Evidence map›Paper›PMID 39507532›Full record

ArticleFrontiers in immunology2024

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

Chunsong Pang, Fang Xu, Yingwei Lin, WeiPing Han, Nianzhu Zhang, Lifen Zhao

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Article in Frontiers in immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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6 authors.

Chunsong Pang *Department of Laboratory Medicine, The Second Hospital of Dalian Medical University, Dalian, Liaoning, China.
Fang Xu *Department of Laboratory Medicine, The Second Hospital of Dalian Medical University, Dalian, Liaoning, China.
Yingwei Lin *Department of Laboratory Medicine, The Second Hospital of Dalian Medical University, Dalian, Liaoning, China.
WeiPing Han *Department of Laboratory Medicine, The Second Hospital of Dalian Medical University, Dalian, Liaoning, China.
Nianzhu ZhangDepartment of Laboratory Medicine, The Second Hospital of Dalian Medical University, Dalian, Liaoning, China.
Lifen ZhaoDepartment of Laboratory Medicine, The Second Hospital of Dalian Medical University, Dalian, Liaoning, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Biomarkers, TumorColorectal NeoplasmsLymphatic MetastasisProteomicsTandem Mass SpectrometryAdultAgedBlood ProteinsChromatography, LiquidFemaleHumansLiquid Chromatography-Mass SpectrometryLymph NodesMaleMiddle AgedBiomarkers, TumorBlood Proteinsbiomarkerscolorectal cancerlymph node metastasisplasmaproteomic

Identifiers

PMID39507532
PMCPMC11538601

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.