BMC Medical Genomics

The role of ITGA3 expression in predicting liver metastasis in patients with epithelial ovarian cancer

Integrin alpha-3 (ITGA3) has been implicated in tumor metastasis in various cancers, but its role in epithelial ovarian cancer (EOC)-associated liver metastasis (LM) remains unclear. This study aimed to investigate its role in LM in primary EOC patients. It was a retrospective study with a sample size of n = 235 receiving surgical resection at Puren Hospital Affiliated to Wuhan University of Science and Technology between January 2020 and December 2021, including 98 LM (LM group) and 137 non-LM cases (N-LM group). ITGA3 expression was assessed by immunohistochemistry. ROC curves were used for predictive performance analysis, Kaplan-Meier curves for survival analysis, and Cox regression analysis for identification of risk factors. Markedly elevated ITGA3 expression in tumor tissues was found in the LM group (P < 0.001), which demonstrated strong predictive value for LM in EOC patients (area under the curve (AUC) = 0.881, sensitivity = 70.41%, specificity = 87.59%, P < 0.001), and strongly correlated with tumor size and postoperative residual lesions (both P < 0.05). Compared with the L-ITGA3 group, the H-ITGA3 group had a higher incidence of postoperative LM (P < 0.001) and showed a left-shifted curve in Kaplan-Meier analysis (P < 0.001). ITGA3 expression in tumor tissues (HR = 5.977), tumor grade (HR = 1.441), and postoperative residual lesions (HR = 1.697) were identified as independent risk factors for postoperative LM. ITGA3 expression in tumor tissue significantly aids in predicting LM in EOC patients and is independently and closely related to adverse clinicopathological outcomes.

CTNNB1 p.D32A (c.95A &gt; C) somatic mutation in stage I grade 1 endometrioid endometrial carcinoma with lung metastasis: a case report

Abstract Background Most endometrial cancers are of low histological grade and uterine-confined, with a high 5-year survival rate. However, a small subset of women with low-grade and early-stage endometrioid endometrial cancer experience recurrence and death; thus, a more precise risk-stratification is needed. Case presentation A 29-year-old woman presented with abnormal vaginal bleeding and was diagnosed with FIGO grade 1 endometrioid endometrial carcinoma by curettage. Comprehensive cancer staging including pelvic and para-aortic lymphadenectomy was then performed. Postoperative pathological findings suggested an FIGO grade 1 endometrioid endometrial carcinoma infiltrating the superficial muscle layer. The patient did not receive adjuvant therapy. After 4 years of follow-up, the patient returned to our institution with lung metastasis. She underwent thoracoscopic resection of the affected lobes, followed by six cycles of combined chemotherapy of paclitaxel and carboplatin. Next-generation sequencing showed that the primary and lung metastatic tumors shared 4 mutations: PTEN (p.P248Lfs*8), CTNNB1 (p.D32A), BCOR (p.N1425S) and CBL (p.S439N). Immunohistochemistry revealed nuclear location of β–catenin in the primary and lung metastatic tumor samples, indicating abnormal activation of β–catenin. Conclusion CTNNB1p.D32A (c.95A &gt; C) mutation may be related to lung metastasis in this patient with low-grade early-stage endometrioid endometrial carcinoma.

Utility of a custom designed next generation DNA sequencing gene panel to molecularly classify endometrial cancers according to The Cancer Genome Atlas subgroups

