Identification and Validation of Stage‐Associated Serum Biomarkers in Colorectal Cancer Using MS‐Based Procedures

Comprehensive serum proteomics profiles and potential protein biomarkers for the early detection of advanced adenoma and colorectal cancer

BACKGROUND

The majority of colorectal cancer (CRC) cases develop from precursor advanced adenoma (AA). With the development of proteomics technologies, blood protein biomarkers have potential applications in the early screening of AA and CRC in the general population.

AIM

To identify serum protein biomarkers for the early screening of AA and CRC.

METHODS

We collected 43 serum samples from 8 normal controls (NCs), 19 AA patients and 16 CRC patients at China-Japan Friendship Hospital. Quantitative proteomic analysis was performed using liquid chromatography-mass spectrometry/mass spectrometry and data independent acquisition, and differentially expressed proteins (DEPs) with P-values < 0.05 and absolute fold changes > 1.5 were screened out, followed by bioinformatics analysis. Prognosis was further analyzed based on public databases, and proteins expression in tissues were validated by immunohistochemistry.

RESULTS

A total of 2132 proteins and 17365 peptides were identified in the serum samples. There were 459 upregulated proteins and 118 downregulated proteins in the NC vs AA group, 289 and 180 in the NC vs CRC group, and 52 and 248 in the AA vs CRC group, respectively. Bioinformatic analysis revealed that these DEPs had different functions and participated in extensive signaling pathways. We also identified DIAPH1, VASP, RAB11B, LBP, SAR1A, TUBGCP5, and DOK3 as important proteins for the progression of AA and CRC. Furthermore, VASP (P < 0.01), LBP (P = 0.01), TUBGCP5 (P < 0.01), and DOK3 (P < 0.01) were associated with a poor prognosis. In addition, we propose that LBP and VASP may be more promising protein biomarkers for the early screening of colorectal tumors.

CONCLUSION

Our study elucidated the serum proteomic profiles of AA and CRC patients, and the identified proteins, such as LBP and VASP, may contribute to the early detection of AA and CRC.

Exploration of the application potential of serum multi-biomarker model in colorectal cancer screening

Analyzing blood lipid and bile acid profile changes in colorectal cancer (CRC) patients. Evaluating the integrated model's diagnostic significance for CRC. Ninety-one individuals with colorectal cancer (CRC group) and 120 healthy volunteers (HC group) were selected for comparison. Serum levels of total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and apolipoproteins (Apo) A1, ApoA2, ApoB, ApoC2, and ApoC3 were measured using immunoturbidimetric and colorimetric methods. Additionally, LC-MS/MS was employed to detect fifteen bile acids in the serum, along with six tumor markers: carcinoembryonic antigen (CEA), carbohydrate antigens (CA) 125, CA19-9, CA242, CA50, and CA72-4. Group comparisons utilized independent sample t-tests and Mann-Whitney U tests. A binary logistic regression algorithm was applied to fit the indicators and establish a screening model; the diagnostic accuracy of individual Indicators and the model was analyzed using receiver operating characteristic (ROC) curves. The CRC group showed significantly lower levels in eight serum lipid indicators and eleven bile acids compared to the HC group (P < 0.05). Conversely, serum levels of TG, CA19-9, and CEA were elevated (P < 0.05). Among the measured parameters, ApoA2 stands out for its strong correlation with the presence of CRC, showcasing exceptional screening efficacy with an area under the curve (AUC) of 0.957, a sensitivity of 85.71%, and a specificity of 93.33%. The screening model, integrating ApoA1, ApoA2, lithocholic acid (LCA), and CEA, attained an impressive AUC of 0.995, surpassing the diagnostic accuracy of individual lipids, bile acids, and tumor markers. CRC patients manifest noteworthy alterations in both blood lipids and bile acid profiles. A screening model incorporating ApoA1, ApoA2, LCA, and CEA provides valuable insights for detecting CRC.

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

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

Integrated meta-analysis of colorectal cancer public proteomic datasets for biomarker discovery and validation

The cancer biomarker field has been an object of thorough investigation in the last decades. Despite this, colorectal cancer (CRC) heterogeneity makes it challenging to identify and validate effective prognostic biomarkers for patient classification according to outcome and treatment response. Although a massive amount of proteomics data has been deposited in public data repositories, this rich source of information is vastly underused. Here, we attempted to reuse public proteomics datasets with two main objectives: i) to generate hypotheses (detection of biomarkers) for their posterior/downstream validation, and (ii) to validate, using an orthogonal approach, a previously described biomarker panel. Twelve CRC public proteomics datasets (mostly from the PRIDE database) were re-analysed and integrated to create a landscape of protein expression. Samples from both solid and liquid biopsies were included in the reanalysis. Integrating this data with survival annotation data, we have validated in silico a six-gene signature for CRC classification at the protein level, and identified five new blood-detectable biomarkers (CD14, PPIA, MRC2, PRDX1, and TXNDC5) associated with CRC prognosis. The prognostic value of these blood-derived proteins was confirmed using additional public datasets, supporting their potential clinical value. As a conclusion, this proof-of-the-concept study demonstrates the value of re-using public proteomics datasets as the basis to create a useful resource for biomarker discovery and validation. The protein expression data has been made available in the public resource Expression Atlas.

