In-depth proteomics analysis of sentinel lymph nodes from individuals with endometrial cancer

Spatial proteomics: unveiling the multidimensional landscape of protein localization in human diseases

Spatial proteomics is a multidimensional technique that studies the spatial distribution and function of proteins within cells or tissues across both spatial and temporal dimensions. This field multidimensionally reveals the complex structure of the human proteome, including the characteristics of protein spatial distribution, dynamic protein translocation, and protein interaction networks. Recently, as a crucial method for studying protein spatial localization, spatial proteomics has been applied in the clinical investigation of various diseases. This review summarizes the fundamental concepts and characteristics of tissue-level spatial proteomics, its research progress in common human diseases such as cancer, neurological disorders, cardiovascular diseases, autoimmune diseases, and anticipates its future development trends. The aim is to highlight the significant impact of spatial proteomics on understanding disease pathogenesis, advancing diagnostic methods, and developing potential therapeutic targets in clinical research.

Proteomic Profile of Endometrial Cancer: A Scoping Review

Proteomics can be a robust tool in protein identification and regulation, allowing the discovery of potential biomarkers. In clinical practice, the management of endometrial cancer can be challenging. Thus, identifying promising markers could be beneficial, helping both in diagnosis and prognostic stratification, even predicting the response to therapy. Therefore, this manuscript systematically reviews the existing evidence of the proteomic profile of human endometrial cancer. The literature search was conducted via Medline (through PubMed) and the Web of Science. The inclusion criteria were clinical, in vitro, and in vivo original studies reporting proteomic analysis using all types of samples to map the human endometrial cancer proteome. A total of 55 publications were included in this review. Most of the articles carried out a proteomic analysis on endometrial tissue, serum and plasma samples, which enabled the identification of several potential diagnostic and prognostic biomarkers. In addition, eight articles were analyzed regarding the identified proteins, where three studies showed a strong correlation, sharing forty-five proteins. This analysis also allowed the identification of the 10 most frequently reported proteins in these studies: EGFR, PGRMC1, CSE1L, MYDGF, STMN1, CASP3 ANXA2, YBX1, ANXA1, and MYH11. Proteomics-based approaches pointed out potential diagnostic and prognostic candidates for endometrial cancer. However, there is a lack of studies exploring novel therapeutic targets.

Is fluorometric sentinel lymph node biopsy in endometrial cancer necessary?

Objective

In this study, we collected perioperative and postoperative follow-up data from patients with endometrial cancer (EC) at different stages to evaluate the role of sentinel lymph node biopsy (SLNB) in endometrial cancer surgery.

Methods

A total of 186 endometrial cancer patients undergoing radical hysterectomy from January 2018 to April 2022 were retrospectively analyzed. Patients were classified into four groups. Group A comprised patients diagnosed with stage IA grade 1 and 2 endometrioid EC who underwent SLNB. Group B comprised patients with stage IA grade 1 and 2 endometrioid EC who did not undergo SLNB. Group C comprised patients with higher-grade endometrioid EC, wherein systematic lymph node dissection was performed based on SLNB results. Group D comprised patients with higher-grade endometrioid EC who did not undergo SLNB and instead underwent direct systematic lymph node dissection. Clinical, pathological data, and follow-up information for all patients were collected.

Results

In Group A and B, SLNB was performed on 36 out of 67 patients with IA stage 1 and 2 endometrial cancer, yielding a SLN positivity rate of 5.6%. There were no significant differences observed between the two groups regarding perioperative outcomes and postoperative follow-up. Conversely, among 119 patients with higher-grade endometrial cancer, 52 underwent SLNB, with 20 patients exhibiting SLN positivity, resulting in a SLN positivity rate of 38.4%. However, the decision to undergo SLNB did not yield significant differences in perioperative outcomes and postoperative follow-up among these patients.

Conclusion

For stage IA grade 1 and 2 endometrioid EC, the incidence of lymph node positivity is low, omitting SLNB in this subpopulation is a feasible option. In other stages of endometrioid EC, there is no significant difference in perioperative and postoperative follow-up data between patients undergoing routine systematic lymphadenectomy and those undergoing systematic lymphadenectomy based on SLNB results. Therefore, if SLNB is not available, the standard procedure of PLND remains an option to obtain information about lymph node status, despite the surgical complications associated with this procedure.