Abstract Background The Cancer Genome Atlas identified four molecular subgroups of endometrial cancer with survival differences based on whole genome, transcriptomic, and proteomic characterization. Clinically accessible algorithms that reproduce this data are needed. Our aim was to determine if targeted sequencing alone allowed for molecular classification of endometrial cancer. Methods Using a custom-designed 156 gene panel, we analyzed 47 endometrial cancers and matching non-tumor tissue. Variants were annotated for pathogenicity and medical records were reviewed for the clinicopathologic variables. Using molecular characteristics, tumors were classified into four subgroups. Group 1 included patients with &gt; 570 unfiltered somatic variants, &gt; 9 cytosine to adenine nucleotide substitutions per sample, and &lt; 1 cytosine to guanine nucleotide substitution per sample. Group 2 included patients with any somatic mutation in MSH2, MSH6, MLH1, PMS2. Group 3 included patients with TP53 mutations without mutation in mismatch repair genes. Remaining patients were classified as group 4. Analyses were performed using SAS 9.4 (SAS Institute Inc., Cary, North Carolina, USA). Results Endometrioid endometrial cancers had more candidate variants of potential pathogenic interest (median 6 IQR 4.13 vs. 2 IQR 2.3; p &lt; 0.01) than uterine serous cancers. PTEN (82% vs. 15%, p &lt; 0.01) and PIK3CA (74% vs. 23%, p &lt; 0.01) mutations were more frequent in endometrioid than serous carcinomas. TP53 (18% vs. 77%, p &lt; 0.01) mutations were more frequent in serous carcinomas. Visual inspection of the number of unfiltered somatic variants per sample identified six grade 3 endometrioid samples with high tumor mutational burden, all of which demonstrated POLE mutations, most commonly P286R and V411L. Of the grade 3 endometrioid carcinomas, those with POLE mutations were less likely to have risk factors necessitating adjuvant treatment than those with low tumor mutational burden. Targeted sequencing was unable to assign samples to microsatellite unstable, copy number low, and copy number high subgroups. Conclusions Targeted sequencing can predict the presence of POLE mutations based on the tumor mutational burden. However, targeted sequencing alone is inadequate to classify endometrial cancers into molecular subgroups identified by The Cancer Genome Atlas.

Frequent POLE-driven hypermutation in ovarian endometrioid cancer revealed by mutational signatures in RNA sequencing

Abstract Background DNA polymerase epsilon (POLE) is encoded by the POLE gene, and POLE-driven tumors are characterized by high mutational rates. POLE-driven tumors are relatively common in endometrial and colorectal cancer, and their presence is increasingly recognized in ovarian cancer (OC) of endometrioid type. POLE-driven cases possess an abundance of TCT &gt; TAT and TCG &gt; TTG somatic mutations characterized by mutational signature 10 from the Catalog of Somatic Mutations in Cancer (COSMIC). By quantifying the contribution of COSMIC mutational signature 10 in RNA sequencing (RNA-seq) we set out to identify POLE-driven tumors in a set of unselected Mayo Clinic OC. Methods Mutational profiles were calculated using expressed single-nucleotide variants (eSNV) in the Mayo Clinic OC tumors (n = 195), The Cancer Genome Atlas (TCGA) OC tumors (n = 419), and the Genotype-Tissue Expression (GTEx) normal ovarian tissues (n = 84). Non-negative Matrix Factorization (NMF) of the mutational profiles inferred the contribution per sample of four distinct mutational signatures, one of which corresponds to COSMIC mutational signature 10. Results In the Mayo Clinic OC cohort we identified six tumors with a predicted contribution from COSMIC mutational signature 10 of over five mutations per megabase. These six cases harbored known POLE hotspot mutations (P286R, S297F, V411L, and A456P) and were of endometrioid histotype (P = 5e−04). These six tumors had an early onset (average age of patients at onset, 48.33 years) when compared to non-POLE endometrioid OC cohort (average age at onset, 60.13 years; P = .008). Samples from TCGA and GTEx had a low COSMIC signature 10 contribution (median 0.16 mutations per megabase; maximum 1.78 mutations per megabase) and carried no POLE hotspot mutations. Conclusions From the largest cohort of RNA-seq from endometrioid OC to date (n = 53), we identified six hypermutated samples likely driven by POLE (frequency, 11%). Our result suggests the clinical need to screen for POLE driver mutations in endometrioid OC, which can guide enrollment in immunotherapy clinical trials.

