Plasma proteomic profiling discovers molecular features associated with upper tract urothelial carcinoma

Integrated clinical and proteomic-based model for diagnostic and prognostic prediction in pRCC

Papillary renal cell carcinoma (pRCC), a main pathological subtype of non-clear cell RCC (nccRCC), has strong heterogeneity. Comparing to other nccRCC subtypes, advanced pRCC has the poorest prognosis. Due to its lower incidence compared to ccRCC, clinical research and exploration of non-invasive biomarkers for pRCC are limited, and it is often misclassified. Herein, we leveraged the advantages of non-invasive plasma samples and the extensive coverage of mass spectrometry (MS)-based proteomics to develop a series of predictive models. First, we established the RCC subtype diagnostic model, which accurately differentiates pRCC, ccRCC, chromophobe RCC (chRCC), and healthy controls, achieving robust performance with an area under the receiver operating characteristic curve (AUROC) of 0.96 and averaged precision (AP) score of 0.91. Furthermore, recognizing the pivotal role of TNM staging in pRCC clinical management, we developed the the TNM staging diagnostic model with AUROC was 0.92 as the complementary noninvasive strategy. Finally, to facilitate real-time clinical monitoring of progression-free survival (PFS), we integrated routine blood indicators and proteomic features to develop the time-clock progression model, which demonstrated high predictive performance (AUROC > 0.95, AP > 0.95). In summary, this study provides a comprehensive plasma proteomic analysis and establishes diagnostic and prognostic predictive models for pRCC.

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.

Proteomic profiling reveals biological processes and biomarkers involved in the pathogenesis of occult breast cancer

BACKGROUND

Occult breast cancer (OBC) is defined as axillary lymph node metastasis without any evidence of a primary tumor in the breast. Because of the limited number of clinical cases, the clinicopathological features and treatment recommendations of OBC are still controversial. In addition, its natural history is poorly understood and its proteomic signature remains unknown.

MATERIALS AND METHODS

We compared the clinicopathological features and prognosis of OBC patients and Non-OBC patients from the Surveillance, Epidemiology, and End Results (SEER) database and analyzed the effects of local treatment on the survival outcomes of OBC patients. Additionally, we performed a quantitative proteomic analysis for tissue samples of metastatic lymph nodes from OBC patients (OBC-LN), and paired tissue samples of metastatic lymph nodes (Non-OBC-LN) and primary tumors (Non-OBC-PT) from Non-OBC patients. We identified differentially expressed proteins in different comparable groups using Student's t test. Functional enrichment and protein-protein interaction network analyses were used to interpret the functions and interactions of the differentially expressed proteins in the comparison of OBC-LN vs Non-OBC-LN. Immunohistochemistry was used for the validation of the hub proteins.

RESULTS

Analysis of data from the SEER database demonstrated that OBC patients had a better prognosis than Non-OBC patients did and that either mastectomy or radiation therapy improved the outcomes of OBC patients. A total of 7208 comparable proteins were successfully quantified. Compared with those of the Non-OBC-LN samples, the OBC-LN protein profiles exhibited an active extracellular matrix and a thoroughly upregulated epithelial-mesenchymal transition phenotype. COL1A1, COL1A2, COL3A1, MMP2 and LUM were overexpressed in the OBC-LN samples and were identified as hub proteins. Immunohistochemical staining demonstrated that the five hub proteins were overexpressed in OBC-LN samples.

CONCLUSION

Our results provide insights for the clinical management of OBC and the proteomic signature of OBC offers molecular basis for further biological research.

Proteomic Analysis of Plasma Exosomes Enables the Identification of Lung Cancer in Patients With Chronic Obstructive Pulmonary Disease

Chronic obstructive pulmonary disease (COPD) is confirmed as an independent risk factor for the development of lung cancer. Although low-dose CT screening significantly reduces the mortality rate of lung cancer, the misdiagnosis and missed diagnosis rates remain high in the COPD population. Additionally, several COPD patients are unable to undergo invasive histological examinations. Therefore, there is an urgent need for minimally invasive biomarkers to screen or diagnose lung cancer in COPD patients. In this study, peripheral blood samples were collected from COPD patients with and without lung cancer. Plasma exosomes (EVs) were extracted for proteomic analysis. Sixteen differentially expressed proteins (DEPs) were preliminarily selected via label-free quantification (LFQ) proteomic technology and comprehensive bioinformatics analysis. Parallel reaction monitoring (PRM) targeted validation identified five candidate proteins associated with COPD with lung cancer. Compared to the COPD group, KRT1, KRT9, and KRT10 were significantly upregulated in the COPD with lung cancer group, while GPLD1 and TF were downregulated. The biomarkers identified in our study provide a foundation for non-invasive screening and diagnosis of lung cancer in COPD patients and exploration of the mechanisms shared between COPD and lung cancer.

