Longitudinal plasma proteome profiling reveals the diversity of biomarkers for diagnosis and cetuximab therapy response of colorectal cancer

Integrative proteomic analysis reveals the potential diagnostic marker and drug target for the Type-2 diabetes mellitus

Objective

The escalating prevalence of Type-2 diabetes mellitus (T2DM) poses a significant global health challenge. Utilizing integrative proteomic analysis, this study aimed to identify a panel of potential protein markers for T2DM, enhancing diagnostic accuracy and paving the way for personalized treatment strategies.

Methods

Proteome profiles from two independent cohorts were integrated: cohort 1 composed of 10 T2DM patients and 10 healthy controls (HC), and cohort 2 comprising 87 T2DM patients and 60 healthy controls. Differential expression analysis, functional enrichment analysis, receiver operating characteristic (ROC) analysis, and classification error matrix analysis were employed.

Results

Comparative proteomic analysis identified the differential expressed proteins (DEPs) and changes in biological pathways associated with T2DM. Further combined analysis refined a group of protein panel (including CA1, S100A6, and DDT), which were significantly increased in T2DM in both two cohorts. ROC analysis revealed the area under curve (AUC) values of 0.94 for CA1, 0.87 for S100A6, and 0.97 for DDT; the combined model achieved an AUC reaching 1. Classification error matrix analysis demonstrated the combined model could reach an accuracy of 1 and 0.875 in the 60% training set and 40% testing set.

Conclusions

This study incorporates different cohorts of T2DM, and refines the potential markers for T2DM with high accuracy, offering more reliable markers for clinical translation.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40200-025-01562-3.

Cleavage of CAD by caspase-3 determines the cancer cell fate during chemotherapy

Metabolic heterogeneity resulting from the intra-tumoral heterogeneity mediates massive adverse outcomes of tumor therapy, including chemotherapeutic resistance, but the mechanisms inside remain largely unknown. Here, we find that the de novo pyrimidine synthesis pathway determines the chemosensitivity. Chemotherapeutic drugs promote the degradation of cytosolic Carbamoyl-phosphate synthetase II, Aspartate transcarbamylase, and Dihydroorotase (CAD), an enzyme that is rate-limiting for pyrimidine synthesis, leading to apoptosis. We also find that CAD needs to be cleaved by caspase-3 on its Asp1371 residue, before its degradation. Overexpressing CAD or mutating Asp1371 to block caspase-3 cleavage confers chemoresistance in xenograft and Cldn18-ATK gastric cancer models. Importantly, mutations related to Asp1371 of CAD are found in tumor samples that failed neoadjuvant chemotherapy and pharmacological targeting of CAD-Asp1371 mutations using RMY-186 ameliorates chemotherapy efficacy. Our work reveals the vulnerability of de novo pyrimidine synthesis during chemotherapy, highlighting CAD as a promising therapeutic target and biomarker.

Data-Independent Acquisition-Based Quantitative Proteomics for Pairwise Comparison of Serum and Plasma

Human blood contains proteins secreted by various organs, but there is no consensus on whether serum or plasma is preferable for proteome studies. Mass spectrometry employing data-independent acquisition has emerged as a transformative methodology in proteomics, enabling reproducible large-scale quantification of proteomes during one LC-MS/MS analytical run and facilitating identification of potential markers and elucidation of biological processes. Here, we profiled the proteome data of ten paired plasma and serum samples in the initial sample set. Functional analysis revealed similarities and differences in biological functions and the preference for different organs between serum and plasma. Furthermore, comparative proteomic analysis highlighted the different proteomic characteristics. Plasma-overrepresented pathways were related to the phagosome and immune, while serum-overrepresented pathways were associated with amino acid metabolism, which were further validated by the follow-up sample set composed of eight paired plasma and serum samples. We have detected potential markers in plasma and serum for various cancers and explored their association with prognosis using data from the TCGA pan-cancer cohort and HPA database. Further assessment is required to validate the reproducibility of the quantification for these markers. Overall, this study highlights the commonality and specificity of plasma and serum at the molecular level, underscoring their respective utility in biological exploration and clinical applications.

Identifying candidate biomarkers for detecting bronchogenic carcinoma stages using metaheuristic algorithms based on information fusion theory

OBJECTIVE

Invasive lung cancer staging poses significant challenges, often requiring painful and costly biopsy procedures. This study aims to identify non-invasive biomarkers for detecting bronchogenic carcinoma and its various stages by analyzing gene expression data using bioinformatics and machine learning techniques. By leveraging these advanced computational methods, we seek to eliminate the need for surgical intervention in the diagnostic process.

