Discovery and Qualification of Serum Protein Biomarker Candidates for Cholangiocarcinoma Diagnosis

Biomarker potential of plasma cell-free DNA for cholangiocarcinoma

Background

To prevent the development of cholangiocarcinoma, an effective screening opisthorchiasis viverrini and/or differential diagnosis of and the cholangiocarcinoma is crucial needed. The level and quality of cfDNA in plasma are being investigated for their potential role as biomarkers in cholangiocarcinoma.

Methods

The study enrolled 43 healthy controls (N), 36 O. viverrini-infected subjects (OV), and 36 cholangiocarcinoma patients (CCA). Plasma cfDNA was quantified by fluorometry (Qubit 4), and qualified analysis, including % tumor fraction, ctDNA, ploidy number, and somatic copy number alteration (SCNA), was conducted using ULP-WGS and analyzed by iChorCNA. The statistical analysis and comparison among the groups were performed.

Results

The results showed that cfDNA could effectively differentiate between N and OV from CCA statistically both by DNA amount and quality. Using a cut-off of >20.94 ng/ml, the sensitivity and specificity of the cfDNA concentration were determined to be 86.11% and 98.73% for the differential diagnosis of cholangiocarcinoma, respectively. The ULP-WGS with iChorCNA indicated the % tumor fraction of cfDNA (P < 0.001) and the values of the ploidy number (P < 0.001) of the cholangiocarcinoma group and the other groups were statistically significant. Moreover, the SCNA of the cholangiocarcinoma group was shown to be significantly high in comparison to that of the healthy control group with an odds ratio of 11.688 (P < 0.001).

Conclusion

The use of cfDNA concentration and ULP-WGS for analyzing DNA quality including % tumor fraction, ctDNA concentration, tumor ploidy, and SCNA are useful for the differential diagnosis of cholangiocarcinoma from opisthorchiasis viverrini and healthy individuals.

A multi-biomarker panel for predicting Tocilizumab response in Rheumatoid arthritis patients

Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease characterized by inflammation in the synovial lining of the joints. Key inflammatory cytokines such as interleukin-6 (IL-6), TNF-α, and others play a critical role in the activation of local synovial leukocytes and the induction of chronic inflammation. Tocilizumab (TCZ), a humanized anti-IL-6 receptor monoclonal antibody, has demonstrated significant clinical efficacy in treating RA patients. However, similar to other inflammatory cytokine blockers, such as TNF-alpha inhibitors, Interleukin-1 inhibitors, or CD20 inhibitors, some patients do not respond to treatment. To address this challenge, our study employed a high-precision proteomics approach to identify protein biomarkers capable of predicting clinical responses to Tocilizumab in RA patients. Through the use of data-independent acquisition (DIA) mass spectrometry, we analyzed serum samples from both TCZ responders and non-responders to discover potential biomarker candidates. These candidates were subsequently validated using individual serum samples from two independent cohorts: a training set (N = 70) and a test set (N = 18), allowing for the development of a robust multi-biomarker panel. The constructed multi-biomarker panel demonstrated an average discriminative power of 86 % between response and non-response groups, with a high area under the curve (AUC) value of 0.84. Additionally, the panel exhibited 100 % sensitivity and 60 % specificity. Collectively, our multi-biomarker panel holds promise as a diagnostic tool to predict non-responders to TCZ treatment in RA patients.

Advances in omics data for eosinophilic esophagitis: moving towards multi-omics analyses

