Rapid and High-Sensitive Phosphoproteomics Elucidated the Spatial Dynamics of the Mouse Brain.

Recent developments in phosphoproteomics have enabled signaling studies where over 10,000 phosphosites can be routinely identified and quantified. Yet, current analyses are limited in sample size, reproducibility, and robustness, hampering experiments that involve low-input samples such as rare cells and fine-needle aspiration biopsies. To address these challenges, we introduced a simple and rapid phosphorylation enrichment method (miniPhos) that uses a minimal amount of the sample to get enough information to decipher biological significance. The miniPhos approach completed the sample pretreatment within 4 h and high effectively collected the phosphopeptides in a single-enrichment format with an optimized enrichment process and miniaturized system. This resulted in an average of 22,000 phosphorylation peptides quantified from 100 µg of proteins and even confidently localized over 4500 phosphosites from as little as 10 µg of peptides. Further application was carried out on different layers of mouse brain micro-sections; our miniPhos method provided quantitative information on protein abundance and phosphosite regulation for the most relevant neurodegenerative diseases, cancers, and signaling pathways in the mouse brain. Surprisingly, the phosphoproteome exhibited more spatial variations than the proteome in the mouse brain. Overall, spatial dynamics of phosphosites are integrated with proteins to gain insights into crosstalk of cellular regulation at different layers, thereby facilitating a more comprehensive understanding of mouse brain development and activity.

Cancer Serum Atlas-Supported Precise Pan-Targeted Proteomics Enable Multicancer Detection.

The wide dynamic range of serum proteome restrained discovery of clinically interested proteins in large cohort studies. Herein, we presented a high-sensitivity, high-throughput, and precise pan-targeted serum proteomic strategy for highly efficient cancer serum proteomic research and biomarker discovery. We constructed a resource of over 2000 cancer-secreted proteins, and the standard MS assays and spectra of at least one synthetic unique peptide per protein were acquired and documented (Cancer Serum Atlas, www.cancerserumatlas.com). Then, the standard peptide-anchored parallel reaction monitoring (SPA-PRM) method was developed with support of the Cancer Serum Atlas, achieving precise quantification of cancer-secreted proteins with high throughput and sensitivity. We directly quantified 325 cancer-related serum proteins in 288 serums of four cancer types (liver, stomach, lung, breast) and controls with the pan-targeted strategy and discovered considerable potential biomarker benefits for early detection of cancer. Finally, a proteomic-based multicancer detection model was built, demonstrating high sensitivity (87.2%) and specificity (100%), with 73.8% localization accuracy for an independent test set. In conclusion, the Cancer Serum Atlas provides a wide range of potential biomarkers that serve as targets and standard assays for systematic and highly efficient serological studies of cancer. The Cancer Serum Atlas-supported pan-targeted proteomic strategy enables highly efficient biomarker discovery and multicancer detection and thus can be a powerful tool for liquid biopsy.

Quantitative proteomics reveals stage-specific protein regulation of triple negative breast cancer.

BACKGROUNDS: Triple negative breast cancer (TNBC) is a heterogeneous disease with more aggressive clinical courses than other subtypes of breast cancer. In this study, we performed high-resolution mass spectrometry-based quantitative proteomics with TNBC clinical tissue specimens to explore the early and sensitive diagnostic signatures and potential therapeutic targets for TNBC patients. METHODS: We performed an iTRAQ labeling coupled LC-MS/MS approach to explore the global proteome in tumor tissues and corresponding para-tumor tissues from 24 patients with grade I-II and grade III primary TNBC. Relative peptide quantification and protein identification were performed by Proteome Discoverer™ software with Mascot search engine. Differentially expressed proteins were analyzed by bioinformatic analyses, including GO function classification annotation and KEGG enrichment analysis. Pathway analyses for protein-protein interactions and upstream regulations of differentially expressed candidates were performed by Ingenuity Pathway Analysis (IPA) software. RESULTS: Totally, 5401 unique proteins were identified and quantified in different stage of TNBCs. 845 proteins were changed in patients with grade I or II TNBC, among which 304 were up-regulated and 541 were down-regulated. Meanwhile, for patients with grade III TNBC, 358 proteins were increased and 651 proteins were decreased. Comparing to para-cancerous tissues, various signaling pathways and metabolic processes, including PPAR pathways, PI3K-Akt pathway, one-carbon metabolism, amino acid synthesis, and lipid metabolism were activated in TNBC cancer tissues. Death receptor signaling was significantly activated in grade I-II TNBCs, however, remarkably inhibited in grade III TNBCs. Western blot experiments were conducted to validate expression levels of CYCS, HMGA1 and XIAP with samples from individual patients. CONCLUSIONS: Overall, our proteomic data presented precise quantification of potential signatures, signaling pathways, regulatory networks, and characteristic differences in each clinicopathological subgroup. The proteome provides complementary information for TNBC accurate subtype classification and therapeutic targets research.