METHODS

We utilized the TCGA-LUAD dataset, including gene expression data from healthy and cancerous samples. To identify robust biomarkers, we applied eight metaheuristic algorithms for feature selection, combined with four classification methods and two data fusion techniques to optimize performance.

RESULTS

Our approach achieved 100% accuracy in distinguishing healthy samples from cancerous ones, outperforming existing methods that reported 97% accuracy. Notably, while prior methods have struggled to separate bronchogenic carcinoma stages effectively, our research achieved an approximate accuracy of 77% in stage classification. Furthermore, using gene enrichment methods, we identified 5, 7, and 16 diagnostic biomarker candidates for stages I, II, III, and IV, respectively.

CONCLUSION

This study demonstrates that integrating bioinformatics, gene set enrichment, and biological pathway analysis can enable non-invasive diagnostics for bronchogenic carcinoma stages. These findings hold promise for developing alternatives to traditional, invasive staging systems, potentially improving patient outcomes and reducing healthcare costs.

IMPDH2's Central Role in Cellular Growth and Diseases: A Potential Therapeutic Target

IMPDH2 is a rate-limiting enzyme in guanine nucleotide biosynthesis. It plays diverse roles in various physiological and pathological processes: nucleotide metabolism, inflammation, immune function, ribosomal stress. Structural or regulatory alterations in IMPDH2 are linked to significant health issues, and critical relevance in disease progression. We aim to underscore the potential of IMPDH2 as a promising therapeutic target for clinical applications.

In-Depth Proteomic Analysis of Tissue Interstitial Fluid Reveals Biomarker Candidates Related to Varying Differentiation Statuses in Gastric Adenocarcinoma

The proteomic heterogeneity of gastric adenocarcinoma (GC) has been extensively investigated at the bulk tissue level, which can only provide an average molecular state. In this study, we collected an in-depth quantitative proteomic dataset of tissues and interstitial fluids (ISFs) from both poorly and non-poorly differentiated GC and presented a comprehensive analysis from several perspectives. Comparison of proteomes between ISFs and tissues revealed that ISF exhibited higher abundances of proteins associated with blood microparticles, protein-lipid complexes, immunoglobulin complexes, and high-density lipoprotein particles. Also, consistent and inconsistent protein abundance changes between them were revealed by a correlation analysis. Interestingly, a more pronounced difference between tumors and normal adjacent tissues was found at the ISF level, which accurately reflected tissue properties compared to those of bulk tissue. Two ISF-derived biomarker candidates, calsyntenin-1 (CLSTN1) and prosaposin (PSAP), were identified by distinguishing patients with different differentiation statuses and were further validated in serum samples. Additionally, the silencing of CLSTN1 and PSAP was demonstrated to suppress cell proliferation, migration, and invasion in poorly differentiated gastric cancer cell lines. In summary, the ISF proteome offers a new perspective on tumor biology. This study provides a valuable resource that significantly enhances the understanding of GC and may ultimately benefit clinical practice.

HEXB Drives Raised Paucimannosylation in Colorectal Cancer and Stratifies Patient Risk

Noninvasive prognostic markers are needed to improve the survival of colorectal cancer (CRC) patients. Toward this goal, we applied untargeted systems glycobiology approaches to snap-frozen and formalin-fixed paraffin-embedded tumor tissues and peripheral blood mononuclear cells from CRC patients spanning different disease stages and matching controls to faithfully uncover molecular changes associated with CRC. Quantitative glycomics and immunohistochemistry revealed that noncanonical paucimannosidic N-glycans are elevated in CRC tumors relative to normal adjacent tissues. Cell origin-focused glycoproteomics enabled using the well-curated Human Protein Atlas combined with immunohistochemistry of CRC tumor tissues recapitulated these findings and indicated that the paucimannosidic proteins were in part from tumor-infiltrating monocytes (e.g., MPO, AZU1) and of CRC cell origin (e.g., LGALS3BP, PSAP). Biosynthetically explaining these observations, N-acetyl-β-D-hexosaminidase (Hex) subunit β (HEXB) was found to be overexpressed in CRC tissues relative to normal adjacent colorectal tissues and colocalization and enzyme inhibition studies confirmed that HEXB facilitates paucimannosidic protein biosynthesis in CRC cells. Employing a sensitive, quick, and robust enzyme activity assay, we then showed that Hex activity was elevated in plasma and peripheral blood mononuclear cells from patients with advanced CRC relative to controls and those with early-stage disease. Surveying a large donor cohort, the plasma Hex activity was found to be raised in CRC patients relative to normal controls and correlated with the 5-year survival of CRC patients indicating that elevated plasma Hex activity is a potential disease risk marker for patient outcome. Our glycoproteomics-driven findings open avenues for better prognostication and disease risk stratification in CRC.