Construction and evaluation of a novel prognostic risk model of aging-related genes in bladder cancer

Background

Bladder cancer (BLCA) is the most common malignancy of the urinary system. Muscle-invasive bladder cancer (MIBC), which constitutes approximately 25% of all BLCA cases, is characterized by frequent recurrence and early onset of metastasis. Bladder cancer most commonly occurs in elderly patients and is significantly associated with aging. However, the prognostic value of age-related genes in BLCA, especially in MIBC, remains unclear.

Materials and methods

Training and testing sets were obtained from The Cancer Genome Atlas BLCA project. Differentially expressed genes between BLCA and normal samples intersected with human aging-related genes. Univariate Cox regression and least absolute shrinkage and selection operator regression analyses were used to identify prognostic aging-related signatures, followed by the construction of a risk score model and nomogram. Kaplan-Meier and receiver operating characteristic analyses were conducted to assess the predictive power. An independent BLCA cohort of 165 samples was included for external validation. The CIBERSORT algorithm was used to explore the characteristics of the immune microenvironment.

Results

Seven genes (IGF1, NGF, GCLM, PYCR1, EFEMP1, APOC3, and IFNB1) were identified by Cox and least absolute shrinkage and selection operator analyses. After combining the gene signature with the clinical parameters of patients with BLCA, a risk-prognosis model and nomogram were constructed and validated with the testing set. Bladder cancer cases with high 7-gene signature scores (high-risk group) and low scores (low-risk group) showed distinct prognoses. Furthermore, 7 types of immune cells were significantly altered between the low- and high-risk groups.

Conclusions

Collectively, our data provide a 7-gene signature that serves as a potential biomarker for BLCA, especially MIBC. Moreover, this 7-gene signature highlights the role of the tumor immune microenvironment in prognosis and thus might be related to the response to anti-programmed cell death protein 1-based immunotherapy.

Predictive biomarkers for colorectal cancer: a state-of-the-art systematic review

INTRODUCTION

Colorectal cancer (CRC) poses a substantial health burden, with early detection paramount for improved prognosis. This study aims to evaluate potential CRC biomarkers and detection techniques.

MATERIALS AND METHODS

This systematic review, reported in adherence to PRISMA Statement 2020 guidelines, collates the latest research on potential biomarkers and detection/prognosis methods for CRC, spanning the last decade.

RESULTS

Out of the 38 included studies, diverse biomarkers and detection methods emerged, with DNA methylation markers like SFRP2 and SDC2, microRNAs including miR-1290, miR-506, and miR-4316, and serum and plasma markers such as NTS levels and U2 snRNA fragments standing out. Methylated cfDNA and m5C methylation alteration in immune cells of the blood, along with circular RNA, showed promise as diagnostic markers. Meanwhile, techniques involving extracellular vesicles and lateral flow immunoassays exhibited potential for swift and effective CRC screening.

DISCUSSION

Our state-of-the-art review identifies potential biomarkers, including SFRP2, SDC2, miR-1290, miR-506, miR-4316, and U2 snRNA fragments, with significant potential in enhancing CRC detection. However, comprehensive validation studies and a rigorous evaluation of clinical utility and cost-effectiveness remain necessary before integration into routine clinical practice.

CONCLUSION

The findings emphasize the need for continued research into biomarkers and detection methods to improve patient outcomes.

ENO1 Binds to ApoC3 and Impairs the Proliferation of T Cells via IL-8/STAT3 Pathway in OSCC

