Proteomic characterization identifies clinically relevant subgroups of soft tissue sarcoma.

Soft tissue sarcoma is a broad family of mesenchymal malignancies exhibiting remarkable histological diversity. We portray the proteomic landscape of 272 soft tissue sarcomas representing 12 major subtypes. Hierarchical classification finds the similarity of proteomic features between angiosarcoma and epithelial sarcoma, and elevated expression of SHC1 in AS and ES is correlated with poor prognosis.

Nomogram predicting overall survival after surgical resection for retroperitoneal leiomyosarcoma patients.

Background: Surgery is the best way to cure the retroperitoneal leiomyosarcoma (RLMS), and there is currently no prediction model on RLMS after surgical resection. The objective of this study was to develop a nomogram to predict the overall survival (OS) of patients with RLMS after surgical resection.

Methods: Patients who underwent surgical resection from September 2010 to December 2020 were included.

Proteomic characteristics reveal the signatures and the risks of T1 colorectal cancer metastasis to lymph nodes.

The presence of lymph node metastasis (LNM) affects treatment strategy decisions in T1NxM0 colorectal cancer (CRC), but the currently used clinicopathological-based risk stratification cannot predict LNM accurately. In this study, we detected proteins in formalin-fixed paraffin-embedded (FFPE) tumor samples from 143 LNM-negative and 78 LNM-positive patients with T1 CRC and revealed changes in molecular and biological pathways by label-free liquid chromatography tandem mass spectrometry (LC-MS/MS) and established classifiers for predicting LNM in T1 CRC. An effective 55-proteins prediction model was built by machine learning and validated in a training cohort (N=132) and two validation cohorts (VC1, N=42; VC2, N=47), achieved an impressive AUC of 1.

GwAAP: A genome-wide amino acid coding-decoding quantitative proteomics system.

Mass spectrometry-based proteomic technology has greatly improved and has been widely applied in various biological science fields. However, proteome-wide accurate quantification of proteins in signaling pathways remains challenging. Here, we report a genome-wide amino acid coding-decoding quantitative proteomic (GwAAP) system to facilitate precise proteome quantification.

Proteomic analysis reveals key differences between squamous cell carcinomas and adenocarcinomas across multiple tissues.

Squamous cell carcinoma (SCC) and adenocarcinoma (AC) are two main histological subtypes of solid cancer; however, SCCs are derived from different organs with similar morphologies, and it is challenging to distinguish the origin of metastatic SCCs. Here we report a deep proteomic analysis of 333 SCCs of 17 organs and 69 ACs of 7 organs. Proteomic comparison between SCCs and ACs identifies distinguishable pivotal pathways and molecules in those pathways play consistent adverse or opposite prognostic roles in ACs and SCCs.

Proteome-wide profiling of transcriptional machinery on accessible chromatin with biotinylated transposons.

To directly and quantitatively identify the transcriptional protein complexes assembled on accessible chromatin, we develop an assay for transposase-accessible chromatin using mass spectrum (ATAC-MS) based on direct transposition of biotinylated adaptors into open chromatin. Coupling with activated gene sequence information by ATAC-seq, ATAC-MS can profile the accessible chromatin-protein machinery. ATAC-MS, combined with fractionation strategies (fATAC-MS), can provide a high-resolution chromatin-transcriptional machinery atlas.