Therapeutic potential of helminths in autoimmune diseases: helminth-derived immune-regulators and immune balance.

Helminths have accompanied human throughout history by releasing immune-evasion molecules that could counteract an aberrant immune response within the host. In the past decades, helminth infections are becoming less prevalent possibly due to the developed sanitation. Meanwhile, the incidence of autoimmune diseases is increasing, which cannot be exclusively explained by the changes of susceptibility genes. While the hygiene hypothesis casts light on the problem. The infections of helminths are believed to interact with and regulate human immunity with the byproduct of suppressing the autoimmune diseases. Thus, helminths are potential to treat or cure the autoimmune diseases. The therapeutic progresses and possible immune suppression mechanisms are illustrated in the review. The helminths that are studied most intensively include Heligmosomoides polygyrus, Hymenolepis diminuta, Schistosoma mansoni, Trichinella spiralis, and Trichuris suis. Special attentions are paid on the booming animal models and clinical trials that are to detect the efficiency of immune-modulating helminth-derived molecules on autoimmune diseases. These trials provide us with a prosperous clinical perspective, but the precise mechanism of the down-regulatory immune response remains to be clarified. More efforts are needed to be dedicated until these parasite-derived immune modulators could be used in clinic to treat or cure the autoimmune diseases under a standard management.

High-sensitivity hybrid MoSe2/AgInGaS quantum dot heterojunction photodetector.

Zero-dimensional (0D)-two-dimensional (2D) hybrid photodetectors have received widespread attention due to their outstanding photoelectric performances. However, these devices with high performances mainly employ quantum dots that contain toxic elements as sensitizing layers, which restricts their practical applications. In this work, we used eco-friendly AgInGaS quantum dots (AIGS-QDs) as a highly light-absorbing layer and molybdenum diselenide (MoSe2) as a charge transfer layer to construct a 0D-2D hybrid photodetector. Notably, we observed that MoSe2 strongly quenches the photoluminescence (PL) of AIGS-QDs and decreases the decay time of PL in the MoSe2/AIGS-QDs heterojunction. The MoSe2/AIGS-QDs hybrid photodetector demonstrates a responsivity of 14.3 A W-1 and a high detectivity of 6.4 × 1011 Jones. Moreover, the detectivity of the hybrid phototransistor is significantly enhanced by more than three times compared with that of the MoSe2 photodetector. Our work suggests that 0D-2D hybrid photodetectors with multiplex I-III-VI QDs provide promising potential for future high-sensitivity photodetectors.

Necroptosis-related regulatory pattern and scoring system for predicting therapeutic efficacy and prognosis in ovarian cancer.

BACKGROUND: Ovarian cancer is difficult to treat and is, therefore, associated with a high fatality rate. Although targeted therapy and immunotherapy have been successfully used clinically to improve the diagnosis and treatment of ovarian cancer, most tumors become drug resistant, and patients experience relapse, meaning that the overall survival rate remains low. AIMS: There is currently a lack of effective biomarkers for predicting the prognosis and/or outcomes of patients with ovarian cancer. Therefore, we used published transcriptomic data derived from a large ovarian cancer sample set to establish a molecular subtyping model of the core genes involved in necroptosis in ovarian cancer. METHODS AND RESULTS: Clustering analysis and differential gene expression analyses were performed to establish the genomic subtypes related to necroptosis and to explore the patterns of regulatory gene expression related to necroptosis in ovarian cancer. A necroptosis scoring system (NSS) was established using principal component analysis according to different regulatory patterns of necroptosis. In addition, this study revealed important biological processes with essential roles in the regulation of ovarian tumorigenesis, including external encapsulating structure organization, leukocyte migration, oxidative phosphorylation, and focal adhesion. Patients with high NSS scores had unique immunophenotypes, such as more abundant M2 macrophages, monocytes, CD4+ memory T cells, and regulatory T cells. Immune checkpoint CD274 had a greater expression in patients with high NSS values. CONCLUSION: This NSS could be used as an independent predictor of prognosis to determine the sensitivity of ovarian cancer to various small-molecule inhibitors, immune checkpoint inhibitors, and platinum-based chemotherapy drugs.