Development of an Inflamed High Throughput Stem-cell-based Gut Epithelium Model to Assess the Impact of Annexin A1

OBJECTIVE AND DESIGN

Annexin A1 (ANXA1) plays a role in maintaining intestinal hemostasis, especially following mucosal inflammation. The published data about ANXA1 was derived from experimental animal models where there is an overlapping between epithelial and immune cells. There is no in vitro gut epithelial model that can assess the direct effect of ANXA1 on the gut epithelium.

METHODS

We developed high-throughput stem-cell-based murine epithelial cells and bacterial lipopolysaccharides (LPS) were used to induce inflammation. The impact of ANXA1 and its functional part (Ac2-26) was evaluated in the inflamed model. Intestinal integrity was assessed by the transepithelial electrical resistance (TEER), and FITC-Dextran permeability. Epithelial junction proteins were assessed using confocal microscopy and RT-qPCR. Inflammatory cytokines were evaluated by RT-qPCR and ELISA.

RESULTS

LPS challenge mediated a damage in the epithelial cells as shown by a drop in the TEER and an increase in FITC-dextran permeability; reduced the expression of epithelial junctional proteins (Occludin, ZO-1, and Cadherin) and increased the expression of the gut leaky protein, Claudin - 2. ANXA1 and Ac2-26 treatment reduced the previous damaging effects. In addition, ANXA1 and Ac2-26 inhibited the inflammatory responses mediated by the LPS and increased the transcription of the anti-inflammatory cytokine, IL-10.

CONCLUSION

ANXA1 and Ac2-26 directly protect the epithelial integrity by affecting the expression of epithelial junction and inflammatory markers. The inflamed gut model is a reliable tool to study intestinal inflammatory diseases, and to evaluate the efficacy of potential anti-inflammatory drugs and the screening of new drugs that could be candidates for inflammatory bowel disease.

Multi-omics profiling reveal cells with novel oncogenic cluster, TRAP1low/CAMSAP3low, emerge more aggressive behavior and poor-prognosis in early-stage endometrial cancer

The clinical heterogeneity of early-stage endometrial cancer (EC) is worthy of further study to identify high-quality prognostic markers and their potential role in aggressive tumor behavior. Mutation of TP53 was considered as an important primary triage in modified molecular typing for EC, it still cannot precisely predict the prognosis of EC. After proteomic analysis of cancer and para-cancerous tissues from 24 early-stage endometrioid EC patients with different survival outcomes, 13 differentially expressed proteins were screen out while 2 proteins enriched in p53 signaling pathway were further identified by single-cell transcriptome (scRNA-seq). Interestingly, tumor necrosis factor type-1 receptor-associated protein (TRAP1) and calmodulin-regulated spectrin-associated protein family member 3 (CAMSAP3) were found to be significantly downregulated in the specific cell cluster. Expectedly, the signature genes of TRAP1low/CAMSAP3low cluster included classical oncogenes. Moreover, close cellular interactions were observed between myeloid cells and the TRAP1low/CAMSAP3low cluster after systematically elucidating their relationship with tumor microenvironment (TME). The expression of TRAP1 and CAMSAP3 was verified by immunohistochemistry. Thus, a novel prediction model combining TRAP1, CAMSAP3 and TP53 was construct by multi-omics. Compared with the area under the curve, it demonstrated a significantly improvemrnt in the diagnostic efficacy in EC patients from TCGA bank. In conclusion, this work improved the current knowledge regarding the prognosis of early-stage EC through proteomics and scRNA-seq. These findings may lead to improvements in precise risk stratification of early-stage EC patients.