Fast and precise quantification of serum biomarkers and simultaneous recognition of multiple diseases enabled by a stable isotope-labelled peptides assisted high-throughput MRM strategy.

Serum/plasma holds promise as an important source of disease-related proteins and even biomarkers in clinical practice. However, the discovery of biomarker candidates in serum/plasma remains challenging. In this study, we constructed an MS strategy that enables the fast and precise quantification of serum biomarkers through coupling a high-throughput scheduled MRM strategy with a stable isotope-labelled (SIL) peptide panel from more than 500 plasma proteins as internal standards. With this strategy, we discovered relevant serum proteins of atherosclerosis (AS), lung cancer (LC) and breast cancer (BC), which can simultaneously recognize these diseases. The results indicate that the powerful strategy we constructed has the potential for serum biomarker screening and disease detection.

Aperture-controllable nano-electrospray emitter and its application in cardiac proteome analysis.

The emitter clogging is the most common hardware failure of nano-electrospray ionization, to improve the durability and electrospray stability of fused silica emitters, we demonstrate a means of fabricating nano-electrospray emitters with controllable aperture size and gradually-narrowed channel on the tip. We simulated the fluid morphologies in the emitter channels by computational fluid dynamics and found more stable flow on aperture-controllable nano-electrospray emitter. Besides, we found the unstable flow sections of commercial emitters match the actual clogging sections very well, indicating the main cause of emitter clogging is unstable flow. We further tested the emitters by nano-LC-MS based proteome analysis. Compared with the commercial emitter, aperture-controllable nano-electrospray emitters promoted the total ion chromatogram intensity by 25%, the number of identified proteins by 6.58%, and the number of identified peptides by 7.87%. In total, 989 proteins were identified from 1 µg of extracted mouse cardiac proteins. After the optimization by using mouse samples, we analyzed clinical auricular dextral tissues from patients undergoing cardiac surgery and found 16 proteins related to atrial fibrillation. Overall, aperture-controllable nano-electrospray emitter exhibits better sensitivity and reproducibility in the application of nano-LC-MS cardiac proteome analysis.

Novel RNA-Affinity Proteogenomics Dissects Tumor Heterogeneity for Revealing Personalized Markers in Precision Prognosis of Cancer.

To discriminate the patient subpopulations with different clinical outcomes within each breast cancer (BC) subtype, we introduce a robust, clinical-practical, activity-based proteogenomic method that identifies, in their oncogenically active states, candidate biomarker genes bearing patient-specific transcriptomic/genomic alterations of prognostic value. First, we used the intronic splicing enhancer (ISE) probes to sort ISE-interacting trans-acting protein factors (trans-interactome) directly from a tumor tissue for subsequent mass spectrometry characterization. In the retrospective, proteogenomic analysis of patient datasets, we identified those ISE trans-factor-encoding genes showing interaction-correlated expression patterns (iCEPs) as new BC-subtypic genes. Further, patient-specific co-alterations in mRNA expression of select iCEP genes distinguished high-risk patient subsets/subpopulations from other patients within a single BC subtype. Function analysis further validated a tumor-phenotypic trans-interactome contained the drivers of oncogenic splicing switches, representing the predominant tumor cells in a tissue, from which novel personalized biomarkers were clinically characterized/validated for precise prognostic prediction and subsequent individualized alignment of optimal therapy.

A new panel of pancreatic cancer biomarkers discovered using a mass spectrometry-based pipeline.

This corrects the article DOI: 10.1038/bjc.2017.85.

A new panel of pancreatic cancer biomarkers discovered using a mass spectrometry-based pipeline.