SLC11A2: a promising biomarker and therapeutic target in ovarian cancer.

Ovarian cancer has the highest mortality rate among gynecologic tumors, with a 5-year survival rate of less than 25%. There is an urgent need for early diagnosis and new drugs to reduce the disease burden of ovarian cancer. The aim of this study was to investigate the effectiveness of SLC11A2 as a therapeutic target and marker for ovarian cancer. Expression data of SLC11A2 were obtained from public databases. Then, the biological functions of SLC11A2 were validated in four ovarian cancer cell lines. Finally, we collected ovarian cancer clinical tissues, serum, and plasma exosomes and used immunohistochemistry, Elisa, and liquid chromatography-mass spectrometry (LC-MS) to validate the test efficacy of SLC11A2. The results showed that ovarian cancers with high SLC11A2 mRNA expression had shorter 5-year PFS and MST. Knockdown of SLC11A2 reduced ovarian cancer migration and increased cisplatin-induced apoptosis. Serum SLC11A2 may help improve the detection rate of ovarian cancer.

Dualistic classification of high grade serous ovarian carcinoma has its root in spatial heterogeneity.

INTRODUCTION: Widespread intra-peritoneal metastases is a main feature of high grade serous ovarian carcinoma (HGSOC). Recently, the extent of tumour heterogeneity was used to evaluate the cancer genomes among multi-regions in HGSOC. However, there is no consensus on the effect of tumour heterogeneity on the evolution of the tumour metastasis process in HGSOC. OBJECTIVES: We performed whole-exome sequencing in multiple regions of matched primary and metastatic HGSOC specimens to reveal the genetic mechanisms of ovarian tumourigenesis and malignant progression. METHODS: 63 samples (including ovarian carcinoma, omentum metastasis, and normal tissues) were used. We analyzed the genomic heterogeneity, traced the subclone dissemination and establishment history and compared the different genetic characters of cancer evolutionary models in HGSOC. RESULTS: We found that HGSOC had substantial intra-tumour heterogeneity (median 54.2, range 0 âˆ¼ 106.7), high inter-patient heterogeneity (P < 0.001), but relatively limited intra-patient heterogeneity (P = 0.949). Two COSMIC mutational signatures were identified in HGSOCs: signature 3 was related to homologous recombination, and signature 1 was associated with aging. Two scenarios were identified by phylogenetic reconstruction in our study: 3 cases (33.3 %) showed star topology, and the other 6 cases (66.7 %) displayed tree topology. Compared with star topology group, more driver events were identified in tree topology group (P < 0.001), and occurred more frequently in early stage than in late stage of clonal evolution (P < 0.001). Moreover, compared with the star topology group, the tree topology group showed higher rate of intra-tumour heterogeneity (P = 0.045). CONCLUSION: A dualistic classification model was proposed for the classification of HGSOC based on spatial heterogeneity, which may contribute to better managing patients and providing individual treatment for HGSOC patients.

A novel necroptosis-related lncRNA signature for predicting prognosis and anti-cancer treatment response in endometrial cancer.

