Low expression of CRISP3 predicts a favorable prognosis in patients with mammary carcinoma

CAP superfamily proteins in human: a new target for cancer therapy

The CAP (Cysteine-rich secretory protein, Antigen 5, and Pathogenesis-related protein 1) superfamily proteins (CAP proteins) are found in all kingdoms of life. The cysteine-rich secreted proteins are prevalent in human organs and tissues and serve as critical signaling molecules within cells, regulating a wide range of biochemical processes in the human body. Due to their involvement in numerous biological processes, CAP proteins have recently attracted significant attention, particularly in the context of tumorigenesis and cancer therapy. This review summarizes the expression patterns and roles of CAP proteins in various cancers. Additionally, it analyzes the mechanisms by which CAP proteins affect cancer cell proliferation and survival, regulate epithelial-mesenchymal transition, influence drug resistance, and regulate epigenetics. The review reveals that CAP proteins play distinct roles in various signaling pathways, such as the MAPK, PI3K-Akt, and p53 pathways, which are crucial for tumor progression. Furthermore, this review summarizes the tumor-inhibiting function of CAP proteins and their potential as cancer biomarkers. These findings suggest that CAP proteins represent a promising new target for innovative cancer diagnosis and treatment.

SpaGRA: graph augmentation facilitates domain identification for spatially resolved transcriptomics

Recent advances in spatially resolved transcriptomics (SRT) have provided new opportunities for characterizing spatial structures of various tissues. Graph-based geometric deep learning have gained widespread adoption for spatial domain identification tasks. Currently, most methods define adjacency relation between cells or spots by their spatial distance in SRT data, which overlooks key biological interactions like gene expression similarities, and leads to inaccuracies in spatial domain identification. To tackle this challenge, we propose a novel method, SpaGRA (https://github.com/sunxue-yy/SpaGRA), for automatic multi-relationship construction based on graph augmentation. SpaGRA uses spatial distance as prior knowledge and dynamically adjusts edge weights with multi-head graph attention networks (GATs). This helps SpaGRA to uncover diverse node relationships and enhance message passing in geometric contrastive learning. Additionally, SpaGRA uses these multi-view relationships to construct negative samples, addressing sampling bias posed by random selection. Experimental results show that SpaGRA demonstrates superior domain identification performance on multiple datasets generated from different protocols. Using SpaGRA, we analyzed the functional regions in the mouse hypothalamus, identified key genes related to heart development in mouse embryos, and observed cancer-associated fibroblasts enveloping cancer cells in the latest Visium HD data. Overall, SpaGRA can effectively characterize spatial structures across diverse SRT datasets.

Computational insights into CRISP3 downregulation in cervical cancer and its cervical lineages pattern

Background

Cysteine-rich secretory protein 3 (CRISP3) emerges as a potential biomarker in the study of many cancers, including cervical cancer (CC). This study aimed to analyze the expression pattern of CRISP3 in CC patients and CC cell lineages, following treatment with the epigenetic drugs: trichostatin A (TSA) and 5-aza-2'-deoxycytidine (5-aza).

Methods

The differentially expressed genes identified in GSE63514 were used to construct a protein-protein interaction network. CRISP3 was selected for subsequent analyses. We utilized data from the TCGA and GENT2 projects to evaluate the expression profile and clinical behavior of CRISP3. Additionally, we conducted cell culture experiments to analyze the expression profile of CRISP3 in cells.

Results

Low levels of CRISP3 were observed in squamous cell carcinoma (SCC) and human papillomavirus (HPV)16+, along with being associated with worse overall survival (OS). MIR-1229-3p was analyzed, and its high expression was associated with worse prognostic outcomes. In CC-derived cell lines, we observed low levels of CRISP3 in SiHa, followed by SW756, C33A, HeLa, and higher levels in CaSki. All cells were treated with TSA, 5-aza, or both. In all cell lines, treatment with TSA resulted in increased transcription of CRISP3.

Conclusion

We identified a significant downregulation of CRISP3 in CC, particularly in cases with HPV16 infection and SCC, which was associated with poorer OS. Preliminary findings suggest that epigenetic treatments with TSA and 5-aza may modulate CRISP3 expression, warranting further research to elucidate its regulatory mechanisms and potential as a prognostic biomarker.

