Screening of CXC chemokines in the microenvironment of ovarian cancer and the biological function of CXCL10

Identification of CXCL13 as a Promising Biomarker for Immune Checkpoint Blockade Therapy and PARP Inhibitor Therapy in Ovarian Cancer

Ovarian cancer has poor response rates to immune checkpoint blockade (ICB) therapy, despite the use of genomic sequencing to identify molecular targets. Homologous recombination deficiency (HRD) is a conventional indicator of genomic instability (GI) and has been used as a marker for targeted therapies. Indicators reflecting HRD status have shown potential in predicting the efficacy of ICB treatment. Public databases, including TCGA, ICGC, and GEO, were used to obtain data. HRD scores, neoantigen load, and TMB were obtained from the TCGA cohort. Candidate biomarkers were validated in multiple databases, such as the Imvigor210 immunotherapy cohort and the open-source single-cell sequencing database. Immunohistochemistry was performed to further validate the results in independent cohorts. CXCL10, CXCL11, and CXCL13 were found to be significantly upregulated in HRD tumors and exhibited prognostic value. A comprehensive analysis of the tumor immune microenvironment (TIME) revealed that CXCL13 expression positively correlated with neoantigen load and immune cell infiltration. In addition, single-cell sequencing data and clinical trial results supported the utility of CXCL13 as a biomarker for ICB therapy. Not only does CXCL13 serve as a biomarker reflecting HRD status, but it also introduces a potentially novel perspective on prognostic biomarkers for ICB in ovarian cancer.

Identification of necroptosis-related gene signatures for predicting the prognosis of ovarian cancer

Ovarian cancer (OC) is one of the most prevalent and fatal malignant tumors of the female reproductive system. Our research aimed to develop a prognostic model to assist inclinical treatment decision-making.Utilizing data from The Cancer Genome Atlas (TCGA) and copy number variation (CNV) data from the University of California Santa Cruz (UCSC) database, we conducted analyses of differentially expressed genes (DEGs), gene function, and tumor microenvironment (TME) scores in various clusters of OC samples.Next, we classified participants into low-risk and high-risk groups based on the median risk score, thereby dividing both the training group and the entire group accordingly. Overall survival (OS) was significantly reduced in the high-risk group, and two independent prognostic factors were identified: age and risk score. Additionally, three genes-C-X-C Motif Chemokine Ligand 10 (CXCL10), RELB, and Caspase-3 (CASP3)-emerged as potential candidates for an independent prognostic signature with acceptable prognostic value. In Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, pathways related to immune responses and inflammatory cell chemotaxis were identified. Cellular experiments further validated the reliability and precision of our findings. In conclusion, necroptosis-related genes play critical roles in tumor immunity, and our model introduces a novel strategy for predicting the prognosis of OC patients.

An integrated machine learning-based model for joint diagnosis of ovarian cancer with multiple test indicators

OBJECTIVE

To construct a machine learning diagnostic model integrating feature dimensionality reduction techniques and artificial neural network classifiers to develop the value of clinical routine blood indexes for the auxiliary diagnosis of ovarian cancer.

METHODS

Patients with ovarian cancer clearly diagnosed in our hospital were collected as a case group (n = 185), and three groups of patients with other malignant otolaryngology tumors (n = 138), patients with benign otolaryngology diseases (n = 339) and those with normal physical examination (n = 92) were used as an overall control group. In this paper, a fully automated segmentation network for magnetic resonance images of ovarian cancer is proposed to improve the reproducibility of tumor segmentation results while effectively reducing the burden on radiologists. A pre-trained Res Net50 is used to the three edge output modules are fused to obtain the final segmentation results. The segmentation results of the proposed network architecture are compared with the segmentation results of the U-net based network architecture and the effect of different loss functions and region of interest sizes on the segmentation performance of the proposed network is analyzed.