Eosinophilic esophagitis (EoE) is a chronic, allergic inflammatory disease of the esophagus characterized by eosinophil accumulation and has a growing global prevalence. EoE significantly impairs quality of life and poses a substantial burden on healthcare resources. Currently, only two FDA-approved medications exist for EoE, highlighting the need for broader research into its management and prevention. Recent advancements in omics technologies, such as genomics, epigenetics, transcriptomics, proteomics, and others, offer new insights into the genetic and immunologic mechanisms underlying EoE. Genomic studies have identified genetic loci and mutations associated with EoE, revealing predispositions that vary by ancestry and indicating EoE's complex genetic basis. Epigenetic studies have uncovered changes in DNA methylation and chromatin structure that affect gene expression, influencing EoE pathology. Transcriptomic analyses have revealed a distinct gene expression profile in EoE, dominated by genes involved in activated type 2 immunity and epithelial barrier function. Proteomic approaches have furthered the understanding of EoE mechanisms, identifying potential new biomarkers and therapeutic targets. However, challenges in integrating diverse omics data persist, largely due to their complexity and the need for advanced computational methods. Machine learning is emerging as a valuable tool for analyzing extensive and intricate datasets, potentially revealing new aspects of EoE pathogenesis. The integration of multi-omics data through sophisticated computational approaches promises significant advancements in our understanding of EoE, improving diagnostics, and enhancing treatment effectiveness. This review synthesizes current omics research and explores future directions for comprehensively understanding the disease mechanisms in EoE.

Plasma Proteomics of Type 2 Diabetes, Hypertension, and Co-Existing Diabetes/Hypertension in Thai Adults

The study explored proteomics to better understand the relationship between type 2 diabetes (T2DM) and hypertension (HT) in Thai adults, using shotgun proteomics and bioinformatics analysis. Plasma samples were taken from 61 subjects: 14 healthy subjects (mean age = 40.85 ± 7.12), 13 with T2DM (mean age = 57.38 ± 6.03), 16 with HT (mean age = 66.87 ± 10.09), and 18 with coexisting T2DM/HT (mean age = 58.22 ± 10.65). Proteins were identified using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Protein-protein interactions were analyzed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) version 11.5. We identified six unique proteins in T2DM patients, including translationally controlled 1 (TPT1) and nibrin (NBN), which are associated with the DNA damage response. In HT patients, seven unique proteins were identified, among them long-chain fatty acid-CoA ligase (ASCL), which functions in the stimulation of triacylglycerol and cholesterol synthesis, and NADPH oxidase activator 1 (NOXA1), which is involved in high blood pressure via angiotensin II-induced reactive oxygen species (ROS)-generating systems. In coexisting T2DM/HT patients, six unique proteins were identified, of which two-microtubule-associated protein 1A (MAP1A)-might be involved in dementia via RhoB-p53 and diacylglycerol kinase beta (DGKB), associated with lipid metabolism. This study identified new candidate proteins that are possibly involved in the pathology of these diseases.

Exploring the Thioredoxin System as a Therapeutic Target in Cancer: Mechanisms and Implications

Cells constantly face the challenge of managing oxidants. In aerobic organisms, oxygen (O2) is used for energy production, generating reactive oxygen species (ROS) as byproducts of enzymatic reactions. To protect against oxidative damage, cells possess an intricate system of redox scavengers and antioxidant enzymes, collectively forming the antioxidant defense system. This system maintains the redox equilibrium and enables the generation of localized oxidative signals that regulate essential cellular functions. One key component of this defense is the thioredoxin (Trx) system, which includes Trx, thioredoxin reductase (TrxR), and NADPH. The Trx system reverses oxidation of macromolecules and indirectly neutralizes ROS via peroxiredoxin (Prx). This dual function protects cells from damage accumulation and supports physiological cell signaling. However, the Trx system also shields tumors from oxidative damage, aiding their survival. Due to elevated ROS levels from their metabolism, tumors often rely on the Trx system. In addition, the Trx system regulates critical pathways such as proliferation and neoangiogenesis, which tumors exploit to enhance growth and optimize nutrient and oxygen supply. Consequently, the Trx system is a potential target for cancer therapy. The challenge lies in selectively targeting malignant cells without disrupting the redox equilibrium in healthy cells. The aim of this review article is threefold: first, to elucidate the function of the Trx system; second, to discuss the Trx system as a potential target for cancer therapies; and third, to present the possibilities for inhibiting key components of the Trx system, along with an overview of the latest clinical studies on these inhibitors.