Lymph node metastasis is associated with poor prognosis of oral squamous cell carcinoma (OSCC), and few studies have explored the relevance of postoperative lymphatic drainage (PLD) in metastatic OSCC. Alpha-enolase (ENO1) is a metabolic enzyme, which is related to lymphatic metastasis of OSCC. However, the role of ENO1 in PLD in metastatic OSCC has not been elucidated. Herein, we collected lymphatic drainage after lymphadenectomy between metastatic and non-metastatic lymph nodes in OSCC patients to investigate the relationship between ENO1 expression and metastasis, and to identify the proteins which interacted with ENO1 in PLD of patients with metastatic OSCC by MS/GST pulldown assay. Results revealed that the metabolic protein apolipoprotein C-III (ApoC3) was a novel partner of ENO1. The ENO1 bound to ApoC3 in OSCC cells and elicited the production of interleukin (IL)-8, as demonstrated through a cytokine antibody assay. We also studied the function of IL-8 on Jurkat T cells co-cultured with OSCC cells in vitro. Western blot analysis was applied to quantitate STAT3 (signal transducer and activator of transcription 3) and p-STAT3 levels. Mechanistically, OSCC cells activated the STAT3 signaling pathway on Jurkat T cells through IL-8 secretion, promoted apoptosis, and inhibited the proliferation of Jurkat T cells. Collectively, these findings illuminate the molecular mechanisms underlying the function of ENO1 in metastasis OSCC and provide new strategies for targeting ENO1 for OSCC treatment.

Proteomics in the pharmaceutical and biotechnology industry: a look to the next decade

INTRODUCTION

Pioneering technologies such as proteomics have helped fuel the biotechnology and pharmaceutical industry with the discovery of novel targets and an intricate understanding of the activity of therapeutics and their various activities in vitro and in vivo. The field of proteomics is undergoing an inflection point, where new sensitive technologies are allowing intricate biological pathways to be better understood, and novel biochemical tools are pivoting us into a new era of chemical proteomics and biomarker discovery. In this review, we describe these areas of innovation, and discuss where the fields are headed in terms of fueling biotechnological and pharmacological research and discuss current gaps in the proteomic technology landscape.

AREAS COVERED

Single cell sequencing and single molecule sequencing. Chemoproteomics. Biological matrices and clinical samples including biomarkers. Computational tools including instrument control software, data analysis.

EXPERT OPINION

Proteomics will likely remain a key technology in the coming decade, but will have to evolve with respect to type and granularity of data, cost and throughput of data generation as well as integration with other technologies to fulfill its promise in drug discovery.

An Aging-Related Gene Signature-Based Model for Risk Stratification and Prognosis Prediction in Lung Adenocarcinoma

Aging is an inevitable time-dependent process associated with a gradual decline in many physiological functions. Importantly, some studies have supported that aging may be involved in the development of lung adenocarcinoma (LUAD). However, no studies have described an aging-related gene (ARG)-based prognosis signature for LUAD. Accordingly, in this study, we analyzed ARG expression data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). After LASSO and Cox regression analyses, a six ARG-based signature (APOC3, EPOR, H2AFX, MXD1, PLCG2, and YWHAZ) was constructed using TCGA dataset that significantly stratified cases into high- and low-risk groups in terms of overall survival (OS). Cox regression analysis indicated that the ARG signature was an independent prognostic factor in LUAD. A nomogram based on the ARG signature and clinicopathological factors was developed in TCGA cohort and validated in the GEO dataset. Moreover, to visualize the prediction results, we established a web-based calculator yurong.shinyapps.io/ARGs_LUAD/. Calibration plots showed good consistency between the prediction of the nomogram and actual observations. Receiver operating characteristic curve and decision curve analyses indicated that the ARG nomogram had better OS prediction and clinical net benefit than the staging system. Taken together, these results established a genetic signature for LUAD based on ARGs, which may promote individualized treatment and provide promising novel molecular markers for immunotherapy.

Clinical Assay for the Early Detection of Colorectal Cancer Using Mass Spectrometric Wheat Germ Agglutinin Multiple Reaction Monitoring

Simple Summary

Colorectal cancer (CRC) is currently the third leading cause of cancer death worldwide. Early diagnosis of CRC is important for increasing the opportunity for treatment and receiving a good prognosis. The aim of our study was to develop a detection method that combined wheat germ agglutinin (WGA) chromatography with mass spectrometry (MS) for early detection of CRC. Further, machine learning algorithms and logistic regression were applied to combine multiple biomarkers we discovered. We validated in a population of 286 plasma samples the diagnostic performance of peptides corresponding to WGA-captured protein and its combination, which received a sensitivity of 84.5% and a specificity of 97.5% in the diagnoses of CRC. Proteomic biomarkers combined with algorithms can provide a powerful tool for discriminating patients with CRC and health controls (HCs). Measurements of WGA-captured PF4, ITIH4, and APOE with MS are then useful for early detection of CRC. Additionally, our study revealed the potential of applying lectin chromatography with MS for disease diagnosis.