RESULTS

The average Dice similarity coefficient, average sensitivity, average specificity (specificity) and average hausdorff distance of the proposed network segmentation results reached 83.62%, 89.11%, 96.37% and 8.50, respectively, which were better than the U-net based segmentation method. For ROIs containing tumor tissue, the smaller the size, the better the segmentation effect. Several loss functions do not differ much. The area under the ROC curve of the machine learning diagnostic model reached 0.948, with a sensitivity of 91.9% and a specificity of 86.9%, and its diagnostic efficacy was significantly better than that of the traditional way of detecting CA125 alone. The model was able to accurately diagnose ovarian cancer of different disease stages and showed certain discriminative ability for ovarian cancer in all three control subgroups.

CONCLUSION

Using machine learning to integrate multiple conventional test indicators can effectively improve the diagnostic efficacy of ovarian cancer, which provides a new idea for the intelligent auxiliary diagnosis of ovarian cancer.

Pan-cancer analysis identifies EMC6 as a potential target for lung adenocarcinoma

Endoplasmic reticulum membrane protein complex subunit 6 (EMC6) plays an important function in both physiological and pathological states of cells. Nevertheless, there are few studies focused on the role of EMC6 in tumors. At first, we performed a series of bioinformatics analyses on 33 kinds of cancers, including differential expression analysis, tumor mutational burden analysis, prognostic analysis, and clinicopathological staging analysis. Then, we corroborated the important role of EMC6 in lung cancer by cytological and in vivo experiments. We found that the reduction of EMC6 expression did effectively inhibit the proliferation, invasion, and metastasis of A549. Finally, EMC6 is indeed involved in the regulation of ferroptosis, cuproptosis, and immune response in LUAD. In a word, our study not only comprehensively analyzed the functional mechanisms of EMC6 in all cancers but also validated the regulatory role of EMC6 in lung cancer for the first time.

Use of a ferroptosis-related gene signature to construct diagnostic and prognostic models for assessing immune infiltration in metabolic dysfunction-associated fatty liver disease

Introduction: Metabolic dysfunction-associated fatty liver disease (MAFLD), a serious health problem worldwide, can involve ferroptosis. This study aimed to comprehensively analyze the ferroptosis-related genes associated with MAFLD. Methods: Ferroptosis-related differentially expressed genes (FRDEGs) were identified in patients with MAFLD and healthy individuals. Gene ontology functional enrichment analysis, Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, and gene set enrichment analysis (GSEA) were used to analyze the relevant action pathways of the FRDEGs. The Encyclopedia of RNA Interactomes, CHIPBase, and comparative toxicogenomics databases were used to build mRNA-miRNA, mRNA-transcription factor (TF), and mRNA-drug interaction networks, respectively. A diagnostic model was constructed and bioinformatics analysis methods, such as least absolute shrinkage and selection operator regression analysis, Cox regression analysis, nomogram-based analysis, consensus clustering analysis, and single-sample GSEA, were used to systematically investigate the prognostic values and immunologic characteristics. Results: A total of 13 FRDEGs were obtained and eight were used to construct a diagnostic model and perform a prognostic analysis. Hub genes were also used to construct mRNA-miRNA and mRNA-TF interaction networks and potential drug or molecular compounds. Two MAFLD subtypes were identified: cluster2, which represents an "immunoactive" type, and cluster1, which represents an "immunosuppressive" type; a significant correlation was observed between the immune cell contents and the expression of three FRDEGs (NR4A1, FADS2, and SCD). Conclusion: A ferroptosis-related gene signature was constructed to diagnose MAFLD-associated steatohepatitis, predict the prognosis of MAFLD patients, and analyze the immunologic characteristics of MAFLD. Our findings may provide insights into developing innovative MAFLD treatment techniques.