Next-generation sequencing analysis of endometrial screening liquid-based cytology specimens: a comparative study to tissue specimens

Abstract Background Liquid-based cytology (LBC) is now a widely used method for cytologic screening and cancer diagnosis. Since the cells are fixed with alcohol-based fixatives, and the specimens are stored in a liquid condition, LBC specimens are suitable for genetic analyses. Methods Here, we established a small cancer gene panel, including 60 genes and 17 microsatellite markers for next-generation sequencing, and applied to residual LBC specimens obtained by endometrial cancer screening to compare with corresponding formalin-fixed paraffin-embedded (FFPE) tissues. Results A total of 49 FFPE and LBC specimens (n = 24) were analyzed, revealing characteristic mutations for endometrial cancer, including PTEN, CTNNB1, PIK3CA, and PIK3R1 mutations. Eight cases had higher scores for both tumor mutation burden (TMB) and microsatellite instability (MSI), which agree with defective mismatch repair (MMR) protein expression. Paired endometrial LBC, and biopsied and/or resected FFPE tissues from 7 cases, presented almost identical mutations, TMB, and MSI profiles in all cases. Conclusion These findings demonstrate that our ad hoc cancer gene panel enabled the detection of therapeutically actionable gene mutations in endometrial LBC and FFPE specimens. Endometrial cancer LBC specimens offer an alternative and affordable source of molecular testing materials.

How does re-classification of variants of unknown significance (VUS) impact the management of patients at risk for hereditary breast cancer?

Abstract Background The popularity of multigene testing increases the probability of identifying variants of uncertain significance (VUS). While accurate variant interpretation enables clinicians to be better informed of the genetic risk of their patients, currently, there is a lack of consensus management guidelines for clinicians on VUS. Methods Among the BRCA1 and BRCA2 mutations screening in 3,544 subjects, 236 unique variants (BRCA1: 86; BRCA2: 150) identified in 459 patients were being reviewed. These variants consist of 231 VUS and 5 likely benign variants at the initial classification. Results The variants in 31.8% (146/459) patients were reclassified during the review, which involved 26 unique variants (11.0%). Also, 31 probands (6.8%) and their family members were offered high-risk surveillance and related management after these variants were reclassified to pathogenic or likely pathogenic. At the same time, 69 probands (15%) had their VUS downgraded to cancer risk equivalent to the general population level. Conclusion A review of archival variants from BRCA1 and BRCA2 genetic testing changed the management for 31.8% of the families due to increased or reduced risk. We encourage regular updates of variant databases, reference to normal population and collaboration between research laboratories on functional studies to define the clinical significances of VUS better.

TOP2A and CENPF are synergistic master regulators activated in cervical cancer

Abstract Background Identification of master regulators (MRs) using transcriptome data in cervical cancer (CC) could help us to develop biomarkers and find novel drug targets to fight this disease. Methods We performed differential expression (DE) analyses of public microarray and RNA-seq transcriptome data of CC and normal cervical tissues (N). Virtual Inference of Protein activity by Enriched Regulon analysis (VIPER) was used to convert the DE outcomes to differential activity (DA) signature for MRs. Synergy analysis was conducted to study synergistic effect of MR-pairs. TCGA and microarray data were used to test the association of expression of a MR and a clinical feature or a molecular feature (e.g. somatic mutations). Various bioinformatic tools/websites (DAVID, GEPIA2, Oncomine, cBioPortal) were used to analyze the expression of the top MRs and their regulons. Results Ten DE and 10 DA signatures were generated for CC. Two MRs, DNA topoisomerase II alpha (TOP2A) and centromere protein F (CENPF) were found to be up-regulated, activated and synergistic in CC compared to N across the 10 datasets. The two MRs activate a common set of genes (regulons) with functions in cell cycle, chromosome, DNA damage etc. Higher expression of CENPF was associated with metastasis. High expression of both MRs is associated with somatic mutation of a set of genes including tumor suppressors (TP53, MSH2, RB1) and genes involved in cancer pathways, cell cycle, DNA damage and repair. The magnitude of up-regulation and the absolute expression level of both MRs in CC are significantly higher compared to many other cancer types. Conclusion TOP2A and CENPF are a synergistic pair of MRs that are overexpressed and activated in CC. Their high expression is correlated with some prognosis features (e.g. metastasis) and molecular features (e.g. somatic mutations) and distinctly high in CC vs. many other cancer types. They may be good biomarkers and anticancer drug targets for CC.