BACKGROUND: Pancreatic carcinoma (PC) is an aggressive malignancy that lacks strategies for early detection. This study aimed to develop a coherent, high-throughput and non-discriminatory pipeline for the novel clinical biomarker discovery of PC. METHODS: We combined mass spectrometry (MS)-intensive methods such as isobaric tags for relative and absolute quantitation with two-dimensional liquid chromatography-tandem mass spectrometry (iTRAQ-2DLC-MS/MS), 1D-targeted LC-MS/MS, prime MRM (P-MRM) and stable isotope dilution-based MRM (SID-MRM) to analyse serum samples from healthy people (normal control, NC), patients with benign diseases (BD) and PC patients to identify novel biomarkers of PC. RESULTS: On the basis of the newly developed pipeline, we identified >1000 proteins, verified 142 differentially expressed proteins and finally targeted four proteins for absolute quantitation in 100 serum samples. The novel biomarker panel of apolipoprotein E (APOE), inter-alpha-trypsin inhibitor heavy chain H3 (ITIH3), apolipoprotein A-I (APOA1), apolipoprotein L1 (APOL1), combining with CA19-9, statistically-significantly improved the sensitivity (95%) and specificity (94.1%), outperforming CA19-9 alone, for the diagnosis of PC. CONCLUSIONS: We developed a highly efficient pipeline for biomarker discovery, verification and validation, with each step systematically informing the next. A panel of proteins that might be clinically relevant biomarkers for PC was found.

Quantitative profiling of glycerophospholipids during mouse and human macrophage differentiation using targeted mass spectrometry.

Macrophage lipid metabolism plays a pivotal role in innate and adaptive immune responses. Previous studies have shown that this process plays a role in infections and contributes to the pathogenesis of diabetes, atherosclerosis, and other immunometabolic diseases. M1 macrophages, or classically activated macrophages, are key players in the defense against bacterial infections. M2 macrophages, or alternatively activated macrophages, are involved in anti-inflammatory responses. Using the multiple reaction monitoring method, we identified changes in lipid composition during the differentiation of human and murine macrophages. We detected over 300 lipid molecules in mammalian macrophages, and we observed a striking shift in the composition of glycerophospholipids (GLs) from saturated and monounsaturated to polyunsaturated during human macrophage polarization. Moreover, M2 macrophages showed a higher level of lysophospholipids (lysoGLs) than did M1 macrophages. The lysoPI species increased in human and mouse M2 macrophages, suggesting that they may be involved in M2 macrophage polarization and anti-inflammatory processes. Collectively, these results indicate that lipids may play a role in the pro- and anti-inflammatory activities of macrophages and may be markers of the macrophage activation state.

Functional lipidomics: Palmitic acid impairs hepatocellular carcinoma development by modulating membrane fluidity and glucose metabolism.

Lipids are essential cellular components and energy sources of living organisms, and altered lipid composition is increasingly recognized as a signature of cancer. We performed lipidomic analysis in a series of hepatocellular carcinoma (HCC) cells and identified over 1,700 intact lipids originating from three major lipid categories. Comparative lipidomic screening revealed that 93 significantly changed lipids and decreased palmitic acyl (C16:0)-containing glycerophospholipids were positively associated with metastatic abilities of HCC cells. Furthermore, both in vitro and in vivo experiments demonstrated that C16:0 incubation specifically reduced malignant cell proliferation, impaired cell invasiveness, and suppressed tumor growth in mouse xenograft models. Biochemical experiments demonstrated that C16:0 treatment decreased cell membrane fluidity and limited glucose metabolism. A phosphoproteomics approach further revealed such C16:0 incubation attenuated phosphorylation levels of mammalian target of rapamycin (mTOR) and signal transducer and activator of transcription 3 (STAT3) pathway proteins. Multiple reaction monitoring analysis of 443 lipid molecules showed 8 reduced C16:0-containing lipids out of total 10 altered lipids when cancer tissues were compared with adjacent nontumor tissues in a cohort of clinical HCC specimens (P < 0.05). CONCLUSION: These data collectively demonstrate the biomedical potential of using altered lipid metabolism as a diagnostic marker for cancerous cells and open an opportunity for treating aggressive HCCs by targeting altered C16:0 metabolism. (Hepatology 2017;66:432-448).

Mapping and analyzing the human liver proteome: progress and potential.