ALDH2 is a novel biomarker and exerts an inhibitory effect on melanoma

Melanoma is a malignant skin tumor. This study aimed to explore and assess the effect of novel biomarkers on the progression of melanoma. Differently expressed genes (DEGs) were screened from GSE3189 and GSE46517 datasets of Gene Expression Omnibus database using GEO2R. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses were conducted based on the identified DEGs. Hub genes were identified and assessed using protein-protein interaction networks, principal component analysis, and receiver operating characteristic curves. Quantitative real-time polymerase chain reaction was employed to measure the mRNA expression levels. TIMER revealed the association between aldehyde dehydrogenase 2 (ALDH2) and tumor immune microenvironment. The viability, proliferation, migration, and invasion were detected by cell counting kit-8, 5-ethynyl-2'-deoxyuridine, wound healing, and transwell assays. Total 241 common DEGs were screened out from GSE3189 and GSE46517 datasets. We determined 6 hub genes with high prediction values for melanoma, which could distinguish tumor samples from normal samples. ALDH2, ADH1B, ALDH3A2, DPT, EPHX2, and GATM were down-regulated in A375 and SK-MEL-2 cells, compared with the human normal melanin cell line (PIG1 cells). ALDH2 was selected as the candidate gene in this research, presenting a high diagnostic and predictive value for melanoma. ALDH2 had a positive correlation with the infiltrating levels of immune cells in melanoma microenvironment. Overexpression of ALDH2 inhibited cell viability, proliferation, migration, and invasion of A375/SK-MEL-2 cells. ALDH2 is a new gene biomarker of melanoma, which exerts an inhibitory effect on melanoma.

Expanding the coverage of regulons from high-confidence prior knowledge for accurate estimation of transcription factor activities

Gene regulation plays a critical role in the cellular processes that underlie human health and disease. The regulatory relationship between transcription factors (TFs), key regulators of gene expression, and their target genes, the so called TF regulons, can be coupled with computational algorithms to estimate the activity of TFs. However, to interpret these findings accurately, regulons of high reliability and coverage are needed. In this study, we present and evaluate a collection of regulons created using the CollecTRI meta-resource containing signed TF-gene interactions for 1186 TFs. In this context, we introduce a workflow to integrate information from multiple resources and assign the sign of regulation to TF-gene interactions that could be applied to other comprehensive knowledge bases. We find that the signed CollecTRI-derived regulons outperform other public collections of regulatory interactions in accurately inferring changes in TF activities in perturbation experiments. Furthermore, we showcase the value of the regulons by examining TF activity profiles in three different cancer types and exploring TF activities at the level of single-cells. Overall, the CollecTRI-derived TF regulons enable the accurate and comprehensive estimation of TF activities and thereby help to interpret transcriptomics data.

Fallopian tube lesions as potential precursors of early ovarian cancer: a comprehensive proteomic analysis

Ovarian cancer is the leading cause of death from gynecologic cancer worldwide. High-grade serous carcinoma (HGSC) is the most common and deadliest subtype of ovarian cancer. While the origin of ovarian tumors is still debated, it has been suggested that HGSC originates from cells in the fallopian tube epithelium (FTE), specifically the epithelial cells in the region of the tubal-peritoneal junction. Three main lesions, p53 signatures, STILs, and STICs, have been defined based on the immunohistochemistry (IHC) pattern of p53 and Ki67 markers and the architectural alterations of the cells, using the Sectioning and Extensively Examining the Fimbriated End Protocol. In this study, we performed an in-depth proteomic analysis of these pre-neoplastic epithelial lesions guided by mass spectrometry imaging and IHC. We evaluated specific markers related to each preneoplastic lesion. The study identified specific lesion markers, such as CAVIN1, Emilin2, and FBLN5. We also used SpiderMass technology to perform a lipidomic analysis and identified the specific presence of specific lipids signature including dietary Fatty acids precursors in lesions. Our study provides new insights into the molecular mechanisms underlying the progression of ovarian cancer and confirms the fimbria origin of HGSC.

Protocol to identify human subcellular alternative protein interactions using cross-linking mass spectrometry

Since the start of mass-spectrometry-based proteomics, proteins from non-referenced open reading frames or alternative proteins (AltProts) have been overlooked. Here, we present a protocol to identify human subcellular AltProt and decipher some interactions using cross-linking mass spectrometry. We describe steps for cell culture, in cellulo cross-link, subcellular extraction, and sequential digestion. We then detail both liquid chromatography-tandem mass spectrometry and cross-link data analyses. The implementation of a single workflow allows the non-targeted identification of signaling pathways involving AltProts. For complete details on the use and execution of this protocol, please refer to Garcia-del Rio et al.1.