Investigation of LGALS2 expression in the TCGA database reveals its clinical relevance in breast cancer immunotherapy and drug resistance

Breast cancer (BRCA) is known as the leading cause of death in women worldwide and has a poor prognosis. Traditional therapeutic strategies such as surgical resection, radiotherapy and chemotherapy can cause adverse reactions such as drug resistance. Immunotherapy, a new treatment approach with fewer side effects and stronger universality, can prolong the survival of BRCA patients and even achieve clinical cure. However, due to population heterogeneity and other reasons, there are still certain factors that limit the efficacy of immunotherapy. Therefore, the importance of finding new tumor immune biomarker cannot be emphasized enough. Studies have reported that LGALS2 was closely related to immunotherapy efficacy, however, it is unclear whether it can act as an immune checkpoint for BRCA immunotherapy. In the current study, changes in LGALS2 expression were analyzed in public datasets such as TCGA-BRCA. We found that LGALS2 expression was associated with immune infiltration, drug resistance and other characteristics of BRCA. Moreover, high LGALS2 expression was closely related to immunotherapy response, and was associated with methylation modifications and clinical resistance for the first time. These findings may help to elucidate the role of LGALS2 in BRCA for the development and clinical application of future immunotherapy strategies against BRCA.

Predicting the immune microenvironment and prognosis with a anoikis - related signature in breast cancer

Background

Tumor heterogeneity is widely recognized as a crucial factor impacting the prognosis of breast cancer (BC) patients. However, there remains an insufficient understanding of the underlying impact of anoikis on the prognosis of BC patients.

Methods

The researchers utilized the TCGA-BRCA dataset to screen and analyze the differentially expressed genes of anoikis-related genes (ARGs) in BC and normal breast tissue. Prognostic gene signatures were established through univariate, LASSO, and multivariate Cox regression analyses. These signatures were evaluated using Kaplan-Meier curve and receiver operating characteristic (ROC) analyses, resulting in the development of an anoikis-related index (ACI). The training dataset was TCGA-BRCA, while METABRIC and GSE96058 were used for external validation. Additionally, nomograms were developed by combining risk scores and clinical parameters, enabling gene set enrichment analysis (GSEA) and tumor immunoassay. Furthermore, an exploration of small molecule compounds was conducted to identify potential therapeutic benefits.

Results

A six-gene anoikis-related signature was constructed, which divided BC patients into high- and low-ACI groups based on median ACI scores. The ACI accurately predicted prognosis and acted as an independent prognostic factor for BC patients. Patients in the high-ACI group exhibited poorer overall survival (OS) across all cohorts and showed more severe clinical manifestations compared to the low-ACI group. The study also explored the potential impacts of anoikis on immune cells infiltrating tumors, immune checkpoints, growth factors, and cytokine levels. Additionally, the potential implications of anoikis in BC immunotherapy were discussed, along with highlighting small molecule compounds that could offer therapeutic benefits.

Conclusions

Anoikis was found to hold significant prognostic value in breast cancer, providing a novel approach for managing patients with different prognoses and implementing more precise immunotherapy strategies.

Development of a machine learning-based signature utilizing inflammatory response genes for predicting prognosis and immune microenvironment in ovarian cancer

Ovarian cancer (OC) represents a significant health challenge, characterized by a particularly unfavorable prognosis for affected women. Accumulating evidence supports the notion that inflammation-related factors impacting the normal ovarian epithelium may contribute to the development of OC. However, the precise role of inflammatory response-related genes (IRRGs) in OC remains largely unknown. To address this gap, we performed an integration of mRNA expression profiles from 7 cohorts and conducted univariate Cox regression analysis to screen 26 IRRGs. By utilizing these IRRGs, we categorized patients into subtypes exhibiting diverse inflammatory responses, with subtype B displaying the most prominent immune infiltration. Notably, the elevated abundance of Treg cells within subtype B contributed to immune suppression, resulting in an unfavorable prognosis for these patients. Furthermore, we validated the distribution ratios of stromal cells, inflammatory cells, and tumor cells using whole-slide digitized histological slides. We also elucidated differences in the activation of biological pathways among subtypes. In addition, machine learning algorithms were employed to predict the likelihood of survival in OC patients based on the expression of prognostic IRRGs. Through rigorous testing of over 100 combinations, we identified CXCL10 as a crucial IRRG. Single-cell analysis and vitro experiments further confirmed the potential secretion of CXCL10 by macrophages and its involvement in lymphangiogenesis within the tumor microenvironment. Overall, the study provides new insights into the role of IRRGs in OC and may have important implications for the development of novel therapeutic approaches.