EP300 promotes tumor stemness via epigenetic activation of CRISP3 leading to lobaplatin resistance in triple-negative breast cancer

Lobaplatin shows antitumor activity against a wide range of tumors, including triple-negative breast cancer (TNBC), and has been linked to cancer stem cell pool. Here, we investigated the molecular mechanisms behind lobaplatin resistance and stemness in vitro and in vivo. Two chemoresistance-related GEO data sets (GSE70690 and GSE103115) were included to screen out relevant genes. Cysteine-rich secretory protein 3 (CRISP3) was found to be overexpressed in lobaplatin-resistant TNBC and related to poor diagnosis. CRISP3 expression was significantly correlated with tumor stemness markers in lobaplatin-resistant cells. E1A-associated protein p300 (EP300) regulated CRISP3 expression by affecting the H3K27ac modification of the CRISP3 promoter. In addition, knocking down EP300 curbed the malignant biological behavior of lobaplatin-resistant cells, which was antagonized by CRISP3 overexpression. Collectively, our results highlight the EP300/CRISP3 axis as a key driver of lobaplatin resistance in TNBC and suggest that therapeutic targeting of this axis may be an effective strategy for enhancing platinum sensitivity in TNBC.

Machine Learning-Enhanced Extraction of Biomarkers for High-Grade Serous Ovarian Cancer from Proteomics Data

Comprehensive biomedical proteomic datasets are accumulating exponentially, warranting robust analytics to deconvolute them for identifying novel biological insights. Here, we report a strategic machine learning (ML)-based feature extraction workflow that was applied to unveil high-performing protein markers for high-grade serous ovarian carcinoma (HGSOC) from publicly available ovarian cancer tissue and serum proteomics datasets. Diagnosis of HGSOC, an aggressive form of ovarian cancer, currently relies on diagnostic methods based on tissue biopsy and/or non-specific biomarkers such as the cancer antigen 125 (CA125) and human epididymis protein 4 (HE4). Our newly developed ML-based approach enabled the identification of new serum proteomic biomarkers for HGSOC. The performance verification of these marker combinations using two independent cohorts affirmed their outperformance against known biomarkers for ovarian cancer including clinically used serum markers with >97% AUC. Our analysis also added novel biological insights such as enriched cancer-related processes associated with HGSOC.

Proteomic Profiling Identifies Candidate Diagnostic Biomarkers of Hydrosalpinx in Endometrial Fluid: A Pilot Study

Hydrosalpinx is a fluid occlusion and distension of the fallopian tubes, often resulting from pelvic inflammatory disease, which reduces the success of artificial reproductive technologies (ARTs) by 50%. Tubal factors account for approximately 25% of infertility cases, but their underlying molecular mechanisms and functional impact on other reproductive tissues remain poorly understood. This proteomic profiling study applied sequential window acquisition of all theoretical fragment ion spectra mass spectrometry (SWATH-MS) to study hydrosalpinx cyst fluid and pre- and post-salpingectomy endometrial fluid. Among the 967 proteins identified, we found 19 and 17 candidate biomarkers for hydrosalpinx in pre- and post-salpingectomy endometrial fluid, respectively. Salpingectomy significantly affected 76 endometrial proteins, providing insights into the enhanced immune response and inflammation present prior to intervention, and enhanced coagulation cascades and wound healing processes occurring one month after intervention. These findings confirmed that salpingectomy reverses the hydrosalpinx-related functional impairments in the endometrium and set a foundation for further biomarker validation and the development of less-invasive diagnostic strategies for hydrosalpinx.

Construction and analysis of a conjunctive diagnostic model of HNSCC with random forest and artificial neural network

Head and neck squamous cell carcinoma (HNSCC) is a heterogeneous tumor that is highly aggressive and ranks fifth among the most common cancers worldwide. Although, the researches that attempted to construct a diagnostic model were deficient in HNSCC. Currently, the gold standard for diagnosing head and neck tumors is pathology, but this requires a traumatic biopsy. There is still a lack of a noninvasive test for such a high-incidence tumor. In order to screen genetic markers and construct diagnostic model, the methods of random forest (RF) and artificial neural network (ANN) were utilized. The data of HNSCC gene expression was accessed from Gene Expression Omnibus (GEO) database; we selected three datasets totally, and we combined 2 datasets (GSE6631 and GSE55547) for screening differentially expressed genes (DEGs) and chose another dataset (GSE13399) for validation. Firstly, the 6 DEGs (CRISP3, SPINK5, KRT4, MMP1, MAL, SPP1) were screened by RF. Subsequently, ANN was applied to calculate the weights of 6 genes. Besides, we created a diagnostic model and nominated it as neuralHNSCC, and the performance of neuralHNSCC by area under curve (AUC) was verified using another dataset. Our model achieved an AUC of 0.998 in the training cohort, and 0.734 in the validation cohort. Furthermore, we used the Cell-type Identification using Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm to investigate the difference in immune cell infiltration between HNSCC and normal tissues initially. The selected 6 DEGs and the constructed novel diagnostic model of HNSCC would make contributions to the diagnosis.