In-depth quantitative proteomics analysis revealed C1GALT1 depletion in ECC-1 cells mimics an aggressive endometrial cancer phenotype observed in cancer patients with low C1GALT1 expression

BACKGROUND

Endometrial cancer (EC) is the most common cancer of the female reproductive organs. Despite the good overall prognosis of most low-grade ECs, FIGO I and FIGO II patients might experience tumor recurrence and worse prognosis. The study of alterations related to EC pathogenesis might help to get insights into underlying mechanisms involved in EC development and progression.

METHODS

Core tumoral samples were used to investigate the role of C1GALT1 in EC by immunohistochemistry (IHC). ECC-1 cells were used as endometrioid EC model to investigate the effect of C1GALT1 depletion using C1GALT1 specific shRNAs. SILAC quantitative proteomics analyses and cell-based assays, PCR, qPCR, WB, dot-blot and IHC analyses were used to identify, quantify and validate dysregulation of proteins.

RESULTS

Low C1GALT1 protein expression levels associate to a more aggressive phenotype of EC. Out of 5208 proteins identified and quantified by LC-MS/MS, 100 proteins showed dysregulation (log2fold-change ≥ 0.58 or ≤-0.58) in the cell protein extracts and 144 in the secretome of C1GALT1 depleted ECC-1 cells. Nine dysregulated proteins were validated. Bioinformatics analyses pointed out to an increase in pathways associated with an aggressive phenotype. This finding was corroborated by loss-of-function cell-based assays demonstrating higher proliferation, invasion, migration, colony formation and angiogenesis capacity in C1GALT1 depleted cells. These effects were associated to the overexpression of ANXA1, as demonstrated by ANXA1 transient silencing cell-based assays, and thus, correlating C1GALT and ANXA1 protein expression and biological effects. Finally, the negative protein expression correlation found by proteomics between C1GALT1 and LGALS3 was confirmed by IHC.

CONCLUSION

C1GALT1 stably depleted ECC-1 cells mimic an EC aggressive phenotype observed in patients and might be useful for the identification and validation of EC markers of progression.

Endometrial cancer diagnostic and prognostic algorithms based on proteomics, metabolomics, and clinical data: a systematic review

Endometrial cancer is the most common gynaecological malignancy in developed countries. Over 382,000 new cases were diagnosed worldwide in 2018, and its incidence and mortality are constantly rising due to longer life expectancy and life style factors including obesity. Two major improvements are needed in the management of patients with endometrial cancer, i.e., the development of non/minimally invasive tools for diagnostics and prognostics, which are currently missing. Diagnostic tools are needed to manage the increasing number of women at risk of developing the disease. Prognostic tools are necessary to stratify patients according to their risk of recurrence pre-preoperatively, to advise and plan the most appropriate treatment and avoid over/under-treatment. Biomarkers derived from proteomics and metabolomics, especially when derived from non/minimally-invasively collected body fluids, can serve to develop such prognostic and diagnostic tools, and the purpose of the present review is to explore the current research in this topic. We first provide a brief description of the technologies, the computational pipelines for data analyses and then we provide a systematic review of all published studies using proteomics and/or metabolomics for diagnostic and prognostic biomarker discovery in endometrial cancer. Finally, conclusions and recommendations for future studies are also given.

Mapping the Protein Kinome: Current Strategy and Future Direction

The kinome includes over 500 different protein kinases, which form an integrated kinase network that regulates cellular phosphorylation signals. The kinome plays a central role in almost every cellular process and has strong linkages with many diseases. Thus, the evaluation of the cellular kinome in the physiological environment is essential to understand biological processes, disease development, and to target therapy. Currently, a number of strategies for kinome analysis have been developed, which are based on monitoring the phosphorylation of kinases or substrates. They have enabled researchers to tackle increasingly complex biological problems and pathological processes, and have promoted the development of kinase inhibitors. Additionally, with the increasing interest in how kinases participate in biological processes at spatial scales, it has become urgent to develop tools to estimate spatial kinome activity. With multidisciplinary efforts, a growing number of novel approaches have the potential to be applied to spatial kinome analysis. In this paper, we review the widely used methods used for kinome analysis and the challenges encountered in their applications. Meanwhile, potential approaches that may be of benefit to spatial kinome study are explored.