The Importance of Thioredoxin-1 in Health and Disease

Thioredoxin-1 (Trx-1) is a multifunctional protein ubiquitously found in the human body. Trx-1 plays an important role in various cellular functions such as maintenance of redox homeostasis, proliferation, and DNA synthesis, but also modulation of transcription factors and control of cell death. Thus, Trx-1 is one of the most important proteins for proper cell and organ function. Therefore, modulation of Trx gene expression or modulation of Trx activity by various mechanisms, including post-translational modifications or protein-protein interactions, could cause a transition from the physiological state of cells and organs to various pathologies such as cancer, and neurodegenerative and cardiovascular diseases. In this review, we not only discuss the current knowledge of Trx in health and disease, but also highlight its potential function as a biomarker.

Differential plasma proteomes of the patients with Opisthorchiasis viverrini and cholangiocarcinoma identify a polymeric immunoglobulin receptor as a potential biomarker

In Southeast Asian countries, nitrosamine compounds and the liver fluke Opisthorchis viverrini have long been identified as carcinogens for cholangiocarcinoma (CHCA). In order to effectively treat O. viverrini infections and prevent the development of CHCA, methods for disease detection are needed. This study aims to identify biomarkers for O. viverrini infection and CHCA. In the discovery phase, technical triplicates of five pooled plasma pools (10 plasma each) of healthy control subjects (noOVCCA), O. viverrini subjects (OV), and cholangiocarcinoma subjects (CCA), underwent solution-based digestion, with the label-free method, using a Thermo Scientific™ Q Exactive™ HF hybrid quadrupole-Orbitrap mass spectrometer and UltiMate 300 LC systems. The noOVCCA, OV, and CCA groups demonstrated different profiles and were clustered, as illustrated by PCA and heat map analysis. The STRING and reactome analysis showed that both OV and CCA groups up-regulated proteins targeting immune system-related proteins. Differential proteomic profiles, S100A9, and polymeric immunoglobulin receptor (PIGR) were specifically expressed in the CCA group. During the validation phase, another 50 plasma samples were validated via the PIGR sandwich ELISA. Using PIGR >1.559 ng/ml as a cut-off point, 78.00% sensitivity, 71.00% specificity, and AUC = 0.8216, were obtained. It is sufficient to differentially diagnose cholangiocarcinoma patients from healthy patients and those with Opisthorchiasis viverrini. Hence, in this study, PIGR was identified and validated as a potential biomarker for CHCA. Plasma PIGR is suggested for screening CHCA, especially in an endemic region of O. viverrini infection.

Translational Proteomic Approach for Cholangiocarcinoma Biomarker Discovery, Validation, and Multiplex Assay Development: A Pilot Study

Cholangiocarcinoma (CCA) is a highly lethal disease because most patients are asymptomatic until they progress to advanced stages. Current CCA diagnosis relies on clinical imaging tests and tissue biopsy, while specific CCA biomarkers are still lacking. This study employed a translational proteomic approach for the discovery, validation, and development of a multiplex CCA biomarker assay. In the discovery phase, label-free proteomic quantitation was performed on nine pooled plasma specimens derived from nine CCA patients, nine disease controls (DC), and nine normal individuals. Seven proteins (S100A9, AACT, AFM, and TAOK3 from proteomic analysis, and NGAL, PSMA3, and AMBP from previous literature) were selected as the biomarker candidates. In the validation phase, enzyme-linked immunosorbent assays (ELISAs) were applied to measure the plasma levels of the seven candidate proteins from 63 participants: 26 CCA patients, 17 DC, and 20 normal individuals. Four proteins, S100A9, AACT, NGAL, and PSMA3, were significantly increased in the CCA group. To generate the multiplex biomarker assays, nine machine learning models were trained on the plasma dynamics of all seven candidates (All-7 panel) or the four significant markers (Sig-4 panel) from 45 of the 63 participants (70%). The best-performing models were tested on the unseen values from the remaining 18 (30%) of the 63 participants. Very strong predictive performances for CCA diagnosis were obtained from the All-7 panel using a support vector machine with linear classification (AUC = 0.96; 95% CI 0.88–1.00) and the Sig-4 panel using partial least square analysis (AUC = 0.94; 95% CI 0.82–1.00). This study supports the use of the composite plasma biomarkers measured by clinically compatible ELISAs coupled with machine learning models to identify individuals at risk of CCA. The All-7 and Sig-4 assays for CCA diagnosis should be further validated in an independent prospective blinded clinical study.