Background: Necroptosis, a form of programmed cell death, underlies tumorigenesis and the progression of cancers. Anti-cancer strategies targeting necroptosis have increasingly been shown to present a potential cancer therapy. However, the predictive utility and anticancer sensitivity value of necroptosis-related lncRNAs (NRLs) for endometrial cancer (EC) are currently unknown. Methods: EC patient gene expression profiles and the corresponding clinical information collected from The Cancer Genome Atlas were used to identify NRLs that constituted a predictive signature for EC. The functional pathways, immune status, clinicopathological correlation, and anticancer drug sensitivity of the patients relative to the NRLs signatures were analyzed. Results: A signature composed of 7 NRLs (AC019080.5, BOLA3-AS1, AC022144.1, AP000345.2, LEF1-AS1, AC010503.4, and RPARP-AS1) was identified. The high-risk patient group with this signature exhibited a poorer prognosis and lower survival rate than low-risk group lacking this signature. This necroptosis-related lncRNA signature had a higher predictive accuracy compared with other clinicopathological variables (area under the receiver operating characteristic curve of the risk score: 0.717). Additionally, when patients were stratified based on other clinicopathological variables, the overall survival was significantly shorter in the high-risk versus low-risk group across all cohorts. Gene set enrichment analysis (GSEA) revealed that immune- and tumor-related signaling pathways and biological processes were enriched in the high-risk group compared to the low-risk group. Single-sample gene set enrichment analysis (ssGSEA) additionally showed that the resulting risk score was strongly correlated with EC patient immune status. Finally, patients with high-risk scores were more sensitive to the anti-cancer drugs such as Docetaxel, Mitomycin.C, Vinblastine, AZD.2281 (olaparib), AZD6244, and PD.0332991 (Palbociclib). Conclusion: These findings reveal a novel necroptosis-related lncRNA signature for predicting EC patient prognosis and shed new light on anticancer therapy strategies for EC.

Protein Panel of Serum-Derived Small Extracellular Vesicles for the Screening and Diagnosis of Epithelial Ovarian Cancer.

Although ovarian cancer, a gynecological malignancy, has the highest fatality rate, it still lacks highly specific biomarkers, and the differential diagnosis of ovarian masses remains difficult to determine for gynecologists. Our study aimed to obtain ovarian cancer-specific protein candidates from the circulating small extracellular vesicles (sEVs) and develop a protein panel for ovarian cancer screening and differential diagnosis of ovarian masses. In our study, sEVs derived from the serum of healthy controls and patients with cystadenoma and ovarian cancer were investigated to obtain a cancer-specific proteomic profile. In a discovery cohort, 1119 proteins were identified, and significant differences in the protein profiles of EVs were observed among groups. Then, 23 differentially expressed proteins were assessed using the parallel reaction monitoring in a validation cohort. Through univariate and multivariate logistic regression analyses, a novel model comprising three proteins (fibrinogen gamma gene (FGG), mucin 16 (MUC16), and apolipoprotein (APOA4)) was established to screen patients with ovarian cancer. This model exhibited an area under the receiver operating characteristic curve (AUC) of 0.936 (95% CI, 0.888-0.984) with 92.0% sensitivity and 82.9% specificity. Another panel comprising serum CA125, sEV-APOA4, and sEV-CD5L showed excellent performance (AUC 0.945 (95% CI, 0.890-1.000), sensitivity of 88.0%, specificity of 93.3%, and accuracy of 89.2%) to distinguish malignancy from benign ovarian masses. Altogether, our study provided a proteomic signature of circulating sEVs in ovarian cancer. The diagnostic proteomic panel may complement current clinical diagnostic measures for screening ovarian cancer in the general population and the differential diagnosis of ovarian masses.

Systematic identification of genes with a cancer-testis expression pattern in 19 cancer types.

Cancer-testis (CT) genes represent the similarity between the processes of spermatogenesis and tumorigenesis. It is possible that their selective expression pattern can help identify driver genes in cancer. In this study, we integrate transcriptomics data from multiple databases and systematically identify 876 new CT genes in 19 cancer types. We explore their relationship with testis-specific regulatory elements. We propose that extremely highly expressed CT genes (EECTGs) are potential drivers activated through epigenetic mechanisms. We find mutually exclusive associations between EECTGs and somatic mutations in mutated genes, such as PIK3CA in breast cancer. We also provide evidence that promoter demethylation and close non-coding RNAs (namely, CT-ncRNAs) may be two mechanisms to reactivate EECTG gene expression. We show that the meiosis-related EECTG (MEIOB) and its nearby CT-ncRNA have a role in tumorigenesis in lung adenocarcinoma. Our findings provide methods for identifying epigenetic-driver genes of cancer, which could serve as targets of future cancer therapies.