Proteomic and metabolomic profiling of plasma uncovers immune responses in patients with Long COVID-19

Long COVID is an often-debilitating condition with severe, multisystem symptoms that can persist for weeks or months and increase the risk of various diseases. Currently, there is a lack of diagnostic tools for Long COVID in clinical practice. Therefore, this study utilizes plasma proteomics and metabolomics technologies to understand the molecular profile and pathophysiological mechanisms of Long COVID, providing clinical evidence for the development of potential biomarkers. This study included three age- and gender-matched cohorts: healthy controls (n = 18), COVID-19 recovered patients (n = 17), and Long COVID patients (n = 15). The proteomics results revealed significant differences in proteins between Long COVID-19 patients and COVID-19 recovered patients, with dysregulation mainly focused on pathways such as coagulation, platelets, complement cascade reactions, GPCR cell signal transduction, and substance transport, which can participate in regulating immune responses, inflammation, and tissue vascular repair. Metabolomics results showed that Long COVID patients and COVID-19 recovered patients have similar metabolic disorders, mainly involving dysregulation in lipid metabolites and fatty acid metabolism, such as glycerophospholipids, sphingolipid metabolism, and arachidonic acid metabolism processes. In summary, our study results indicate significant protein dysregulation and metabolic abnormalities in the plasma of Long COVID patients, leading to coagulation dysfunction, impaired energy metabolism, and chronic immune dysregulation, which are more pronounced than in COVID-19 recovered patients.

LC-MS/MS analysis reveals plasma protein signatures associated with lymph node metastasis in colorectal cancer

Objectives

Colorectal cancer (CRC) is a major global health concern, ranking as the third most common cancer and the fourth leading cause of cancer-related deaths worldwide. Currently, the diagnostic accuracy of Lymph node metastasis (LNM) is currently unsatisfactory. Therefore, there is an urgent need to develop a reliable tool that can accurately predict lymph node metastasis (LNM) in patients diagnosed with CRC.

Methods

We conducted an extensive proteomics investigation aimed at examining lymph node metastasis (LNM) in individuals diagnosed with colorectal cancer (CRC). In the discovery stage, employing a mass spectrometry-based proteomic approach, we analyzed a cohort of 60 colorectal cancer patients (NM=30, LNM=30), identifying distinct molecular profiles that differentiate patients with and without lymph node metastasis (LNM). Subsequently, we validated the protein classifier associated with lymph node metastasis.

Results

We elucidated a combinatorial predictive protein biomarker that can distinguish patients with and without lymph node metastasis by LC-MS/MS. The classifier achieved an area under the curve (AUC) of 0.892 (95% CI, 0.842-0.941), while in the testing cohort, it attained an AUC of 0.929 (95% CI, 0.824-1.000). Furthermore, the four protein markers demonstrated an AUC of 0.84 (95% CI, 0.783-0.890) in the validation cohort. Additionally, we categorized patients into three types based on immunophenotyping. Type 1 primarily consisted of patients with negative lymph node metastasis (NM), characterized by immune cells such as NK cells, CD4 T effector memory cells, and memory B cells. Type 2 mainly included patients with positive lymph node metastasis (LNM), characterized by immune cells such as mesangial cells, epithelial cells, and mononuclear cells. In Type 1, a prominent upregulation observed in immune inflammation, as well as in glucose and lipid metabolism. In Type 2, significant upregulation was evident in pathways such as pyrimidine metabolism and cell cycle regulation. The findings of this study suggest that immune mechanisms may exert a pivotal role in the process of lymph node metastasis in CRC.

Conclusions

Here, we present plasma protein signatures associated with lymph node metastasis in colorectal cancer (CRC). However, further validation across multiple centers is necessary to generalize these findings.

Clinical Implications of the Molecular and Genomic Landscape of Upper Tract Urothelial Carcinoma

PURPOSE OF REVIEW

Upper tract urothelial carcinoma (UTUC) is an aggressive entity with treatment strategies mirroring bladder cancer. Genomic and molecular profiling allows for a better characterization of this disease and allows for patient-tailored approaches. We aim to describe the genomic and molecular implications of this disease.

RECENT FINDINGS

Technological advances have the potential for early diagnosis and precise molecular analysis in patients with UTUC. Genomic profile clustering, specific mRNA signatures, and pathway-specific protein abundance tools have oncologic and clinical implications. We describe their utility in the context of this disease. In the era of precision medicine, designing clinical trials that explore the diagnostic and prognostic implications of biomolecular signatures in the context of UTUC is of utmost importance. Promising advances in this arena provide tools for physicians to avoid overtreatment in this patient population.

