Proteomics-based Predictive Model for the Early Detection of Metastasis and Recurrence in Head and Neck Cancer

Plasma-based untargeted metabolomics reveals potential biomarkers for screening and distinguishing of ovarian tumors

Ovarian cancer (OC), a leading cause of gynecological cancer mortality, is frequently detected at advanced stages due to asymptomatic early progression. This study investigates plasma-based untargeted metabolomics for identifying biomarkers to screen and differentiate ovarian tumors (OT). Plasma samples from OC, benign ovarian tumors (BOT), and healthy controls (HC) were analyzed. Samples were randomized into train and test sets, with differential metabolites screened via two-tailed Student's t-test and partial least squares discriminant analysis. ROC models evaluated discriminatory capacity. Key metabolites demonstrated high predictive value: TMAO and hippuric acid distinguished OT from HC (AUC > 0.95), while linoleic acid, alpha-linolenic acid, and arachidonic acid (AUC > 0.9) further supported OT screening. Kynurenine differentiated OC from BOT (AUC = 0.808). Reduced levels of specific lysophosphatidylcholines (LPC (17:0/0:0), LPC (15:0/0:0)) also distinguished OT from HC (AUC = 0.771-0.89). These findings suggest plasma metabolomics holds promise for noninvasive biomarker discovery in OT screening and distinguishing between malignant and benign cases, though further validation of metabolite quantification is warranted prior to clinical application.

Exploring novel protein-based biomarkers for advancing breast cancer diagnosis: A review

This review provides a contemporary examination of the evolving landscape of breast cancer (BC) diagnosis, focusing on the pivotal role of novel protein-based biomarkers. The overview begins by elucidating the multifaceted nature of BC, exploring its prevalence, subtypes, and clinical complexities. A critical emphasis is placed on the transformative impact of proteomics, dissecting the proteome to unravel the molecular intricacies of BC. Navigating through various sources of samples crucial for biomarker investigations, the review underscores the significance of robust sample processing methods and their validation in ensuring reliable outcomes. The central theme of the review revolves around the identification and evaluation of novel protein-based biomarkers. Cutting-edge discoveries are summarised, shedding light on emerging biomarkers poised for clinical application. Nevertheless, the review candidly addresses the challenges inherent in biomarker discovery, including issues of standardisation, reproducibility, and the complex heterogeneity of BC. The future direction section envisions innovative strategies and technologies to overcome existing challenges. In conclusion, the review summarises the current state of BC biomarker research, offering insights into the intricacies of proteomic investigations. As precision medicine gains momentum, the integration of novel protein-based biomarkers emerges as a promising avenue for enhancing the accuracy and efficacy of BC diagnosis. This review serves as a compass for researchers and clinicians navigating the evolving landscape of BC biomarker discovery, guiding them toward transformative advancements in diagnostic precision and personalised patient care.

Catalyst-free late-stage functionalization to assemble α-acyloxyenamide electrophiles for selectively profiling conserved lysine residues

Covalent probes coupled with chemical proteomics represent a powerful method for investigating small molecule and protein interactions. However, the creation of a reactive warhead within various ligands to form covalent probes has been a major obstacle. Herein, we report a convenient and robust process to assemble a unique electrophile, an α-acyloxyenamide, through a one-step late-stage coupling reaction. This procedure demonstrates remarkable tolerance towards other functional groups and facilitates ligand-directed labeling in proteins of interest. The reactive group has been successfully incorporated into a clinical drug targeting the EGFR L858R mutant, erlotinib, and a pan-kinase inhibitor. The resulting probes have been shown to be able to covalently engage a lysine residue proximal to the ATP-binding pocket of the EGFR L858R mutant. A series of active sites, and Mg2+, ATP-binding sites of kinases, such as K33 of CDK1, CDK2, CDK5 were detected. This is the first report of engaging these conserved catalytic lysine residues in kinases with covalent inhibition. Further application of this methodology to natural products has demonstrated its success in profiling ligandable conserved lysine residues in whole proteome. These findings offer insights for the development of new targeted covalent inhibitors (TCIs).