Diagnosis Biomarkers of Cholangiocarcinoma in Human Bile: An Evidence-Based Study

Simple Summary

A liquid biopsy has the characteristics of low trauma and easy acquisition in the diagnosis of cholangiocarcinoma. Many researchers try to find diagnostic or prognostic biomarkers of CCA through blood, urine, bile and other body fluids. Due to the close proximity of bile to the lesion and the stable nature, bile gradually comes into people’s view. The evaluation of human bile diagnostic biomarkers is not only to the benefit of screening more suitable clinical markers but also of exploring the pathological changes of the disease.

Abstract

Cholangiocarcinoma (CCA) is a multifactorial malignant tumor of the biliary tract, and the incidence of CCA is increasing in recent years. At present, the diagnosis of CCA mainly depends on imaging and invasive examination, with limited specificity and sensitivity and late detection. The early diagnosis of CCA always faces the dilemma of lacking specific diagnostic biomarkers. Non-invasive methods to assess the degree of CAA have been developed throughout the last decades. Among the many specimens looking for CCA biomarkers, bile has gotten a lot of attention lately. This paper mainly summarizes the recent developments in the current research on the diagnostic biomarkers for CCA in human bile at the levels of the gene, protein, metabolite, extracellular vesicles and volatile organic compounds.

In-Depth Serum Proteomics by DIA-MS with In Silico Spectral Libraries Reveals Dynamics during the Active Phase of Systemic Juvenile Idiopathic Arthritis

In serum proteomics using mass spectrometry, the number of detectable proteins is reduced due to high-abundance proteins, such as albumin. However, recently developed data-independent acquisition mass spectrometry (DIA-MS) proteomics technology has made it possible to remarkably improve the number of proteins in a serum analysis by removing high-abundance proteins. Using this technology, we analyzed sera from patients with systemic juvenile idiopathic arthritis (sJIA), a rare pediatric disease. As a result, we identified 2727 proteins with a wide dynamic range derived from various tissue leakages. We also selected 591 proteins that differed significantly in their active phases. These proteins were involved in many inflammatory processes, and we also identified immunoproteasomes, which were not previously found in serum, suggesting that they may be involved in the pathogenesis of sJIA. A detailed high-depth DIA-MS proteomic analysis of serum may be useful for understanding the pathogenesis of sJIA and may provide clues for the development of new biomarkers.

Data analysis methods for defining biomarkers from omics data

Omics mainly includes genomics, epigenomics, transcriptomics, proteomics and metabolomics. The rapid development of omics technology has opened up new ways to study disease diagnosis and prognosis and to define prospective information of complex diseases. Since omics data are usually large and complex, the method used to analyze the data and to define important information is crucial in omics study. In this review, we focus on advances in biomarker discovery methods based on omics data in the last decade, and categorize them as individual feature analysis, combinatorial feature analysis and network analysis. We also discuss the challenges and perspectives in this field.

Plasma checkpoint protein 1 (Chk1) as a potential diagnostic biomarker for opisthorchiasis and cholangiocarcinoma

BACKGROUND

Patients infected with a parasite often develop opisthorchiasis viverrini, which often progresses into cholangiocarcinoma (CCA) due to the asymptomatic nature of the infection. Currently, there are no effective diagnostic methods for opisthorchiasis or cholangiocarcinoma.

OBJECTIVE

The aim of this study was to identify the host-responsive protein that can be developed as a diagnostic biomarker of opisthorchiasis and cholangiocarcinoma.

METHODS

Plasma samples were collected from non-OVCCA, OV, and CCA subjects, and the proteomes were investigated by LC-MS/MS. Venn diagrams and protein network prediction by STITCH were used to identify the potential biomarkers. The level of candidate protein, the plasma checkpoint protein 1 (Chk1), was measured by indirect enzyme-linked immunosorbent assay (ELISA).