Multi-Omics Analysis and Verification of the Oncogenic Value of CCT8 in Pan-Cancers

Background

Chaperonin-containing TCP1 subunit 8 (CCT8) has been proved to be involved in the occurrence and development of some cancers. However, no study has reported the potential role of CCT8 in a pan-cancer manner.

Methods

TIMER2.0, GEPIA2, UALCAN and Sangerbox were used to explore the expression, prognosis and methylation of CCT8. We used cBioPortal, TISIDB, SangerBox, TIMER2.0 and TISMO to investigate the genetic alteration of CCT8 and the relationship of CCT8 with molecular subtype, immune subtype, immune infiltration and immunotherapy response. CCT8-related genes were screened out through GEPIA and STRING for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. CCK-8, the colony formation assay, the wound healing assay and the Transwell assay were performed to explore the influence of CCT8 on proliferation and migration.

Results

CCT8 was highly expressed in most cancers with a poor prognosis. The expression level of CCT8, which was affected by the promoter region methylation and genetic alteration, was related to the molecular and immune subtype of cancers. Interestingly, CCT8 was positively associated with the activated CD4 T cells and type 2 T-helper cells. CCT8 played a vital role in the cell cycle and RNA transport of cancers, and it significantly inhibited the proliferation and migration of lung adenocarcinoma cells when it was knocked down.

Conclusion

CCT8 plays an indispensable role in promoting the proliferation and migration of many cancers. CCT8 might be a biomarker of T-helper type 2 (Th2) cell infiltration and a promising therapeutic target for T-helper type 1(Th1)/Th2 imbalance.

Neo-vascularization-based therapeutic perspectives in advanced ovarian cancer

The process of angiogenesis is well described for its potential role in the development of normal ovaries, and physiological functions as well as in the initiation, progression, and metastasis of ovarian cancer (OC). In advanced stages of OC, cancer cells spread outside the ovary to the pelvic, abdomen, lung, or multiple secondary sites. This seriously limits the efficacy of therapeutic options contributing to fatal clinical outcomes. Notably, a variety of angiogenic effectors are produced by the tumor cells to initiate angiogenic processes leading to the development of new blood vessels, which provide essential resources for tumor survival, dissemination, and dormant micro-metastasis of tumor cells. Multiple proangiogenic effectors and their signaling axis have been discovered and functionally characterized for potential clinical utility in OC. In this review, we have provided the current updates on classical and emerging proangiogenic effectors, their signaling axis, and the immune microenvironment contributing to the pathogenesis of OC. Moreover, we have comprehensively reviewed and discussed the significance of the preclinical strategies, drug repurposing, and clinical trials targeting the angiogenic processes that hold promising perspectives for the better management of patients with OC.

Targeting CXCL9/10/11-CXCR3 axis: an important component of tumor-promoting and antitumor immunity

Chemokines are chemotactic-competent molecules composed of a family of small cytokines, playing a key role in regulating tumor progression. The roles of chemokines in antitumor immune responses are of great interest. CXCL9, CXCL10, and CXCL11 are important members of chemokines. It has been widely investigated that these three chemokines can bind to their common receptor CXCR3 and regulate the differentiation, migration, and tumor infiltration of immune cells, directly or indirectly affecting tumor growth and metastasis. Here, we summarize the mechanism of how the CXCL9/10/11-CXCR3 axis affects the tumor microenvironment, and list the latest researches to find out how this axis predicts the prognosis of different cancers. In addition, immunotherapy improves the survival of tumor patients, but some patients show drug resistance. Studies have found that the regulation of CXCL9/10/11-CXCR3 on the tumor microenvironment is involved in the process of changing immunotherapy resistance. Here we also describe new approaches to restoring sensitivity to immune checkpoint inhibitors through the CXCL9/10/11-CXCR3 axis.

