Abstract
Head and neck paragangliomas (HNPGLs) are rare neuroendocrine tumors that pose significant challenges in both diagnosis and treatment. The pathogenic mechanism remains unclear, and there is no proteomic analysis-based molecular classification. Therefore, gaining a deeper understanding of this disease from the protein level is crucial because proteins play a fundamental role in the occurrence and development of tumors. We collected 44 tumor samples from patients diagnosed with HNPGL. The adrenal paraganglioma tissue (= 46) was used as the disease control group and the chorda tympani nerves (= 18) were used as the control group. High-pH reversed-phase liquid chromatography and liquid chromatography with tandem mass spectrometry analyses were used to build an integrated protein database of tumor samples. We then obtained two sets of differentially expressed proteins between the tumor group and the control group to identify the unique proteomic signatures of HNPGLs. Ingenuity pathway analysis annotations were used to perform the functional analysis. Subsequently, we developed a clinically relevant molecular classification for HNPGLs that connected the clinical characteristics with meaningful proteins and pathways to explain the varied clinical manifestations. We identified 6,640 proteins in the HNPGL group, and 314 differentially expressed proteins unique to HNPGL were discovered via inter-group comparison. We identified two HNPGL subgroups that significantly differed in clinical manifestation and proteomic characteristics. On the basis of the proteomic results, we proposed a pathogenic mechanism underlying HNPGL. We conducted a comprehensive analysis of the molecular mechanisms of HNPGL to build, for the first time, a clinically relevant molecular classification. By focusing on differential proteomic analyses between different types of paragangliomas, we were able to obtain a comprehensive description of the proteomic characteristics of HNPGL, which will be valuable for the search for significant biomarkers as a new treatment method for HNPGL.
Overview
- The study aims to understand the pathogenic mechanism of head and neck paragangliomas (HNPGLs) and develop a clinically relevant molecular classification. The study collected 44 tumor samples from patients diagnosed with HNPGL and used high-pH reversed-phase liquid chromatography and liquid chromatography with tandem mass spectrometry analyses to build an integrated protein database of tumor samples. Two sets of differentially expressed proteins were identified between the tumor group and the control group, and Ingenuity pathway analysis annotations were used to perform the functional analysis. The study identified 6,640 proteins in the HNPGL group and 314 differentially expressed proteins unique to HNPGL. The study identified two HNPGL subgroups that significantly differed in clinical manifestation and proteomic characteristics. The study proposed a pathogenic mechanism underlying HNPGL and built a clinically relevant molecular classification for HNPGL. The study's primary objective is to develop a clinically relevant molecular classification for HNPGL that connects the clinical characteristics with meaningful proteins and pathways to explain the varied clinical manifestations.
Comparative Analysis & Findings
- The study identified 314 differentially expressed proteins unique to HNPGL via inter-group comparison. Two HNPGL subgroups were identified that significantly differed in clinical manifestation and proteomic characteristics. The study proposed a pathogenic mechanism underlying HNPGL. The study's key findings include the identification of 314 differentially expressed proteins unique to HNPGL, the identification of two HNPGL subgroups that significantly differed in clinical manifestation and proteomic characteristics, and the proposed pathogenic mechanism underlying HNPGL.
Implications and Future Directions
- The study's findings provide a comprehensive description of the proteomic characteristics of HNPGL, which will be valuable for the search for significant biomarkers as a new treatment method for HNPGL. The study's molecular classification can be used to identify patients with specific clinical manifestations and guide personalized treatment. The study's findings can also be used to develop new therapeutic strategies targeting specific proteins or pathways. The study's limitations include the small sample size and the need for further validation of the identified proteins and pathways. Future research directions include expanding the sample size, validating the identified proteins and pathways, and exploring the role of specific proteins or pathways in the development and progression of HNPGL.