INTRODUCTION: The liver is an important organ in humans. Hepatocellular carcinoma (HCC) is one of the deadliest cancers in the world. Progress in the Human Liver Proteome Project (HLPP) has improved understanding of the liver and the liver cancer proteome. AREAS COVERED: Here, we summarize the recent progress in liver proteome modification profiles, proteomic studies in liver cancer, proteomic study in the search for novel liver cancer biomarkers and drug targets, and progress of the Chromosome Centric Human Proteome Project (CHPP) in the past five years in the Institutes of Biomedical Sciences (IBS) of Fudan University. Expert commentary: Recent advances and findings discussed here provide great promise of improving the outcome of patients with liver cancer.

Controlling nonspecific trypsin cleavages in LC-MS/MS-based shotgun proteomics using optimized experimental conditions.

Trypsin has traditionally been used for enzymatic digestion during sample preparation in shotgun proteomics. The stringent specificity of trypsin is essential for accurate protein identification and quantification. But nonspecific trypsin cleavages are often observed in LC-MS/MS-based shotgun proteomics. To explore the extent of nonspecific trypsin cleavages, a series of biological systems including a standard protein mixture, Saccharomyces cerevisiae, human serum, human cancer cell lines and mouse brain were examined. We found that nonspecific trypsin cleavages commonly occurred in various trypsin digested samples with high frequency. To control these nonspecific trypsin cleavages, we optimized fundamental parameters during sample preparation with mouse brain homogenates. These parameters included denaturing agents and protein storage time, trypsin type, enzyme-to-substrate ratio, as well as protein concentration during digestion. The optimized experimental conditions significantly decreased the ratio of partially tryptic peptides in total identifications from 28.4% to 2.8%. Furthermore, the optimized digestion protocol was applied to the study of N-glycoproteomics, and the proportions of partially tryptic peptides in enriched mixtures were also sharply reduced. Our work demonstrates the importance of controlling nonspecific trypsin cleavages in both shotgun proteomics and glycoproteomics and provides a better understanding and standardization for routine proteomics sample treatment.

Transcriptome and proteome of human hepatocellular carcinoma reveal shared metastatic pathways with significant genes.

Previously isolated pathways screened from individual genes were investigated at either the transcriptional or translational level; however, the consistency between the pathways screened at the gene expression levels was obscure in metastatic human hepatocellular carcinoma (HCC). To elucidate this question, we performed a transcriptomic (16,353 genes) and proteomic (7861 proteins) analysis simultaneously on six metastatic HCC cell lines against two nonmetastatic HCC cell lines, with all HBV traceable and close genetic-backgrounds for a comparative study. The quantitative and integrated results showed that significant genes were screened differentially with 351 transcripts from the transcriptome and 304 proteins from the proteome, with limited overlapping genes (7%). However, we discovered that these discrete 351 transcripts and 304 proteins screened share extrusive significant-pathways/networks with a 77% overlap, including active TGF-ß, RAS, NFκB, and Wnt, and inactive HNF4A, which are responsible for HCC metastasis. We conclude that the discrete, but significant genes predicted by either ome play intrinsically important roles in the linkage of responsible pathways shared by both omes in HCC metastasis.

Systematic comparison between SDS-PAGE/RPLC and high-/low-pH RPLC coupled tandem mass spectrometry strategies in a whole proteome analysis.

SDS-PAGE and high-pH RPLC are commonly used fractionation strategies in proteomics research. A comparative investigation of these two strategies would be meaningful to thoroughly understand their respective features. Here, we systematically compared the two methods by trying 4 SDS-PAGE/RPLC and 3 high-/low-pH RPLC different workflows for a higher sensitivity in protein identification. Totally 9793 proteins were identified in HepG2 cells, with 8581 proteins identified by high-/low-pH RPLC workflows and 7933 by SDS-PAGE/RPLC workflows. The results demonstrate that using high-pH RPLC in the first dimensional separation would favour a high-throughput proteome analysis but choosing SDS-PAGE could yield much better peptide coverage. We found that the SDS-PAGE fractionation method benefits the neutral pI peptides. We also analyzed unexpected modifications caused by the two strategies. Our results suggest that more pre-fractionation benefits protein identifications in both strategies and pooling of gel pieces according to their grey values increased the identification efficiency in SDS-PAGE/RPLC workflows.

Site-specific structural characterization of O-glycosylation and identification of phosphorylation sites of recombinant osteopontin.