Employing non-targeted interactomics approach and subcellular fractionation to increase our understanding of the ghost proteome

Eukaryotic mRNA has long been considered monocistronic, but nowadays, alternative proteins (AltProts) challenge this tenet. The alternative or ghost proteome has largely been neglected and the involvement of AltProts in biological processes. Here, we used subcellular fractionation to increase the information about AltProts and facilitate the detection of protein-protein interactions by the identification of crosslinked peptides. In total, 112 unique AltProts were identified, and we were able to identify 220 crosslinks without peptide enrichment. Among these, 16 crosslinks between AltProts and Referenced Proteins (RefProts) were identified. We further focused on specific examples such as the interaction between IP_2292176 (AltFAM227B) and HLA-B, in which this protein could be a potential new immunopeptide, and the interactions between HIST1H4F and several AltProts which can play a role in mRNA transcription. Thanks to the study of the interactome and the localization of AltProts, we can reveal more of the importance of the ghost proteome.

Annexin A protein family: Focusing on the occurrence, progression and treatment of cancer

The annexin A (ANXA) protein family is a well-known tissue-specific multigene family that encodes Ca²⁺ phospholipid-binding proteins. A considerable amount of literature is available on the abnormal expression of ANXA proteins in various malignant diseases, including cancer, atherosclerosis and diabetes. As critical regulatory molecules in cancer, ANXA proteins play an essential role in cancer progression, proliferation, invasion and metastasis. Recent studies about their structure, biological properties and functions in different types of cancers are briefly summarised in this review. We further discuss the use of ANXA as new class of targets in the clinical diagnosis and treatment of cancer.

Insights into the Relationship between Pentraxin-3 and Cancer

Although cancer can be cured if detected early and treated effectively, it is still a leading cause of death worldwide. Tumor development can be limited by an appropiate immune response, but it can be promoted by chronic extensive inflammation through metabolic dysregulation and angiogenesis. In the past decade, numerous efforts have been made in order to identify novel candidates with predictive values in cancer diagnostics. In line with this, researchers have investigated the involvement of pentraxin-3 (PTX-3) in cellular proliferation and immune escape in various types of cancers, although it has not been clearly elucidated. PTX-3 is a member of the long pentraxin subfamily which plays an important role in regulating inflammation, innate immunity response, angiogenesis, and tissue remodeling. Increased synthesis of inflammatory biomarkers and activation of different cellular mechanisms can induce PTX-3 expression in various types of cells (neutrophils, monocytes, lymphocytes, myeloid dendritic cells, fibroblasts, and epithelial cells). PTX-3 has both pro- and anti-tumor functions, thus dual functions in oncogenesis. This review elucidates the potential usefulness of PTX-3 as a serum biomarker in cancer. While future investigations are needed, PTX-3 is emerging as a promising tool for cancer's diagnosis and prognosis, and also treatment monitoring.

Diagnosis and Prediction of Endometrial Carcinoma Using Machine Learning and Artificial Neural Networks Based on Public Databases

Endometrial carcinoma (EC), a common female reproductive system malignant tumor, affects thousands of people with high morbidity and mortality worldwide. This study was aimed at developing a prediction model for the diagnosis of EC in the general population. First, we obtained datasets GSE63678, GSE106191, and GSE115810 from the Gene Expression Omnibus (GEO) database, dataset GSE17025 from the GEO database, and the RNA sequence of EC from The Cancer Genome Atlas (TCGA) database to constitute the training, test, and validation groups, respectively. Subsequently, the 96 most significantly differentially expressed genes (DEGs) were identified and analyzed for function and pathway enrichment in the training group. Next, we acquired the disease-specific genes by random forest and established an artificial neural network for the diagnosis. Receiver operating characteristic (ROC) curves were utilized to identify the signature across the three groups. Finally, immune infiltration was analyzed to reveal tumor-immune microenvironment (TIME) alterations in EC. The top 96 DEGs (77 down-regulated and 19 up-regulated genes) were primarily enriched in the interleukin-17 signaling pathway, protein digestion and absorption, and transcriptional misregulation in cancer. Subsequently, 14 characterizing genes of EC were identified by random forest. In the training, test, and validation groups, the artificial neural network was constructed with high diagnostic accuracies of 0.882, 0.864, and 0.839, respectively, and areas under the ROC curve (AUCs) of 0.928, 0.921, and 0.782, respectively. Finally, resting and activated mast cells were found to have increased in TIME. We constructed an artificial diagnostic model with excellent reliability for EC and uncovered variations in the immunological ecosystem of EC through integrated bioinformatics approaches, which might be potential diagnostic targets for EC.