Integrated Multi-Omics Signature Predicts Survival in Head and Neck Cancer

Head and Neck Cancer (HNC) is characterized by phenotypic, biological, and clinical heterogeneity. Despite treatment modalities, approximately half of all patients will die of the disease. Several molecular biomarkers have been investigated, but until now, without clinical translation. Here, we identified an integrative nine-gene multi-omics signature correlated with HNC patients’ survival independently of relapses or metastasis development. This prognosis multi-omic signature comprises genes mapped in the chromosomes 1q, 3p, 8q, 17q, 19p, and 19q and encompasses alterations at copy number, gene expression, and methylation. Copy number alterations in LMCD1-A1S and GRM7, the methylation status of CEACAM19, KRT17, and ST18, and the expression profile of RPL29, UBA7, FCGR2C, and RPSAP58 can predict the HNC patients’ survival. The difference higher than two years observed in the survival of HNC patients that harbor this nine-gene multi-omics signature can represent a significant step forward to improve patients’ management and guide new therapeutic targets development.

Inositol monophosphatase 1 (IMPA1) promotes triple-negative breast cancer progression through regulating mTOR pathway and EMT process

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer, which is characterized by high heterogeneity and metabolic dysregulation. Inositol monophosphatase 1(IMPA1) is critical for the metabolism of inositol, which has profound effects on gene expression and other biological processes. Here, we report for the first time that IMPA1 was upregulated in TNBC cell lines and tissues, and enhanced cell colony formation and proliferation in vitro and tumorigenicity in vivo. Additionally, IMPA1 promoted cell motility in vitro and metastatic lung colonization in vivo. Mechanistic investigations by transcriptome sequencing revealed that 4782 genes were differentially expressed between cells with IMPA1 knockdown and control cells. Among the differentially expressed genes after IMPA1 knockdown, five significantly altered genes were verified via qRT-PCR assays. Morerover, we found that the expression profile of those five targets as a gene set was significantly associated with IMPA1 status in TNBC cells. As this gene set was associated with mTOR pathway and epithelial-mesenchymal transition (EMT) process, we further confirmed that IMPA1 induced mTOR activity and EMT process, which at least in part contributed to IMPA1-induced TNBC progression. Collectively, our findings reveal a previously unrecognized role of IMPA1 in TNBC progression and identify IMPA1 as a potential target for TNBC therapy.

The Prediction of a 3-Protein-Based Model on the Prognosis of Head and Neck Squamous Cell Carcinoma

Background

Head and neck squamous cell carcinoma (HNSCC) is one of the commonest malignant tumors. Using high-throughput genomic methods, RNA-based diagnostic and prognostic models for HNSCC with potential clinical value have been developed. However, the clinical utility and reproducibility of these models are uncertain. Because the complex regulatory processes occurring after mRNA is transcribed, the abundance of proteins in a cell can never be fully predicted or explained by their corresponding mRNA expression. We aimed to assume and verify a novel protein signature for checking the HNSCC patients' prognosis.

Methods

The functional proteomic data of 332 HNSCC cases were collected from The Cancer Proteome Atlas (TCPA), and the related follow-up and clinical data were acquired from The Cancer Genome Atlas (TCGA). This study adopted multivariate and univariate Cox regression analysis, Akaike Information Criterion, receiver operating characteristic (ROC) analysis, and Kaplan-Meier method.

Results

Patients' clinical features in both sets were comparable (all, P > 0.05). The area under the ROC curve (AUC) for the 3-protein signature (X4EBP1_pT37T46, HER3_pY1289, and NF2) in the test set was 0.655 and in the combined cohort (all 332 patients combined) was 0.699. In addition, the 3-protein signature exhibited better predictive value for the survival of HNSCC patients as in comparison with conventional clinical factors like age, gender, tumor stage, and smoking history (TNM stage).

Conclusion

The 3-protein signature developed in this study exhibits good performance in predicting the overall survival of with HNSCC patients. The 3-protein signature exhibited better predictive value for survival than conventional clinical factors just like gender, TNM stage, smoking history, and age.

Applications of Multi-omics Approaches for Exploring the Molecular Mechanism of Ovarian Carcinogenesis

Ovarian cancer ranks as the fifth most common cause of cancer-related death in females. The molecular mechanisms of ovarian carcinogenesis need to be explored in order to identify effective clinical therapies for ovarian cancer. Recently, multi-omics approaches have been applied to determine the mechanisms of ovarian oncogenesis at genomics (DNA), transcriptomics (RNA), proteomics (proteins), and metabolomics (metabolites) levels. Multi-omics approaches can identify some diagnostic and prognostic biomarkers and therapeutic targets for ovarian cancer, and these molecular signatures are beneficial for clarifying the development and progression of ovarian cancer. Moreover, the discovery of molecular signatures and targeted therapy strategies could noticeably improve the prognosis of ovarian cancer patients.