Osteopontin (OPN) plays a key role in multiple physiological and pathological processes such as cytokine production, mineralization, inflammation, immune responses, and tumorigenesis. Post-translational modifications (PTMs) of OPN significantly affect its structure and biological properties; however, site-specific characterization of O-glycosylation in human OPN has not been reported. In this work, we profiled the overall glycan pattern of human recombinant OPN using a lectin array and completed detailed structural analysis of O-glycopeptides by mass spectrometry (MS). We detected 28 O-glycopeptides from 7 O-glycosylation regions of human OPN, occupied by highly heterogeneous O-glycans. These O-glycans carried, in part, functionally relevant epitopes such as T antigens (Galß1-3GalNAcα1-), sialyl-Tn antigens, sialyl-T antigens, and sialyl-Le(x/a) antigens [Neuα2-3Galß1-4(Fucα1-3)GlcNAc/Neuα2-3Galß1-3(Fucα1-4)GlcNAc]. MS(3) spectra of the generated O-glycopeptides showed cleavages of the peptide backbone and provided essential information on the peptide sequence. Furthermore, 26 phosphorylation sites were identified by reverse-phase liquid chromatography-tandem mass spectrometry (RPLC-MS/MS), including a novel one (Y209). We provide a detailed, site-specific structural characterization of O-glycosylation and identify the phosphorylation sites of OPN. These data lay the foundation for further research into the role of oligosaccharides and phosphorylation of recombinant human OPN. This article is part of a Special Issue entitled: Medical Proteomics.

CAV1 promotes HCC cell progression and metastasis through Wnt/ß-catenin pathway.

Caveolin-1 (CAV1) has significant roles in many primary tumors and metastasis, despite the fact that malignant cells from different cancer types have different profiles of CAV1 expression. There is little information concerning CAV1 expression and role in hepatocellular carcinoma (HCC) progresion and metastasis. The role of CAV1 in HCC progression was explored in this study. We reported that CAV1 was overexpressed in highly invasive HCC cell lines compared with poorly invasive ones. The immunohistochemical staining was obviously stronger in metastatic HCC samples than in the non-metastatic specimens via tissue microarrays. Furthermore, CAV1 overexpression enhanced HCC cell invasiveness in vitro, and promoted tumorigenicity and lung metastasis in vivo. By contrast, CAV1 stable knockdown markedly reduced these malignant behaviors. Importantly, we found that CAV1 could induce EMT process through Wnt/ß-catenin pathway to promote HCC metastasis. We also identify MMP-7 as a novel downstream target of CAV1. We have determined that CAV1 acts as a mediator between hyperactive ERK1/2 signaling and regulation of MMP-7 transcription. Together, these studies mechanistically show a previously unrecognized interplay between CAV1, EMT, ERK1/2 and MMP-7 that is likely significant in the progression of HCC toward metastasis.

Omics evidence: single nucleotide variants transmissions on chromosome 20 in liver cancer cell lines.

Cancer genomics unveils many cancer-related mutations, including some chromosome 20 (Chr.20) genes. The mutated messages have been found in the corresponding mRNAs; however, whether they could be translated to proteins still requires more evidence. Herein, we proposed a transomics strategy to profile the expression status of human Chr.20 genes (555 in Ensembl v72). The data of transcriptome and translatome (the mRNAs bound with ribosome, translating mRNAs) revealed that ∼80% of the coding genes on Chr.20 were detected with mRNA signals in three liver cancer cell lines, whereas of the proteome identified, only ∼45% of the Chr.20 coding genes were detected. The high amount of overlapping of identified genes in mRNA and RNC-mRNA (ribosome nascent-chain complex-bound mRNAs, translating mRNAs) and the consistent distribution of the abundance averages of mRNA and RNC-mRNA along the Chr.20 subregions in three liver cancer cell lines indicate that the mRNA information is efficiently transmitted from transcriptional to translational stage, qualitatively and quantitatively. Of the 457 genes identified in mRNAs and RNC-mRNA, 136 were found to contain SNVs with 213 sites, and >40% of these SNVs existed only in metastatic cell lines, suggesting them as the metastasis-related SNVs. Proteomics analysis showed that 16 genes with 20 SNV sites were detected with reliable MS/MS signals, and some SNVs were further validated by the MRM approach. With the integration of the omics data at the three expression phases, therefore, we are able to achieve the overall view of the gene expression of Chr.20, which is constructive in understanding the potential trend of encoding genes in a cell line and exploration of a new type of markers related to cancers.