An improved cancer diagnosis algorithm for protein mass spectrometry based on PCA and a one-dimensional neural network combining ResNet and SENet

Cancer is one of the most serious health problems worldwide. Because cancer has no specific symptoms in its early stages, it is often not diagnosed until it is in advanced stages, reducing the likelihood of successful treatment. Therefore, early diagnosis of cancer is a formidable challenge. Mass spectrometry-based proteomics offers a robust technical foundation for cancer diagnosis. However, mass spectrometry data are characterized by high dimensionality, large data volume, and noise interference, which can lead to diagnostic errors in clinical applications. To address this challenge, an improved algorithm combining principal component analysis (PCA) with a convolutional neural network (CNN) algorithm (denoted as PCA-1DSE-ResCNN) was proposed to assist in analyzing high-dimensional mass spectral data. The algorithm initially reduced the dimensionality of the data through the PCA technique. Subsequently, the convolutional neural network algorithm (1DSE-ResCNN) integrating residual blocks and squeeze-and-excitation blocks was used as a classifier. This approach can not only alleviate the issues of overfitting and gradient vanishing caused by deep network layers but also reduce redundant information, enabling the algorithm to effectively learn high-dimensional data features and deal with nonlinear relationships. To validate the effectiveness of the algorithm, the high-dimensional ovarian cancer mass spectrometry dataset was selected as an example to examine its application performance in early diagnosis of ovarian cancer. The experimental results demonstrated that the PCA-1DSE-ResCNN algorithm outperforms other methods in terms of accuracy, specificity, and sensitivity on three high-dimensional ovarian cancer datasets. This study will contribute to the rapid diagnosis and early detection of cancer.

STAVER: a standardized benchmark dataset-based algorithm for effective variation reduction in large-scale DIA-MS data

Mass spectrometry (MS)-based proteomics has become instrumental in comprehensively investigating complex biological systems. Data-independent acquisition (DIA)-MS, utilizing hybrid spectral library search strategies, allows for the simultaneous quantification of thousands of proteins, showing promise in enhancing protein identification and quantification precision. However, low-quality profiles can considerably undermine quantitative precision, resulting in inaccurate protein quantification. To tackle this challenge, we introduced STAVER, a novel algorithm that leverages standardized benchmark datasets to reduce non-biological variation in large-scale DIA-MS analyses. By eliminating unwanted noise in MS signals, STAVER significantly improved protein quantification precision, especially in hybrid spectral library searches. Moreover, we validated STAVER's robustness and applicability across multiple large-scale DIA datasets, demonstrating significantly enhanced precision and reproducibility of protein quantification. STAVER offers an innovative and effective approach for enhancing the quality of large-scale DIA proteomic data, facilitating cross-platform and cross-laboratory comparative analyses. This advancement significantly enhances the consistency and reliability of findings in clinical research. The complete package is available at https://github.com/Ran485/STAVER.

Plasma proteome profiling reveals dynamic of cholesterol marker after dual blocker therapy

Dual blocker therapy (DBT) has the enhanced antitumor benefits than the monotherapy. Yet, few effective biomarkers are developed to monitor the therapy response. Herein, we investigate the DBT longitudinal plasma proteome profiling including 113 longitudinal samples from 22 patients who received anti-PD1 and anti-CTLA4 DBT therapy. The results show the immune response and cholesterol metabolism are upregulated after the first DBT cycle. Notably, the cholesterol metabolism is activated in the disease non-progressive group (DNP) during the therapy. Correspondingly, the clinical indicator prealbumin (PA), free triiodothyronine (FT3) and triiodothyronine (T3) show significantly positive association with the cholesterol metabolism. Furthermore, by integrating proteome and radiology approach, we observe the high-density lipoprotein partial remodeling are activated in DNP group and identify a candidate biomarker APOC3 that can reflect DBT response. Above, we establish a machine learning model to predict the DBT response and the model performance is validated by an independent cohort with balanced accuracy is 0.96. Thus, the plasma proteome profiling strategy evaluates the alteration of cholesterol metabolism and identifies a panel of biomarkers in DBT.