Analysis of genetic variation in human papillomavirus type 16 E1 and E2 in women with cervical infection in Xinjiang, China

Abstract Background Xinjiang is one of the regions with a high incidence of cervical cancer, and the genetic variation of human papillomavirus may increase its ability to infect the human body and enhance virus-mediated immune escape ability. Methods Sanger sequencing of the HPV16 genome from 165 samples positive for HPV16 infection and phylogenetic analysis of the E1 and E2 genes revealed the gene polymorphism of HPV16 in Xinjiang. Results The results showed that there were 109 samples with variations in HPV16 E1, 48 sites with nucleotide variations (19 missense variations and 29 synonymous variations), and 91 samples with variations in HPV16 E2, 25 sites with nucleotide variations (20 missense variations and five synonymous variations). Conclusions From the phylogenetic tree results, 149 samples were of the European variant and 16 samples were of the Asian variant. No African or North American/Asian variant types were found.

Tumorigenic role of Pak4 in ovarian cancer and its correlation with immune infiltration

Abstract Background Ovarian cancer is the most common cause of gynecological cancer death. Pak4 has been proved to be tumorigenic in many types of cancers, but its role in ovarian cancer is still not clarified. Methods In this study, we used immunohistochemistry to investigate into Pak4 expression in different histological types of ovarian cancer. TIMER, TISCH2, GEPIA, ualcan, KM plotter, GSCA and GeneMANIA were used to identify the prognostic roles and gene regulation networks of Pak4 in ovarian cancer. Immune infiltration levels were investigated using TIMER database. Results Pak4 was highly expressed in ovarian cancers, regardless of different FIGO stages and histological grades. Single cell sequencing database proved that Pak4 was highly expressed in malignant ovarian cancer cells. Pak4 level was significantly correlated with different histological types of ovarian cancer. Pak4 expression was negatively connected with OS and PFS of ovarian cancer patients. Functions of Pak4 and its interacted genes were mainly involved in protein serine/threonine kinase activity, regulation of actin filament-based process and regulation of cytoskeleton organization. Pak4 level was negatively correlated with immune biomarkers of B cell infiltration ( p  = 2.39e-05), CD8 + T cell infiltration ( p  = 1.51e-04), neutrophil ( p  = 1.74e-06) and dendritic cell ( p  = 4.41e-08). Close correlation was found between Pak4 expression and T cell exhaustion ( p  &lt; 0.05). Conclusions Our results demonstrated the expression level, gene interaction networks and immune infiltration levels of Pak4 in ovarian cancer. And the results revealed role of Pak4 in tumorigenesis and the possibility to be a potential immunotherapeutic target.

Comprehensive analysis of PHGDH for predicting prognosis and immunotherapy response in patients with endometrial carcinoma