Abstract

Colorectal cancer (CRC) is currently the third leading cause of cancer-related mortality in the world. U.S. Food and Drug Administration-approved circulating tumor markers, including carcinoembryonic antigen, carbohydrate antigen (CA) 19-9 and CA125 were used as prognostic biomarkers of CRC that attributed to low sensitivity in diagnosis of CRC. Therefore, our purpose is to develop a novel strategy for novel clinical biomarkers for early CRC diagnosis. We used mass spectrometry (MS) methods such as nanoLC-MS/MS, targeted LC-MS/MS, and stable isotope-labeled multiple reaction monitoring (MRM) MS coupled to test machine learning algorithms and logistic regression to analyze plasma samples from patients with early-stage CRC, late-stage CRC, and healthy controls (HCs). On the basis of our methods, 356 peptides were identified, 6 differential expressed peptides were verified, and finally three peptides corresponding wheat germ agglutinin (WGA)-captured proteins were semi-quantitated in 286 plasma samples (80 HCs and 206 CRCs). The novel peptide biomarkers combination of PF4^54–62, ITIH4^429–438, and APOE^198–207 achieved sensitivity 84.5%, specificity 97.5% and an AUC of 0.96 in CRC diagnosis. In conclusion, our study demonstrated that WGA-captured plasma PF4^54–62, ITIH4^429–438, and APOE^198–207 levels in combination may serve as highly effective early diagnostic biomarkers for patients with CRC.

Proteomic Analyses of Fibroblast- and Serum-Derived Exosomes Identify QSOX1 as a Marker for Non-invasive Detection of Colorectal Cancer

Simple Summary

Early diagnosis of colorectal cancer (CRC) is crucial to improve patient outcomes. The tumour microenvironment immediately adapts to malignant transformations, including the activation of fibroblasts in the connective tissue nearby. In this study, we investigated fibroblast activity-related protein secretion via extracellular vesicles (EVs). QSOX1, a protein identified to be significantly reduced in activated fibroblasts and derived EVs, was also found to be significantly reduced in circulating blood plasma EVs of CRC patients as compared to control patients. Hence, blood plasma EV-associated QSOX1 represents a promising platform for diagnostic CRC screening.

Abstract

The treatment of colorectal cancer (CRC) has improved during the last decades, but methods for crucial early diagnosis are yet to be developed. The influence of the tumour microenvironment on liquid biopsies for early cancer diagnostics are gaining growing interest, especially with emphasis on exosomes (EXO), a subgroup of extracellular vesicles (EVs). In this study, we established paired cancer-associated (CAFs) and normal fibroblasts (NF) from 13 CRC patients and investigated activation status-related protein abundance in derived EXOs. Immunohistochemical staining of matched patient tissue was performed and an independent test cohort of CRC patient plasma-derived EXOs was assessed by ELISA. A total of 11 differentially abundant EV proteins were identified between NFs and CAFs. In plasma EXOs, the CAF-EXO enriched protein EDIL3 was elevated, while the NF-EXO enriched protein QSOX1 was diminished compared to whole plasma. Both markers were significantly reduced in patient-matched CRC tissue compared to healthy colon tissue. In an independent test cohort, a significantly reduced protein abundance of QSOX1 was observed in plasma EXOs from CRC patients compared to controls and diagnostic ROC curve analysis revealed an AUC of 0.904. In conclusion, EXO-associated QSOX1 is a promising novel marker for early diagnosis and non-invasive risk stratification in CRC.

Proteomic Analyses of Fibroblast- and Serum-Derived Exosomes Identify QSOX1 as a Marker for Non-invasive Detection of Colorectal Cancer

The treatment of colorectal cancer (CRC) has improved during the last decades, but methods for crucial early diagnosis are yet to be developed. The influence of the tumour microenvironment on liquid biopsies for early cancer diagnostics are gaining growing interest, especially with emphasis on exosomes (EXO), a subgroup of extracellular vesicles (EVs). In this study, we established paired cancer-associated (CAFs) and normal fibroblasts (NF) from 13 CRC patients and investigated activation status-related protein abundance in derived EXOs. Immunohistochemical staining of matched patient tissue was performed and an independent test cohort of CRC patient plasma-derived EXOs was assessed by ELISA. A total of 11 differentially abundant EV proteins were identified between NFs and CAFs. In plasma EXOs, the CAF-EXO enriched protein EDIL3 was elevated, while the NF-EXO enriched protein QSOX1 was diminished compared to whole plasma. Both markers were significantly reduced in patient-matched CRC tissue compared to healthy colon tissue. In an independent test cohort, a significantly reduced protein abundance of QSOX1 was observed in plasma EXOs from CRC patients compared to controls and diagnostic ROC curve analysis revealed an AUC of 0.904. In conclusion, EXO-associated QSOX1 is a promising novel marker for early diagnosis and non-invasive risk stratification in CRC.