Involvement in Tumorigenesis and Clinical Significance of CXCL1 in Reproductive Cancers: Breast Cancer, Cervical Cancer, Endometrial Cancer, Ovarian Cancer and Prostate Cancer

C-X-C motif chemokine ligand 1 (CXCL1) is a member of the CXC chemokine subfamily and a ligand for CXCR2. Its main function in the immune system is the chemoattraction of neutrophils. However, there is a lack of comprehensive reviews summarizing the significance of CXCL1 in cancer processes. To fill this gap, this work describes the clinical significance and participation of CXCL1 in cancer processes in the most important reproductive cancers: breast cancer, cervical cancer, endometrial cancer, ovarian cancer, and prostate cancer. The focus is on both clinical aspects and the significance of CXCL1 in molecular cancer processes. We describe the association of CXCL1 with clinical features of tumors, including prognosis, ER, PR and HER2 status, and TNM stage. We present the molecular contribution of CXCL1 to chemoresistance and radioresistance in selected tumors and its influence on the proliferation, migration, and invasion of tumor cells. Additionally, we present the impact of CXCL1 on the microenvironment of reproductive cancers, including its effect on angiogenesis, recruitment, and function of cancer-associated cells (macrophages, neutrophils, MDSC, and T_reg). The article concludes by summarizing the significance of introducing drugs targeting CXCL1. This paper also discusses the significance of ACKR1/DARC in reproductive cancers.

Prognostic marker C3AR1 is associated with ovarian cancer cell proliferation and immunosuppression in the tumor microenvironment

BACKGROUND

C3AR1 was reported in driving tumor immunity in multiple cancers. However, its roles in ovarian cancer remain unclear. This study aims to determine role of C3AR1 in prognosis and regulating tumor infiltrating immune cells of ovarian cancer (OC).

MATERIALS AND METHODS

The expression, prognosis and clinical data related to C3AR1 were collected from public databases such as The Cancer Genome Atlas (TCGA), Human Protein Atlas (HPA) and Clinical Proteomics Tumor Analysis Alliance (CPTAC), and further analyze their relationship with immune infiltration. Immunohistochemistry verified the expression of C3AR1 in ovarian cancer and control tissues. C3AR1 was forced expressed in SKOV3 cells by plasmid transfection, and verified by qRT-PCR and Western blot. Cell proliferation were evaluated by EdU assay.

RESULTS

Bioinformatics analysis (TCGA, CPTAC) and immunohistochemical staining of clinical samples confirmed higher C3AR1 expression in ovarian cancer than that in normal tissues. High C3AR1 expression predicted adverse clinical outcomes. KEGG and GO analysis showed that the biological processes of C3AR1 in ovarian cancer are mainly involved in T cell activation, cytokine and chemokine activation. C3AR1 expression was positively correlated with chemokines and their receptors in the tumor microenvironment, such as CCR1(R = 0.83), IL10RA (R = 0.92), and INFG (R = 0.74). In addition, increased C3AR1 expression predicted more infiltration of tumor-associated macrophages, dendritic cell and CD8 + T cell. Some important m6A regulators, such as IGF2BP2, ALKBH5, IGFBP3 and METL14, are significantly positively or negatively correlated with C3AR1. Finally, overexpression of C3AR1 significantly increased proliferation of SKOV3 cells.

CONCLUSION

In summary, our study suggested that C3AR1 is associated with the prognosis and immune cell infiltration of ovarian cancer, and is a promising immunotherapeutic target.

A novel focal adhesion-related risk model predicts prognosis of bladder cancer —— a bioinformatic study based on TCGA and GEO database

Background

Bladder cancer (BLCA) is the ninth most common cancer globally, as well as the fourth most common cancer in men, with an incidence of 7%. However, few effective prognostic biomarkers or models of BLCA are available at present.

Methods

The prognostic genes of BLCA were screened from one cohort of The Cancer Genome Atlas (TCGA) database through univariate Cox regression analysis and functionally annotated by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The intersecting genes of the BLCA gene set and focal adhesion-related gene were obtained and subjected to the least absolute shrinkage and selection operator regression (LASSO) to construct a prognostic model. Gene set enrichment analysis (GSEA) of high- and low-risk patients was performed to explore further the biological process related to focal adhesion genes. Univariate and multivariate Cox analysis, receiver operating characteristic (ROC) curve analysis, and Kaplan–Meier survival analysis (KM) were used to evaluate the prognostic model. DNA methylation analysis was presented to explore the relationship between prognosis and gene methylation. Furthermore, immune cell infiltration was assessed by CIBERSORT, ESTIMATE, and TIMER. The model was verified in an external GSE32894 cohort of the Gene Expression Omnibus (GEO) database, and the Prognoscan database presented further validation of genes. The HPA database validated the related protein level, and functional experiments verified significant risk factors in the model.