Abstract Background PHGDH (Phosphoglycerate Dehydrogenase) is the first branch enzyme in the serine biosynthetic pathway and plays a vital role in several cancers. However, little is known about the clinical significance of PHGDH in endometrial cancer. Methods Clinicopathological data of endometrial cancer were downloaded from the Cancer Genome Atlas database (TCGA). First, the expression of PHGDH in pan-cancer was investigated, as well as the expression and prognostic value of PHGDH in endometrial cancer. The effect of PHGDH expression on the prognosis of endometrial cancer was analyzed by Kaplan-Meier plotter and Cox regression. The relationship between PHGDH expression and clinical characteristics of endometrial cancer was investigated by logistic regression. Receiver operating characteristic (ROC) curves and nomograms were developed. Possible cellular mechanisms were explored using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, the Gene Ontology (GO), and gene set enrichment analysis (GSEA). Finally, TIMER and CIBERSORT were used to analyze the relationship between PHGDH expression and immune infiltration. CellMiner™ was used to analyze the drug sensitivity of PHGDH. Results The results showed that PHGDH expression was significantly higher in endometrial cancer tissues than in normal tissues at mRNA and protein levels. Kaplan-Meier survival curves showed that patients in the high expression group had shorter overall survival (OS) and disease free survival (DFS) than patients in the low PHGDH expression group. Multifactorial COX regression analysis further supported that high PHGDH expression was an independent risk factor associated with prognosis in patients with endometrial cancer. The results showed estrogen response, mTOR, K-RAS, and epithelial mesenchymal transition (EMT) were differentially elevated in the high-expression group of the PHGDH group. CIBERSORT analysis showed that PHGDH expression is related to the infiltration of multiple immune cells. When PHGDH is highly expressed, the number of CD8 + T cells decreases. Conclusion PHGDH plays a vital role in the development of endometrial cancer, which is related to tumor immune infiltration, and can be used as an independent diagnostic and prognostic marker for endometrial cancer.

Machine learning-based screening and validation of pyroptosis-associated prognostic genes and potential drugs in cervical cancer

Pyroptosis is a newly discovered form of programmed cell death, but its mechanism in the development of cervical cancer has not been elucidated. Cervical cancer differentially expressed pyroptosis-related genes were identified via bioinformatic analysis Gene Expression Omnibus (GEO) dataset GSE7803, GSE9750, GSE63514 and GSE67522. The correlation between the expression of pyroptosis-related genes in normal cervical tissue and cervical cancer tissue was analyzed through the TCGA database. Using LASSO regression algorithm to establish a prediction model for the obtained genes related to pyroptosis. Exploring the functions of differentially expressed genes through GO and KEGG pathway analysis. Using PPI network analysis to screen hub genes, using CIBERSORT method for immune infiltration analysis of prognostic genes, and finally predicting drug-gene interactions in DGIdb database. A total of 19 pyroptosis-related genes were screened from the GEO dataset of cervical cancer tissues, revealing their regulation of endopeptidase activity, inflammation response, positive regulation of cytokine production and cellular response to environmental stimuli. LASSO regression algorithm was used to establish prediction models for 7 of these genes, and 3 pyroptosis-related genes (SPP1, VEGFA, and CXCL8) closely associated with the prognosis of cervical cancer were identified. qRT-PCR confirmed that compared with normal cervical tissue, the expression of SPP1, VEGFA, and CXCL8 was increased in cervical cancer (P<0.05). SPP1, VEGFA, and CXCL8 are most closely related to macrophages, Th2, and neutrophils, respectively. 148 potential targeted drugs targeting key genes were predicted, providing a possible basis for predicting the prognosis and treatment of cervical cancer. Knocking down SPP1 can inhibit cell proliferation and migration in cervical cancer cells in vitro. In conclusion, our study has identified key genes related to pyroptosis in cervical cancer, which potentially become effective clinical prognostic biomarkers, and further research is needed to explore their underlying mechanisms.

Case report: response to immunotherapy and association with the fh gene in hereditary leiomyomatosis and renal cell cancer-associated renal cell cancer