RESULTS

Chk1 was present in the center of the protein network analysis in both the OV and CCA groups. In addition, the plasma Chk1 levels were significantly increased in both groups (P< 0.05). The sensitivity of the opisthorchiasis viverrini and cholangiocarcinoma was 59.38% and 65.62%, respectively, while the specificity of both was 85.71%.

CONCLUSION

Chk1 was identified by differential plasma proteomes and was increased in O. viverrini-infected and cholangiocarcinoma-derived plasma samples. Higher levels of plasma Chk1 levels may serve as a potential diagnostic biomarker for opisthorchiasis and cholangiocarcinoma.

Therapeutic challenges at the preclinical level for targeted drug development for Opisthorchis viverrini-associated cholangiocarcinoma

INTRODUCTION

Cholangiocarcinoma (CCA) is a malignant tumor of bile duct epithelium with the highest incidence found in Thailand. Some patients are considered suitable for adjuvant therapy and surgical resection is currently the curative treatment for CCA patients. Tumor recurrence is still a hurdle after treatment; hence, finding novel therapeutic strategies to combat CCA is necessary for improving outcome for patients.

AREAS COVERED

We discuss targeted therapies and other novel treatment approaches which include protein kinase inhibitors, natural products, amino acid transporter-based inhibitors, immunotherapy, and drug repurposing. We also examine the challenges of tumor heterogeneity, cancer stem cells (CSCs), the tumor microenvironment, exosomes, multiomics studies, and the potential of precision medicine.

EXPERT OPINION

Because CCA is difficult to diagnose at the early stage, the traditional treatment approaches are not effective for many patients and most tumors recur. Consequently, researchers are exploring multi-aspect molecular carcinogenesis to uncover molecular targets for further development of novel targeted drugs.

Ex vivo glucocorticoid-induced secreted proteome approach for discovery of glucocorticoid-responsive proteins in human serum

PURPOSE

To identify glucocorticoid-responsive proteins measurable in human serum that may have clinical utility in therapeutic drug monitoring and the diagnosis of cortisol excess or deficiency.

EXPERIMENTAL DESIGN

A phased biomarker discovery strategy was conducted in two cohorts. Secretome from peripheral blood mononuclear cells (PBMC) isolated from six volunteers after ex vivo incubation ± dexamethasone (DEX) 100 ng/mL for 4 h and 24 h was used for candidate discovery and qualification using untargeted proteomics and a custom multiple reaction monitoring mass spectrometry (MRM-MS) assay, respectively. For validation, five candidates were measured by immunoassay in serum from an independent cohort (n = 20), sampled at 1200 h before and after 4 mg oral DEX.

RESULTS

The discovery secretome proteomics data generated a shortlist of 45 candidates, with 43 measured in the final MRM-MS assay. Differential analysis revealed 16 proteins that were significant in at least one of two time points. In the validation cohort, 3/5 serum proteins were DEX-responsive, two significantly decreased: lysozyme C (p < 0.0001) and nucleophosmin-1 (p < 0.01), while high mobility group box 2 significantly increased (p < 0.01).

CONCLUSIONS AND CLINICAL RELEVANCE

Using an ex vivo proteomic approach in PBMC, we have identified circulating glucocorticoid-responsive proteins which may have potential as serum biomarkers of glucocorticoid activity.

Establishment of a Potential Serum Biomarker Panel for the Diagnosis and Prognosis of Cholangiocarcinoma Using Decision Tree Algorithms