Results

VCL, COL6A1, RAC3, PDGFD, JUN, LAMA2, and ITGB6 were used to construct a prognostic model in the TCGA-BLCA cohort and validated in the GSE32894 cohort. The 7-gene model successfully stratified the patients into both cohorts’ high- and low-risk groups. The higher risk score was associated with a worse prognosis.

Conclusions

The 7-gene prognostic model can classify BLCA patients into high- and low-risk groups based on the risk score and predict the overall survival, which may aid clinical decision-making.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-10264-5.

Systematic Analysis of CXC Chemokine–Vascular Endothelial Growth Factor A Network in Colonic Adenocarcinoma from the Perspective of Angiogenesis

Background

Tumor angiogenesis plays a vital role in tumorigenesis, proliferation, and metastasis. Recently, vascular endothelial growth factor A (VEGFA) and CXC chemokines have been shown to play vital roles in angiogenesis. Exploring the expression level, gene regulatory network, prognostic value, and target prediction of the CXC chemokine-VEGFA network in colon adenocarcinoma (COAD) is crucial from the perspective of tumor angiogenesis.

Methods

In this study, we analyzed gene expression and regulation, prognostic value, target prediction, and immune infiltrates related to the CXC chemokine-VEGFA network in patients with COAD using multiple databases (cBioPortal, UALCAN, Human Protein Atlas, GeneMANIA, GEPIA, TIMER (version 2.0), TRRUST (version 2), LinkedOmics, and Metascape).

Results

Our results showed that CXCL1/2/3/5/6/8/11/16/17 and VEGFA were markedly overexpressed, while CXCL12/13/14 were underexpressed in patients with COAD. Moreover, genetic alterations in the CXC chemokine-VEGFA network found at varying rates in patients with COAD were as follows: CXCL1/2/17 (2.1%), CXCL3/16 (2.6%), CXCL5/14 (2.4%), CXCL6 (3%), CXCL8 (0.8%), CXCL11/13 (1.9%), CXCL12 (0.6%), and VEGFA (1.3%). Promoter methylation of CXCL1/2/3/11/13/17 was considerably lower in patients with COAD, whereas methylation of CXCL5/6/12/14 and VEGFA was considerably higher. Furthermore, CXCL9/10/11 and VEGFA expression was notably correlated with the pathological stages of COAD. In addition, patients with COAD with high CXCL8/11/14 or low VEGFA expression levels survived longer than patients with dissimilar expression levels. CXC chemokines and VEGFA form a complex regulatory network through coexpression, colocalization, and genetic interactions. Moreover, many transcription factor targets of the CXC chemokine-VEGFA network in patients with COAD were identified: RELA, NFKB1, ZFP36, XBP1, HDAC2, SP1, ATF4, EP300, BRCA1, ESR1, HIF1A, EGR1, STAT3, and JUN. We further identified the top three miRNAs involved in regulating each CXC chemokine within the network: miR-518C, miR-369-3P, and miR-448 regulated CXCL1; miR-518C, miR-218, and miR-493 regulated CXCL2; miR-448, miR-369-3P, and miR-221 regulated CXCL3; miR-423 regulated CXCL13; miR-378, miR-381, and miR-210 regulated CXCL14; miR-369-3P, miR-382, and miR-208 regulated CXCL17; miR-486 and miR-199A regulated VEGFA. Furthermore, the CXC chemokine-VEGFA network in patients with COAD was notably associated with immune infiltration.

Conclusions

This study revealed that the CXC chemokine-VEGFA network might act as a prognostic biomarker for patients with COAD. Moreover, our study provides new therapeutic targets for COAD, serving as a reference for further research in the future.