Proteomic Profiling of Serum Extracellular Vesicles Identifies Diagnostic Signatures and Therapeutic Targets in Breast Cancer

Analysis of extracellular vesicles (EV) is a promising noninvasive liquid biopsy approach for breast cancer detection, prognosis, and therapeutic monitoring. A comprehensive understanding of the characteristics and proteomic composition of breast cancer-specific EVs from human samples is required to realize the potential of this strategy. In this study, we applied a mass spectrometry-based, data-independent acquisition proteomic approach to characterize human serum EVs derived from patients with breast cancer (n = 126) and healthy donors (n = 70) in a discovery cohort and validated the findings in five independent cohorts. Examination of the EV proteomes enabled the construction of specific EV protein classifiers for diagnosing breast cancer and distinguishing patients with metastatic disease. Of note, TALDO1 was found to be an EV biomarker of distant metastasis of breast cancer. In vitro and in vivo analysis confirmed the role of TALDO1 in stimulating breast cancer invasion and metastasis. Finally, high-throughput molecular docking and virtual screening of a library consisting of 271,380 small molecules identified a potent TALDO1 allosteric inhibitor, AO-022, which could inhibit breast cancer migration in vitro and tumor progression in vivo. Together, this work elucidates the proteomic alterations in the serum EVs of breast cancer patients to guide the development of improved diagnosis, monitoring, and treatment strategies. Significance: Characterization of the proteomic composition of circulating extracellar vesicles in breast cancer patients identifies signatures for diagnosing primary and metastatic tumors and reveals tumor-promoting cargo that can be targeted to improve outcomes.

Plasma Proteomics Analysis of Early Biomarkers for Predicting Female Fecundability: A Nested Case-Control Study

The present study aimed to identify and verify new plasma protein markers to predict the female fecundability level. A nested case-control study was conducted involving couples who participated in the Chinese National Free Preconception Check-up Project. Women who successfully conceive within one year were defined as the high fecundability group, and those unable to conceive were defined as the low fecundability group. In the training cohort, potential protein biomarkers were identified using proteomics technology and were further tested in a validation cohort by the Western blotting assay, enzyme-linked immunosorbent assay, and biochemical tests. Meanwhile, receiver operating characteristic curve analysis were used to evaluate the predictive value. Cox proportional hazard regression analyses were conducted to calculate hazard ratios; restricted cubic spline analysis was used to assess the linear relationship between the the protein level and hazard ratios for fecundability. Pyruvate, a key product of glycolysis, was significantly increased in the high fecundability group (P < 0.01) compared to the low fecundability group, and its area under the curve value was 0.68 (P < 0.05). There was a linear positive dose-response association between the pyruvate level and fecundability possibility (hazard ratios = 1.66, 95% CI: 1.07-2.59, p for trend = 0.025, nonlinearity, p-value = 0.2927).

Investigating cellular similarities and differences between upper tract urothelial carcinoma and bladder urothelial carcinoma using single-cell sequencing

Background

Upper tract urothelial carcinoma (UTUC) and bladder urothelial carcinoma (BLCA) both originate from uroepithelial tissue, sharing remarkably similar clinical manifestations and therapeutic modalities. However, emerging evidence suggests that identical treatment regimens may lead to less favorable outcomes in UTUC compared to BLCA. Therefore, it is imperative to explore molecular processes of UTUC and identify biological differences between UTUC and BLCA.

Methods

In this study, we performed a comprehensive analysis using single-cell RNA sequencing (scRNA-seq) on three UTUC cases and four normal ureteral tissues. These data were combined with publicly available datasets from previous BLCA studies and RNA sequencing (RNA-seq) data for both cancer types. This pooled analysis allowed us to delineate the transcriptional differences among distinct cell subsets within the microenvironment, thus identifying critical factors contributing to UTUC progression and phenotypic differences between UTUC and BLCA.

Results

scRNA-seq analysis revealed seemingly similar but transcriptionally distinct cellular identities within the UTUC and BLCA ecosystems. Notably, we observed striking differences in acquired immunological landscapes and varied cellular functional phenotypes between these two cancers. In addition, we uncovered the immunomodulatory functions of vein endothelial cells (ECs) in UTUC, and intercellular network analysis demonstrated that fibroblasts play important roles in the microenvironment. Further intersection analysis showed that MARCKS promote UTUC progression, and immunohistochemistry (IHC) staining revealed that the diverse expression patterns of MARCKS in UTUC, BLCA and normal ureter tissues.

Conclusion

This study expands our multidimensional understanding of the similarities and distinctions between UTUC and BLCA. Our findings lay the foundation for further investigations to develop diagnostic and therapeutic targets for UTUC.

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.