Abstract Hereditary leiomyomatosis and renal cell cancer (HLRCC) is a rare autosomal dominant syndrome caused by a germline mutation in the fumarate hydratase (FH) gene that manifests with cutaneous leiomyomas, uterine fibroids, and renal cell cancer (RCC). Patients with HLRCC-associated RCC (HLRCC-RCC) have aggressive clinical courses, but there is no standardized therapy for advanced HLRCC-RCC. In this study, we described a case of aggressive HLRCC in a 33-year-old female who exhibited a novel heterozygous germline insertion mutation in exon 8 of the FH gene (c.1126 C &gt; T; p.Q376*). The patient underwent laparoscopic resection of the right kidney, but metastases appeared within 3 months after surgery. Histological staining of the resected tumor revealed high expression levels of programmed cell death-ligand 1 (PD-L1). Therefore, the patient was treated with immunotherapy. The patient achieved a partial response to immunotherapy, and the treatment of metastatic lesions has continued to improve. A thorough literature review pinpointed 76 historical cases of HLRCC-RCC that had undergone immunotherapy. From this pool, 46 patients were selected for this study to scrutinize the association between mutations in the FH gene and the effectiveness of immunotherapy. Our results indicate that immunotherapy could significantly improve the overall survival (OS) of patients with HLRCC-RCC. However, no influence of different mutations in the FH germline gene on the therapeutic efficacy of immunotherapy was observed. Therefore, our study suggested that immunotherapy was an effective therapeutic option for patients with HLRCC regardless of the type of FH germline mutation.

Analysis of human papillomavirus type 16 E4, E5 and L2 gene variations among women with cervical infection in Xinjiang, China

Abstract Background There is a high incidence of cervical cancer in Xinjiang. Genetic variation in human papillomavirus may increase its ability to invade, spread, and escape host immune response. Methods HPV16 genome was sequenced for 90 positive samples of HPV16 infection. Sequences of the E4, E5 and L2 genes were analysed to reveal sequence variation of HPV16 in Xinjiang and the distribution of variation among the positive samples of HPV16 infection. Results Eighty-one of the 90 samples of HPV16 infection showed variation in HPV16 E4 gene with 18 nucleotide variation sites, of which 8 sites were synonymous variations and 11 missense variations. 90 samples of HPV16 infection showed variation in HPV16 E5 and L2 genes with 16 nucleotide variation sites (6 synonymous, 11 missense variations) in the E5 gene and 100 nucleotide variation sites in L2 gene (37 synonymous, 67 missense variations). The frequency of HPV16 L2 gene missense variations G3377A, G3599A, G3703A, and G3757A was higher in the case groups than in the control groups. Conclusions Phylogenetic tree analysis showed that 87 samples were European strains, 3 cases were Asian strains, there were no other variations, and G4181A was related to Asian strains. HPV16 L2 gene missense variations G3377A, G3599A, G3703A, and G3757A were significantly more frequent in the case groups than in the control groups.

A macrophage related signature for predicting prognosis and drug sensitivity in ovarian cancer based on integrative machine learning

Abstract Background Ovarian cancer ranks the leading cause of gynecologic cancer-related death in the United States and the fifth most common cause of cancer-related mortality among American women. Increasing evidences have highlighted the vital role of macrophages M2/M1 proportion in tumor progression, prognosis and immunotherapy. Methods Weighted gene co-expression network analysis (WGCNA) was performed to identify macrophages related markers. Integrative procedure including 10 machine learning algorithms were performed to develop a prognostic macrophage related signature (MRS) with TCGA, GSE14764, GSE140082 datasets. The role of MRS in tumor microenvironment (TME) and therapy response was evaluated with the data of CIBERSORT, MCPcounter, QUANTISEQ, XCELL, CIBERSORT-ABS, TIMER and EPIC, GSE91061 and IMvigor210 dataset. Results The optimal MRS developed by the combination of CoxBoost and StepCox[forward] algorithm served as an independent risk factor in ovarian cancer. Compared with stage, grade and other established prognostic signatures, the current MRS had a better performance in predicting the overall survival rate of ovarian cancer patients. Low risk score indicated a higher TME score, higher level of immune cells, higher immunophenoscore, higher tumor mutational burden, lower TIDE score and lower IC50 value in ovarian cancer. The survival prediction nomogram had a good potential for clinical application in predicting the 1-, 3-, and 5-year overall survival rate of ovarian cancer patients. Conclusion All in all, the current study constructed a powerful prognostic MRS for ovarian cancer patients using 10 machine learning algorithms. This MRS could predict the prognosis and drug sensitivity in ovarian cancer.