Systematic analyses of the transcriptome, translatome, and proteome provide a global view and potential strategy for the C-HPP.

To estimate the potential of the state-of-the-art proteomics technologies on full coverage of the encoding gene products, the Chinese Human Chromosome Proteome Consortium (CCPC) applied a multiomics strategy to systematically analyze the transciptome, translatome, and proteome of the same cultured hepatoma cells with varied metastatic potential qualitatively and quantitatively. The results provide a global view of gene expression profiles. The 9064 identified high confident proteins covered 50.2% of all gene products in the translatome. Those proteins with function of adhesion, development, reproduction, and so on are low abundant in transcriptome and translatome but absent in proteome. Taking the translatome as the background of protein expression, we found that the protein abundance plays a decisive role and hydrophobicity has a greater influence than molecular weight and isoelectric point on protein detectability. Thus, the enrichment strategy used for low-abundant transcription factors helped to identify missing proteins. In addition, those peptides with single amino acid polymorphisms played a significant role for the disease research, although they might negligibly contribute to new protein identification. The proteome raw and metadata of proteome were collected using the iProX submission system and submitted to ProteomeXchange (PXD000529, PXD000533, and PXD000535). All detailed information in this study can be accessed from the Chinese Chromosome-Centric Human Proteome Database.

Systematic analysis of missing proteins provides clues to help define all of the protein-coding genes on human chromosome 1.

Our first proteomic exploration of human chromosome 1 began in 2012 (CCPD 1.0), and the genome-wide characterization of the human proteome through public resources revealed that 32-39% of proteins on chromosome 1 remain unidentified. To characterize all of the missing proteins, we applied an OMICS-integrated analysis of three human liver cell lines (Hep3B, MHCC97H, and HCCLM3) using mRNA and ribosome nascent-chain complex-bound mRNA deep sequencing and proteome profiling, contributing mass spectrometric evidence of 60 additional chromosome 1 gene products. Integration of the annotation information from public databases revealed that 84.6% of genes on chromosome 1 had high-confidence protein evidence. Hierarchical analysis demonstrated that the remaining 320 missing genes were either experimentally or biologically explainable; 128 genes were found to be tissue-specific or rarely expressed in some tissues, whereas 91 proteins were uncharacterized mainly due to database annotation diversity, 89 were genes with low mRNA abundance or unsuitable protein properties, and 12 genes were identifiable theoretically because of a high abundance of mRNAs/RNC-mRNAs and the existence of proteotypic peptides. The relatively large contribution made by the identification of enriched transcription factors suggested specific enrichment of low-abundance protein classes, and SRM/MRM could capture high-priority missing proteins. Detailed analyses of the differentially expressed genes indicated that several gene families located on chromosome 1 may play critical roles in mediating hepatocellular carcinoma invasion and metastasis. All mass spectrometry proteomics data corresponding to our study were deposited in the ProteomeXchange under the identifiers PXD000529, PXD000533, and PXD000535.

An iTRAQ based quantitative proteomic strategy to explore novel secreted proteins in metastatic hepatocellular carcinoma cell lines.

Secretomics is receiving more and more considerable attention due to the key roles of secreted proteins in cancer. Most of the potential biomarkers for clinical diagnosis and treatment of cancer are secreted proteins. However, the low concentration of secreted proteins and contaminants released from dead cells are a great challenge to secretomic profiling studies. Although some bioinformatics tools such as SecretomeP and SignalP can help to annotate or predict secreted proteins, they also cause false positive or negative rates of identification especially for nonclassical secreted proteins. Therefore, an iTRAQ based quantitative proteomics strategy was set up in this work and applied in the secretomics study of metastatic HCC cell lines. A total of 94 proteins were identified as secreted and 31 of them were newly found in our data. Compared with the known secreted proteins participating in inter-cellular signalling, most of the newly identified secreted proteins were metabolic enzymes, such as PKM2 and EHHADH, whose functions focused on the synthesis/metabolism of glucose, fatty acids and amino acids. Exploring their secretion would help to further study their bio-functions in conditioned media and the effects on the interactions of cancer cells and the microenvironment. Differences between the secretomes of the two metastatic HCC cell lines were also explored in the same experiment. This strategy showed its superiority in accurately identifying secreted proteins as well as monitoring their variation under different biological conditions.