A pan-cancer analysis reveals role of clusterin (CLU) in carcinogenesis and prognosis of human tumors

Clusterin (CLU) is a chaperone-like protein that has been demonstrated to have a direct relationship with cancer occurrence, progression, or metastasis. Clusterin was downregulated in tumor tissues using three datasets of tongue squamous carcinoma from the Gene Expression Omnibus. We further retrieved datasets from The Cancer Genome Atlas and Gene Expression Omnibus to thoroughly investigate the carcinogenic consequences of Clusterin. Our findings revealed that decreased Clusterin expression in malignancies was associated with a worse overall survival prognosis in individuals with multiple tumors; Clusterin gene deep deletions were found in almost all malignancies and were connected to most cancer patient's prognosis, Clusterin DNA methylation level was dependent on tumor type, Clusterin expression was also linked to the invasion of cancer-associated CD8+ T-cells and fibroblasts in numerous cancer forms. Moreover, pathway enrichment analysis revealed that Clusterin primarily regulates biological processes such as cholesterol metabolism, phospholipid binding, and protein-lipid complex formation. Overall, our pan-cancer research suggests that Clusterin expression levels are linked to tumor carcinogenesis and prognosis, which contributes to understanding the probable mechanism of Clusterin in tumorigenesis as well as its clinical prognostic significance.

LINC01342 silencing upregulates microRNA-508-5p to inhibit progression of lung cancer by reducing cysteine-rich secretory protein 3

Long noncoding RNAs (lncRNAs) are critical players during cancer progression. Nevertheless, the effect of most lncRNAs in lung cancer (LC) remains unclear. We aimed to explore the role of LINC01342 in LC development through the microRNA-508-5p (miR-508-5p)/cysteine-rich secretory protein 3 (CRISP3) axis. LINC01342, miR-508-5p, and CRISP3 expression in clinical samples and cell lines were determined, and their correlations in LC were analyzed. The prognostic role of LINC01342 in LC patients was evaluated. LC cells were screened and, respectively, transfected to alter the expression of LINC01342, miR-508-5p, and CRISP3. Then, proliferation, migration, invasion, and apoptosis of transfected LC cells were determined, and the in vivo tumor growth was observed as well. Binding relationships between LINC01342 and miR-508-5p, and between miR-508-5p and CRISP3 were identified. LINC01342 and CRISP3 were upregulated and miR-508-5p was downregulated in LC tissues and cells. High LINC01342 expression indicated a poor prognosis of LC patients. The LINC01342/CRISP3 silencing or miR-508-5p elevation inhibited proliferation, migration, and invasion of LC cells and promoted LC cell apoptosis, and also suppressed the in vivo tumor growth. LINC01342 bound to miR-508-5p and miR-508-5p targeted CRISP3. LINC01342 plays a prognostic role in LC and LINC01342 silencing upregulates miR-508-5p to inhibit the progression of LC by reducing CRISP3.

The integration of differentially expressed genes based on multiple microarray datasets for prediction of the prognosis in oral squamous cell carcinoma