Establishment and validation of an immune infiltration predictive model for ovarian cancer

Abstract Background The most prevalent mutation in ovarian cancer is the TP53 mutation, which impacts the development and prognosis of the disease. We looked at how the TP53 mutation associates the immunophenotype of ovarian cancer and the prognosis of the disease. Methods We investigated the state of TP53 mutations and expression profiles in culturally diverse groups and datasets and developed an immune infiltration predictive model relying on immune-associated genes differently expressed between TP53 WT and TP53 MUT ovarian cancer cases. We aimed to construct an immune infiltration predictive model (IPM) to enhance the prognosis of ovarian cancer and investigate the impact of the IPM on the immunological microenvironment. Results TP53 mutagenesis affected the expression of seventy-seven immune response-associated genes. An IPM was implemented and evaluated on ovarian cancer patients to distinguish individuals with low- and high-IPM subgroups of poor survival. For diagnostic and therapeutic use, a nomogram is thus created. According to pathway enrichment analysis, the pathways of the human immune response and immune function abnormalities were the most associated functions and pathways with the IPM genes. Furthermore, patients in the high-risk group showed low proportions of macrophages M1, activated NK cells, CD8+ T cells, and higher CTLA-4, PD-1, PD-L1, and TIM-3 than patients in the low-risk group. Conclusions The IPM model may identify high-risk patients and integrate other clinical parameters to predict their overall survival, suggesting it is a potential methodology for optimizing ovarian cancer prognosis.

A novel extrachromosomal circular DNA related genes signature for overall survival prediction in patients with ovarian cancer

AbstractObjectiveOvarian cancer (OV) has a high mortality rate all over the world, and extrachromosomal circular DNA (eccDNA) plays a key role in carcinogenesis. We wish to study more about the molecular structure of eccDNA in the UACC-1598–4 cell line and how its genes are associated with ovarian cancer prognosis.MethodsWe sequenced and annotated the eccDNA by Circle_seq of the OV cell line UACC-1598–4. To acquire the amplified genes of OV on eccDNA, the annotated eccDNA genes were intersected with the overexpression genes of OV in TCGA. Univariate Cox regression was used to find the genes on eccDNA that were linked to OV prognosis. The least absolute shrinkage and selection operator (LASSO) and cox regression models were used to create the OV prognostic model, as well as the receiver operating characteristic curve (ROC) curve and nomogram of the prediction model. By applying the median value of the risk score, the samples were separated into high-risk and low-risk groups, and the differences in immune infiltration between the two groups were examined using ssGSEA.ResultsEccDNA in UACC-1598–4 has a length of 0-2000 bp, and some of them include the whole genes or gene fragments. These eccDNA originated from various parts of chromosomes, especially enriched in repeatmasker, introns, and coding regions. They were annotated with 2188 genes by Circle_seq. Notably, the TCGA database revealed that a total of 198 of these eccDNA genes were overexpressed in OV (p &lt; 0.05). They were mostly enriched in pathways associated with cell adhesion, ECM receptors, and actin cytoskeleton. Univariate Cox analysis showed 13 genes associated with OV prognosis. LASSO and Cox regression analysis were used to create a risk model based on remained 9 genes. In both the training (TCGA database) and validation (International Cancer Genome Consortium, ICGC) cohorts, a 9-gene signature could successfully discriminate high-risk individuals (allp &lt; 0.01). Immune infiltration differed significantly between the high-risk and low-risk groups. The model’s area under the ROC curve was 0.67, and a nomograph was created to assist clinician.ConclusionEccDNA is found in UACC-1598–4, and part of its genes linked to OV prognosis. Patients with OV may be efficiently evaluated using a prognostic model based on eccDNA genes, including SLC7A1, NTN1, ADORA1, PADI2, SULT2B1, LINC00665, CILP2, EFNA5, TOMM.