Potential biomarkers which include S100 calcium binding protein A9 (S100A9), mucin 5AC (MUC5AC), transforming growth factor β1 (TGF-β1), and angiopoietin-2 have previously been shown to be effective for cholangiocarcinoma (CCA) diagnosis. This study attempted to measure the sera levels of these biomarkers compared with carbohydrate antigen 19-9 (CA19-9). A total of 40 serum cases of CCA, gastrointestinal cancers (non-CCA), and healthy subjects were examined by using an enzyme-linked immunosorbent assay. The panel of biomarkers was evaluated for their accuracy in diagnosing CCA and subsequently used as inputs to construct the decision tree (DT) model as a basis for binary classification. The findings showed that serum levels of S100A9, MUC5AC, and TGF-β1 were dramatically enhanced in CCA patients. In addition, 95% sensitivity and 90% specificity for CCA differentiation from healthy cases, and 70% sensitivity and 83% specificity for CCA versus non-CCA cases was obtained by a panel incorporating all five candidate biomarkers. In CCA patients with low CA19-9 levels, S100A9 might well be a complementary marker for improved diagnostic accuracy. The high levels of TGF-β1 and angiopoietin-2 were both associated with severe tumor stages and metastasis, indicating that they could be used as a reliable prognostic biomarkers panel for CCA patients. Furthermore, the outcome of the CCA burden from the Classification and Regression Tree (CART) algorithm using serial CA19-9 and S100A9 showed high diagnostic efficiency. In conclusion, results have shown the efficacy of CCA diagnosis and prognosis of the novel CCA-biomarkers panel examined herein, which may prove be useful in clinical settings.

Triqler for MaxQuant: Enhancing Results from MaxQuant by Bayesian Error Propagation and Integration

Error estimation for differential protein quantification by label-free shotgun proteomics is challenging due to the multitude of error sources, each contributing uncertainty to the final results. We have previously designed a Bayesian model, Triqler, to combine such error terms into one combined quantification error. Here we present an interface for Triqler that takes MaxQuant results as input, allowing quick reanalysis of already processed data. We demonstrate that Triqler outperforms the original processing for a large set of both engineered and clinical/biological relevant data sets. Triqler and its interface to MaxQuant are available as a Python module under an Apache 2.0 license from https://pypi.org/project/triqler/.

Use of data-independent acquisition mass spectrometry for comparative proteomics analyses of sera from pregnant women with intrahepatic cholestasis of pregnancy

We used data-independent acquisition (DIA) proteomics technology followed by ELISAs and automated biochemical analyses to identify and validate protein expression levels in Intrahepatic Cholestasis of Pregnancy (ICP) and healthy pregnant controls. We employed bioinformatics to identify metabolic processes associated with differentially expressed proteins.The expression levels of two proteins (S100-A9 and the L-lactate dehydrogenase A chain) were significantly higher in ICP patients than in controls; the areas under the receiver operating characteristic (ROC) curves (AUCs) were 0.774 and 0.828, respectively. The expression levels of two other proteins (apolipoprotein A-I and cholinesterase) were significantly lower in patients, with values of 0.900 and 0.842, respectively. Multiple logistic regression showed that a combination of the levels of the four proteins optimized the AUC (0.962), thus more reliably diagnosing ICP. The levels of all four proteins were positively associated with that of total bile acids. Bioinformatics analyses indicated that the four proteins principally affected neutrophil activation involved in the immune response, cell adhesion, lipoprotein metabolism, and the PPAR signaling pathway. SIGNIFICANCE: This preliminary work improves our understanding of changes in serum levels of protein in pregnant women with ICP. The four proteins may serve as novel noninvasive biomarkers for ICP.

Identification of TUBB2A by quantitative proteomic analysis as a novel biomarker for the prediction of distant metastatic breast cancer

Background

Metastasis of breast cancer to distal organs is fatal. However, few studies have identified biomarkers that are associated with distant metastatic breast cancer. Furthermore, the inability of current biomarkers, such as HER2, ER, and PR, to differentiate between distant and nondistant metastatic breast cancers accurately has necessitated the development of novel biomarker candidates.

Methods

An integrated proteomics approach that combined filter-aided sample preparation, tandem mass tag labeling (TMT), high pH fractionation, and high-resolution MS was applied to acquire in-depth proteomic data from FFPE distant metastatic breast cancer tissues. A bioinformatics analysis was performed with regard to gene ontology and signaling pathways using differentially expressed proteins (DEPs) to examine the molecular characteristics of distant metastatic breast cancer. In addition, real-time polymerase chain reaction (RT-PCR) and invasion/migration assays were performed to validate the differential regulation and function of our protein targets.

