptophan, indoleacrylic acid, acyl-carnitine C10:1, and lysoPC(18:2)) were chosen as combinational potential biomarkers for NSCLC. The area under the curve (AUC) value, sensitivity (SE), and specificity (SP) of these six biomarkers were 0.99, 0.98, and 0.99, respectively. Potential diagnostic implications of the metabolic characteristics in NSCLC was studied. The metabolomics results were further verified by transcriptomics analysis of 1027 NSCLC patients and 108 adjacent peritumoral tissues from TCGA database. This analysis identified 2202 genes with significantly different expressions in cancer cells compared to normal controls, which in turn defined pathways implicated in the metabolism of the compounds revealed by metabolomics analysis. We built a fully connected network of metabolites and genes, which shows a good correspondence between the transcriptome analysis and the metabolites selected for diagnosis. In conclusion, this work provides evidence that the metabolic biomarkers identified may be used for NSCLC diagnosis and screening. Comprehensive analysis of metabolomics and transcriptomics data offered a validated and comprehensive understanding of metabolism in NSCLC.

Mass spectrometry based proteomics and metabolomics in personalized oncology

Precision medicine (PM) means the customization of healthcare with decisions and practices adjusted to the individual patient. It includes personalized diagnostics, patients' sub-classification, individual treatment selection and the monitoring of its effectiveness. Currently, in oncology, PM is based on the molecular and cellular features of a tumor, its microenvironment and the patient's genetics and lifestyle. Surprisingly, the available targeted therapies were found effective only in a subset of patients. An in-depth understanding of tumor biology is crucial to improve their effectiveness and develop new therapeutic targets. Completion of genetic information with proteomics and metabolomics can give broader knowledge about tumor biology which consequently provides novel biomarkers and indicates new therapeutic targets. Recently, metabolomics and proteomics have extensively been applied in the field of oncology. In the context of PM, human studies, with the use of mass spectrometry (MS) which allows the detection of thousands of molecules in a large number of samples, are the most valuable. Such studies, focused on cancer biomarkers discovery or patients' stratification, are presented in this review. Moreover, the technical aspects of MS-based clinical proteomics and metabolomics are described.

Mining cancer biology through bioinformatic analysis of proteomic data

Introduction: Discovery proteomics for cancer research generates complex datasets of diagnostic, prognostic, and therapeutic significance in human cancer. With the advent of high-resolution mass spectrometers, able to identify thousands of proteins in complex biological samples, only the application of bioinformatics can lead to the interpretation of data which can be relevant for cancer research. Areas covered: Here, we give an overview of the current bioinformatic tools used in cancer proteomics. Moreover, we describe their applications in cancer proteomics studies of cell lines, serum, and tissues, highlighting recent results and critically evaluating their outcomes. Expert opinion: The use of bioinformatic tools is a fundamental step in order to manage the large amount of proteins (from hundreds to thousands) that can be identified and quantified in a cancer biological samples by proteomics. To handle this challenge and obtain useful data for translational medicine, it is important the combined use of different bioinformatic tools. Moreover, a particular attention to the global experimental design, and the integration of multidisciplinary skills are essential for best setting of tool parameters and best interpretation of bioinformatics output.

Apolipoprotein C1 (APOC1) as a novel diagnostic and prognostic biomarker for gastric cancer

Background

Gastric cancer (GC) is a common malignant cancer in the worldwide, especially in China. Patients with GC have poor prognosis, which is mainly due to lack of early diagnosis. Up to now, there is no good biomarker to detect GC at early stage. Apolipoprotein C1 (APOC1), a component of both triglyceride-rich lipoproteins and high-density lipoproteins, is reported to be involved in numerous biological processes.

Methods

Expression of APOC1 mRNA was analyzed by in silicon assay. Concentration of APOC1 in serum was measured by ELISA assay. Expression of APOC1 protein in GC tissue array was checked by immunohistochemistry.

Results

It was firstly found that concentration of APOC1 in serum was significantly higher in GC than that in control. Expression of APOC1 protein was also higher in GC than that in adjacent issues of GC and normal tissues using tissues array by immunohistochemistry. In addition, the expression of APOC1 is significantly associated with clinical stage (P=0.011), tumor classification (P=0.010), as well as with the lymph node metastasis (P=0.048). Area under the curve (AUC) of receiver operating characteristic (ROC) curve of APOC1 was 0.803. Furthermore, elevated APOC1 expression in GC was found to be correlated with decreased overall survival (P=0.00214).

Conclusions

All these results suggested that APOC1 might be a potential serum biomarker to diagnose GC and a potential prognostic marker for GC.