Oral squamous cell carcinoma (OSCC) is a common human malignancy. However, its pathogenesis and prognostic information are poorly elucidated. In the present study, we aimed to probe the most significant differentially expressed genes (DEGs) and their prognostic performance in OSCC. Multiple microarray datasets from the Gene Expression Omnibus (GEO) database were aggregated to identify DEGs between OSCC tissue and control tissue. Least absolute shrinkage and selection operator (LASSO) Cox model was constructed to determine the prognostic performance of the aggregated DEGs based on The Cancer Genome Atlas (TCGA) OSCC cohort. Ten datasets with 341 OSCC samples and 283 control samples were included. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment revealed that the integrated DEGs were enriched in the IL-17 signaling pathway, viral protein interactions with cytokines and cytokine receptors, and amoebiasis, among others. Our LASSO Cox model was able to discriminate two groups with different overall survival in the training cohort and test cohort (p < 0.001). The time-dependent receiver operating characteristic (ROC) curve revealed that the area under the curve (AUC) values at one year, three years, and five years were 0.831, 0.898, and 0.887, respectively. In the testing cohort, the time-dependent ROC curve showed that the AUC values at one year, three years, and five years were 0.696, 0.693, and 0.860, respectively. Our study showed that the integrated DEGs of OSCC might be applicable in the evaluation of prognosis in OSCC. However, further research should be performed to validate our findings.

Serum proteins differentially expressed in early- and late-onset preeclampsia assessed using iTRAQ proteomics and bioinformatics analyses

BACKGROUND

Preeclampsia remains a serious disorder that puts at risk the lives of perinatal mothers and infants worldwide. This study assessed potential pathogenic mechanisms underlying preeclampsia by investigating differentially expressed proteins (DEPs) in the serum of patients with early-onset preeclampsia (EOPE) and late-onset preeclampsia (LOPE) compared with healthy pregnant women.

METHODS

Blood samples were collected from four women with EOPE, four women with LOPE, and eight women with normal pregnancies, with four women providing control samples for each preeclampsia group. Serum proteins were identified by isobaric tags for relative and absolute quantitation combined with liquid chromatography-tandem mass spectrometry. Serum proteins with differences in their levels compared with control groups of at least 1.2 fold-changes and that were also statistically significantly different between the groups at P < 0.05 were further analyzed. Bioinformatics analyses, including gene ontology and Kyoto Encyclopedia of Genes and Genomes signaling pathway analyses, were used to determine the key proteins and signaling pathways associated with the development of PE and to determine those DEPs that differed between women with EOPE and those with LOPE. Key protein identified by mass spectrometry was verified by enzyme linked immunosorbent assay (ELISA).

RESULTS

Compared with serum samples from healthy pregnant women, those from women with EOPE displayed 70 proteins that were differentially expressed with significance. Among them, 51 proteins were significantly upregulated and 19 proteins were significantly downregulated. In serum samples from women with LOPE, 24 DEPs were identified , with 10 proteins significantly upregulated and 14 proteins significantly downregulated compared with healthy pregnant women. Bioinformatics analyses indicated that DEPs in both the EOPE and LOPE groups were associated with abnormalities in the activation of the coagulation cascade and complement system as well as with lipid metabolism. In addition, 19 DEPs in the EOPE group were closely related to placental development or invasion of tumor cells. Downregulationof pregnancy-specific beta-1-glycoprotein 9 (PSG9) in the LOPE group was confirmed by ELISA.

CONCLUSION

The pathogenesis of EOPE and LOPE appeared to be associated with coagulation cascade activation, lipid metabolism, and complement activation. However, the pathogenesis of EOPE also involved processes associated with greater placental injury. This study provided several new proteins in the serum which may be valuable for clinical diagnosis of EOPE and LOPE, and offered potential mechanisms underpinning the development of these disorders.

Identification of a Profile of Neutrophil-Derived Granule Proteins in the Surface of Gold Nanoparticles after Their Interaction with Human Breast Cancer Sera

It is well known that the interaction of a nanomaterial with a biological fluid leads to the formation of a protein corona (PC) surrounding the nanomaterial. Using standard blood analyses, alterations in protein patterns are difficult to detect. PC acts as a “nano-concentrator” of serum proteins with affinity for nanoparticles’ surface. Consequently, characterization of PC could allow detection of otherwise undetectable changes in protein concentration at an early stage of a disease, such as breast cancer (BC). Here, we employed gold nanoparticles (AuNPs_diameter: 10.02 ± 0.91 nm) as an enrichment platform to analyze the human serum proteome of BC patients (n = 42) and healthy controls (n = 42). Importantly, the analysis of the PC formed around AuNPs after their interaction with serum samples of BC patients showed a profile of proteins that could differentiate breast cancer patients from healthy controls. These proteins developed a significant role in the immune and/or innate immune system, some of them being neutrophil-derived granule proteins. The analysis of the PC also revealed serum proteome alterations at the subtype level.