Results

A total of 9441 and 8746 proteins were identified from the pooled and individual sample sets, respectively. Based on our criteria, TUBB2A was selected as a novel biomarker candidate. The metastatic activities of TUBB2A were subsequently validated. In our bioinformatics analysis using DEPs, we characterized the overall molecular features of distant metastasis and measured differences in the molecular functions of distant metastatic breast cancer between breast cancer subtypes.

Conclusions

Our report is the first study to examine the distant metastatic breast cancer proteome using FFPE tissues. The depth of our dataset allowed us to discover a novel biomarker candidate and a proteomic characteristics of distant metastatic breast cancer. Distinct molecular features of various breast cancer subtypes were also established. Our proteomic data constitute a valuable resource for research on distant metastatic breast cancer.

Discovery of Potential Plasma Biomarkers for Tuberculosis in HIV-Infected Patients by Data-Independent Acquisition-Based Quantitative Proteomics

Purpose

Tuberculosis (TB) is the leading cause of mortality in individuals infected with human immunodeficiency virus (HIV), yet the methods for detecting Mycobacterium tuberculosis at an early stage remain insensitive or ineffective. This study aimed to discover plasma biomarkers for distinguishing HIV-TB coinfected individuals from HIV individuals without TB (HIV-nonTB).

Patients and Methods

A total of 200 Chinese HIV-positive patients were recruited, 100 each for HIV-nonTB group and HIV-TB group. Plasma proteomic profiles were analyzed for 50 patients each in both groups, using data-independent acquisition (DIA)-mass spectrometry-based proteomics. Differently expressed proteins were revealed with ridge regression analysis. Enzyme-linked immunosorbent assay (ELISA) analyses were performed for further validation in other 100 patients.

Results

DIA-mass spectrometry revealed 13 upregulated and 33 downregulated proteins in the HIV-TB group. AMACR (α-methylacyl-CoA racemase), LDHB (L-lactate dehydrogenase B chain), and RAP1B (Ras-related protein Rap-1b) were selected for building a diagnostic model, for which the receiver operation characteristic curve had under areas of 0.99 and 0.89 testing with proteomics data (sensitivity = 92%, specificity = 100%) and ELISA data (sensitivity = 76%, specificity = 92%), respectively.

Conclusion

The combination of AMACR, LDHB, and RAP1B proteins may serve as a potential marker of TB in HIV-infected patients.

Construction and Validation of a Protein Prognostic Model for Lung Squamous Cell Carcinoma

Lung squamous cell carcinoma (LUSCC), as the major type of lung cancer, has high morbidity and mortality rates. The prognostic markers for LUSCC are much fewer than lung adenocarcinoma. Besides, protein biomarkers have advantages of economy, accuracy and stability. The aim of this study was to construct a protein prognostic model for LUSCC. The protein expression data of LUSCC were downloaded from The Cancer Protein Atlas (TCPA) database. Clinical data of LUSCC patients were downloaded from The Cancer Genome Atlas (TCGA) database. A total of 237 proteins were identified from 325 cases of LUSCC patients based on the TCPA and TCGA database. According to Kaplan-Meier survival analysis, univariate and multivariate Cox analysis, a prognostic prediction model was established which was consisted of 6 proteins (CHK1_pS345, CHK2, IRS1, PAXILLIN, BRCA2 and BRAF_pS445). After calculating the risk values of each patient according to the coefficient of each protein in the risk model, the LUSCC patients were divided into high risk group and low risk group. The survival analysis demonstrated that there was significant difference between these two groups (p= 4.877e-05). The area under the curve (AUC) value of the receiver operating characteristic (ROC) curve was 0.699, which suggesting that the prognostic risk model could effectively predict the survival of LUSCC patients. Univariate and multivariate analysis indicated that this prognostic model could be used as independent prognosis factors for LUSCC patients. Proteins co-expression analysis showed that there were 21 proteins co-expressed with the proteins in the risk model. In conclusion, our study constructed a protein prognostic model, which could effectively predict the prognosis of LUSCC patients.