CXCL13 and CXCR5 are upregulated in PCOS mice ovaries but downregulated following metformin administration

Polycystic ovary syndrome (PCOS) is becoming a common pathology among women, yet its pathogenesis remains enigmatic. The chemokine C-X-C motif ligand 13 (CXCL13) and its receptor type 5 (CXCR5) regulate inflammatory responses but their roles in PCOS remain unknown. Metformin is commonly administered to PCOS patients but its mechanism of action remains unclear. Thus, we aimed to determine the expression of CXCL13 and CXCR5 in the ovaries of PCOS mice and to evaluate the therapeutic effect of metformin on them. The study comprised four groups of mice: control, PCOS, PCOS plus metformin, and PCOS plus vehicle. CXCL13 and CXCR5 were found to be elevated in the ovarian tissues of the PCOS mice. Metformin reduced ovarian CXCL13 and CXCR5 expressions in the PCOS mice. Hence, CXCL13 and CXCR5 are potentially involved in PCOS pathogenesis; and metformin may help alleviate the symptoms of PCOS by inhibiting CXCL13 expression and actions.

TRPV1 is a potential biomarker for the prediction and treatment of multiple cancers based on a pan-cancer analysis

BACKGROUND

Transient receptor potential cation channel subfamily V member 1 (TRPV1) was considered to play pivotal roles in multiple cancers; however, the expression and clinical significance of the TRPV1 remain unclear, which were explored in this study.

RESULTS

The pan-cancer analysis was performed based on 10,236 samples in 32 cancers. Differential TRPV1 expression levels were detected in 12 cancers (p < 0.05). TRPV1 demonstrated its conspicuous prognosis significance and prediction effects for some cancers (e.g., lung adenocarcinoma), indicating its potential as a valuable and novel biomarker in treating and predicting cancers. TRPV1 expression was relevant to DNA methyltransferases, mismatch repair genes, tumor mutational burden, and microsatellite instability. TRPV1 expression was associated with the immune microenvironment of some cancers, and its roles in different cancers may be mediated by affecting various immune cells. Gene set enrichment analysis discloses the significant relevance of TRPV1 expression with a series of metabolic and immunoregulatory-related pathways.

CONCLUSIONS

This study provided a comprehensive workflow of the expression, clinical significance, and underlying mechanisms of TRPV1 in pan-cancer. TRPV1 may be an underlying biomarker for predicting and treating multiple cancer.

Differential roles of the Wip1-p38-p53 DNA damage response pathway in early/advanced-stage ovarian clear cell carcinomas

Background

Ovarian clear cell carcinoma (OCCC) is one of the most lethal types of ovarian cancer. Early-stage OCCC can be cured by surgery; however, advanced-stage disease shows poor prognosis due to chemoresistance unlike the more common high-grade serous carcinoma.

Methods

We explored the differential roles of the Wip1–p38–p53 DNA damage response pathway in respective early- or advanced-stage OCCC by immunohistochemistry of Wip1, phospho-p38, p53, and phospho-p53 from consecutive 143 patients.

Results

High Wip1 expression correlated with positive p53 (p=0.011), which in turn correlated with low nuclear phospho-p38 expression (p=0.0094). In the early stages, positive p53 showed trends toward worse overall survival (OS) (p=0.062), whereas in the advanced stages, high Wip1 correlated with worse OS (p=0.0012). The univariate and multivariate analyses of prognostic factors indicated that high Wip1 was significant and independent for worse OS (p=0.011) in the advanced stages, but not in the early stages. Additionally, high Wip1 showed trends toward shorter treatment-free interval (TFI) in the advanced stages, but not in the early stages (p=0.083 vs. 0.93). Furthermore, high Wip1 was significantly associated with positive p53 only in the patients with shorter TFI (<6 months), but not in those with longer TFI (≥6 months) (p=0.036 vs. 0.34).

Conclusions

Wip1 appears to play a crucial role for the prognosis of OCCC through chemoresistance specifically in the advanced stages, implicating that Wip1 possibly serves as a reasonable therapeutic target for improving chemoresistance and poor prognosis of advanced-stage OCCC.