A large-scale targeted proteomics of serum and tissue shows the utility of classifying high grade and low grade meningioma tumors

BACKGROUND

Meningiomas are the most prevalent primary brain tumors. Due to their increasing burden on healthcare, meningiomas have become a pivot of translational research globally. Despite many studies in the field of discovery proteomics, the identification of grade-specific markers for meningioma is still a paradox and requires thorough investigation. The potential of the reported markers in different studies needs further verification in large and independent sample cohorts to identify the best set of markers with a better clinical perspective.

METHODS

A total of 53 fresh frozen tumor tissue and 51 serum samples were acquired from meningioma patients respectively along with healthy controls, to validate the prospect of reported differentially expressed proteins and claimed markers of Meningioma mined from numerous manuscripts and knowledgebases. A small subset of Glioma/Glioblastoma samples were also included to investigate inter-tumor segregation. Furthermore, a simple Machine Learning (ML) based analysis was performed to evaluate the classification accuracy of the list of proteins.

RESULTS

A list of 15 proteins from tissue and 12 proteins from serum were found to be the best segregator using a feature selection-based machine learning strategy with an accuracy of around 80% in predicting low grade (WHO grade I) and high grade (WHO grade II and WHO grade III) meningiomas. In addition, the discriminant analysis could also unveil the complexity of meningioma grading from a segregation pattern, which leads to the understanding of transition phases between the grades.

CONCLUSIONS

The identified list of validated markers could play an instrumental role in the classification of meningioma as well as provide novel clinical perspectives in regard to prognosis and therapeutic targets.

Integrative Proteomic and Pharmacological Analysis of Colon Cancer Reveals the Classical Lipogenic Pathway with Prognostic and Therapeutic Opportunities

Despite recent advancements, the high mortality rate remains a concern in colon cancer (CAC). Identification of therapeutic markers could prove to be a great asset in CAC management. Multiple studies have reported hyperactivation of de novo lipogenesis (DNL), but its association with the pathology is unclear. This study aims to establish the importance as well as the prognostic and therapeutic potential of DNL in CAC. The key lipogenic enzymes fatty acid synthase along with ATP citrate lyase were quantified using an LC-MS/MS-based targeted proteomics approach in the samples along with the matched controls. The potential capacity of the proteins to distinguish between the tumor and controls was demonstrated using random forest-based class prediction analysis using the peptide intensities. Furthermore, in-depth proteomics of DNL inhibition in the CAC cell line revealed the significance of the pathway in proliferation and metastasis. DNL inhibition affected the major signaling pathways, including DNA repair, PI3K-AKT-mTOR pathway, membrane trafficking, proteasome, etc. The study revealed the upregulation of 26S proteasome machinery as a result of the treatment with subsequent induction of apoptosis. Again, in silico molecular docking-based drug repurposing was performed to find potential drug candidates. Furthermore, we have demonstrated that blocking DNL could be explored as a therapeutic option in CAC treatment.

Colon Adenocarcinoma Quantitative Proteomics Reveals Dysregulation in Key Cancer Signaling Pathways and a Candidate Protein Marker Panel

Colorectal cancer (CRC) is reportedly the second leading cause of cancer death worldwide. By the end of the decade, there will likely be more than one million fatalities worldwide from this cancer, with an estimated 2.2 million additional cases. We need new ways of thinking about cancer research. One approach is to deploy systems science using quantitative proteomics to obtain postgenomic and functional insights into cancer. The present study compares the tissue proteome of CRC (n = 10) with the matched peritumoral controls (n = 10) in samples obtained from the Indian subcontinent. When compared with the controls, a list of 22 substantially altered protein candidates was identified, which were associated with the growth, survival, and metastasis of the tumor. A list of the unique peptides from top significant proteins, including olfactomedin-4, alanyl aminopeptidase, and grancalcin was further validated using a parallel reaction monitoring-based targeted proteomics approach. In addition, biological pathway analysis showed perturbation in key biological processes, including dysregulation in purine metabolism, MYC targets in cancer, DNA repair, and replication, and leukocyte transendothelial migration, among others. The protein panel reported herein is also shown to be dysregulated in CRC and warrants further research toward understanding pathobiology, diagnostics, and therapeutics development in CRC.