CpG Site-Specific Methylation-Modulated Divergent Expression of PRSS3 Transcript Variants Facilitates Nongenetic Intratumor Heterogeneity in Human Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC) is one of the most lethal human tumors with extensive intratumor heterogeneity (ITH). Serine protease 3 (PRSS3) is an indispensable member of the trypsin family and has been implicated in the pathogenesis of several malignancies, including HCC. However, the paradoxical effects of PRSS3 on carcinogenesis due to an unclear molecular basis impede the utilization of its biomarker potential. We hereby explored the contribution of PRSS3 transcripts to tumor functional heterogeneity by systematically dissecting the expression of four known splice variants of PRSS3 (PRSS3-SVs, V1~V4) and their functional relevance to HCC.

Methods

The expression and DNA methylation of PRSS3 transcripts and their associated clinical relevance in HCC were analyzed using several publicly available datasets and validated using qPCR-based assays. Functional experiments were performed in gain- and loss-of-function cell models, in which PRSS3 transcript constructs were separately transfected after deleting PRSS3 expression by CRISPR/Cas9 editing.

Results

PRSS3 was aberrantly differentially expressed toward bipolarity from very low (PRSS3Low) to very high (PRSS3High) expression across HCC cell lines and tissues. This was attributable to the disruption of PRSS3-SVs, in which PRSS3-V2 and/or PRSS3-V1 were dominant transcripts leading to PRSS3 expression, whereas PRSS3-V3 and -V4 were rarely or minimally expressed. The expression of PRSS3-V2 or -V1 was inversely associated with site-specific CpG methylation at the PRSS3 promoter region that distinguished HCC cells and tissues phenotypically between hypermethylated low-expression (mPRSS3-SVLow) and hypomethylated high-expression (umPRSS3-SVHigh) groups. PRSS3-SVs displayed distinct functions from oncogenic PRSS3-V2 to tumor-suppressive PRSS3-V1, -V3 or PRSS3-V4 in HCC cells. Clinically, aberrant expression of PRSS3-SVs was translated into divergent relevance in patients with HCC, in which significant epigenetic downregulation of PRSS3-V2 was seen in early HCC and was associated with favorable patient outcome.

Conclusions

These results provide the first evidence for the transcriptional and functional characterization of PRSS3 transcripts in HCC. Aberrant expression of divergent PRSS3-SVs disrupted by site-specific CpG methylation may integrate the effects of oncogenic PRSS3-V2 and tumor-suppressive PRSS3-V1, resulting in the molecular diversity and functional plasticity of PRSS3 in HCC. Dysregulated expression of PRSS3-V2 by site-specific CpG methylation may have potential diagnostic value for patients with early HCC.

In Vitro, In Vivo, and In Silico Models of Lymphangiogenesis in Solid Malignancies

Lymphangiogenesis (LA) is the formation of new lymphatic vessels by lymphatic endothelial cells (LECs) sprouting from pre-existing lymphatic vessels. It is increasingly recognized as being involved in many diseases, such as in cancer and secondary lymphedema, which most often results from cancer treatments. For some cancers, excessive LA is associated with cancer progression and metastatic dissemination to the lymph nodes (LNs) through lymphatic vessels. The study of LA through in vitro, in vivo, and, more recently, in silico models is of paramount importance in providing novel insights and identifying the key molecular actors in the biological dysregulation of this process under pathological conditions. In this review, the different biological (in vitro and in vivo) models of LA, especially in a cancer context, are explained and discussed, highlighting their principal modeled features as well as their advantages and drawbacks. Imaging techniques of the lymphatics, complementary or even essential to in vivo models, are also clarified and allow the establishment of the link with computational approaches. In silico models are introduced, theoretically described, and illustrated with examples specific to the lymphatic system and the LA. Together, these models constitute a toolbox allowing the LA research to be brought to the next level.