Proteomics Analysis Identified ASNS as a Novel Biomarker for Predicting Recurrence of Skull Base Chordoma

Background

The prognostic factors of skull base chordoma associated with outcomes of patients after surgery remain inadequately identified. This study was designed to identify a novel prognostic factor for patients with skull base chordoma.

Method

Using a proteomic technique, the tumor biomarkers that were upregulated in the rapid-recurrence group of chordoma were screened and then narrowed down by bioinformatic analysis. Finally one potential biomarker was chosen for validation by immunohistochemistry using tissue microarray (TMA). A total of 187 patients included in TMA were randomly divided into two cohorts, the training cohort included 93 patients and the validation cohort included 94 patients. Kaplan-Meier survival analysis was used to assess the patients’ survival. Univariable and multivariable Cox regression analysis were used to identify prognostic factors predicting recurrence-free survival (RFS). CCK-8 assay, clonal formation assay and transwell assay were used to test the effect of asparagine synthetase (ASNS) on the proliferation, migration and invasion in chordoma cell lines.

Results

Among 146 upregulated proteins, ASNS was chosen as a potential prognostic biomarker after bioinformatics analysis. The H-scores of ASNS ranged from 106.27 to 239.58 in TMA. High expression of ASNS was correlated with shorter RFS in both the training cohort (p = 0.0093) and validation cohort (p < 0.001). Knockdown of ASNS by small interfering RNA (siRNA) inhibited the growth, colony formation, migration and invasion of chordoma cells in vitro.

Conclusion

This study indicates that high expression of ASNS is correlated with poor prognosis of patients with skull base chordoma. ASNS may be a useful prognostic factor for patients with skull base chordoma.

SPP1 derived from silica-exposed macrophage exosomes triggers fibroblast transdifferentiation

The occurrence and development of silicosis is related to the interaction of multiple cells through signal transmission caused by silica dust. Including inflammatory changes reduced by macrophages and phenotypic transdifferentiation reduced by lung fibroblasts. As a communication medium between cells, exosomes have become a hot research topic. To explore the role of exosomal proteins in the occurrence and development of silicosis and the possible intervention targets, this study conducted proteomic analysis of macrophage-derived exosomes induced by silica, to identify specific proteins for intervention. In this study, we used proteomic analysis to screen exosomal protein profiles from the RAW264.7 macrophages exposed to silica. A total of 291 proteins were differentially expressed, of which 178 were upregulated and 113 were downregulated. By performing functional annotation and analysis of the differentially expressed proteins, we identified proteins SPP1, HMGB3, and HNRNPAB, which were consistent with the proteomics analysis. The involvement of SPP1 protein in fibrosis was studied further. Knocking down the expression of SPP1 in exosomes resulted in a decrease in fibrosis-related indicators. These results help to understand that exosomal protein can mediate cell communication and play a key role in the transition from fibroblasts to myofibroblasts. Further, this study also provided strategies and scientific basis for future studies on the intervention of silicosis.

Proteomics analysis identified TPI1 as a novel biomarker for predicting recurrence of intrahepatic cholangiocarcinoma

BACKGROUND

Intrahepatic cholangiocarcinoma (ICC) is the second most common tumor in primary liver cancer, but the prognostic factors associated with long-term outcomes after surgical resection remain poorly defined. This study aimed to develop a novel prognostic classifier for patients with ICC after surgery.

METHODS

Using a proteomics approach, we screened tumor markers that up-regulated in ICC tissues, and narrowed down by bioinformatics analysis, western blot and immunohistochemistry. Prognostic markers were identified using Cox regression analyses in primary training cohort and the predictive models for time to recurrence (TTR) were established. The predictive accuracy of predictive model was validated in external validation cohort and prospective validation cohort. MTT assay, clonal formation assay and trans-well assays were used to verify the effect on the proliferation and migration in ICC cell line.

RESULTS

Triosephosphate isomerise (TPI1) was significantly up-regulated in ICC tissues and Kaplan-Meier analysis reveals that higher TPI1 expression was strongly correlated with higher recurrence rate of ICC patients. In the primary training cohort, mean TTR was significantly longer (p < 0.0001) than in the low-risk group (26.9 months for TTR, 95% CI 22.4-31.5) than in the high-risk group (14.5 months for TTR, 95% CI 10.6-18.4). Similar results were observed in two validation cohorts. In addition, a nomogram to predict recurrence was developed. Moreover, Knockdown of TPI1 by shRNA inhibited ICC cell growth, colony information, migration, invasion in vitro.

CONCLUSIONS

Current prognostic models were accurate in predicting recurrence for ICC patients after surgical resection.