LRRC superfamily expression in stromal cells predicts the clinical prognosis and platinum resistance of ovarian cancer

AbstractBackgroundLeucine-rich repeat sequence domains are known to mediate protein‒protein interactions. Recently, some studies showed that members of the leucine rich repeat containing (LRRC) protein superfamily may become new targets for the diagnosis and treatment of tumours. However, it is not known whether any of the LRRC superfamily genes is expressed in the stroma of ovarian cancer (OC) and is associated with prognosis.MethodsThe clinical data and transcriptional profiles of OC patients from the public databases TCGA (n = 427), GTEx (n = 88) and GEO (GSE40266 and GSE40595) were analysed by R software. A nomogram model was also generated through R. An online public database was used for auxiliary analysis of prognosis, immune infiltration and protein‒protein interaction (PPI) networks. Immunohistochemistry and qPCR were performed to determine the protein and mRNA levels of genes in high-grade serous ovarian cancer (HGSC) tissues of participants and the MRC-5 cell line induced by TGF-β.ResultsLRRC15 and LRRC32 were identified as differentially expressed genes from the LRRC superfamily by GEO transcriptome analysis. PPI network analysis suggested that they were most enriched in TGF-β signalling. The TCGA-GTEx analysis results showed that only LRRC15 was highly expressed in both cancer-associated fibroblasts (CAFs) and the tumour stroma of OC and was related to clinical prognosis. Based on this, we developed a nomogram model to predict the incidence of adverse outcomes in OC. Moreover, LRRC15 was positively correlated with CAF infiltration and negatively correlated with CD8 + T-cell infiltration. As a single indicator, LRRC15 had the highest accuracy (AUC = 0.920) in predicting the outcome of primary platinum resistance.ConclusionsThe LRRC superfamily is related to the TGF-β pathway in the microenvironment of OC. LRRC15, as a stromal biomarker, can predict the clinical prognosis of HGSC and promote the immunosuppressive microenvironment. LRRC15 may be a potential therapeutic target for reversing primary resistance in OC.

Occurrence of variants of unknown clinical significance in genetic testing for hereditary breast and ovarian cancer syndrome and Lynch syndrome: a literature review and analytical observational retrospective cohort study

Abstract Background and purpose Over the last decade, the implementation of multigene panels for hereditary tumor syndrome has increased at our institution (Inselspital, University Hospital Berne, Switzerland). The aim of this study was to determine the prevalence of variants of unknown significance (VUS) in patients with suspected Lynch syndrome and suspected hereditary breast and ovarian cancer syndrome, the latter in connection with the trend toward ordering larger gene panels. Results Retrospectively collected data from 1057 patients at our institution showed at least one VUS in 126 different cases (11.9%). In patients undergoing genetic testing for BRCA1/2 , the prevalence of VUS was 6%. When &lt; 10 additional genes were tested in addition to BRCA1/2 , the prevalence increased to 13.8%, and 31.8% for &gt; 10 additional genes, respectively. The gene most frequently affected with a VUS was ATM . 6% of our patients who were tested for Lynch syndrome had a VUS result in either MLH1, MSH2 or MSH6 . Conclusions Our data demonstrate that panel testing statistically significantly increases VUS rates due to variants in non- BRCA genes. Good genetic counseling before and after obtaining results is therefore particularly important when conducting multigene panels to minimize patient uncertainty due to VUS results.

Considerations for feature selection using gene pairs and applications in large-scale dataset integration, novel oncogene discovery, and interpretable cancer screening

Abstract Background Advancements in transcriptomic profiling have led to the emergence of new challenges regarding data integration and interpretability. Variability between measurement platforms makes it difficult to compare between cohorts, and large numbers of gene features have encouraged the use black box methods that are not easily translated into biologically and clinically meaningful findings. We propose that gene rankings and algorithms that rely on relative expression within gene pairs can address such obstacles. Methods We implemented an innovative process to evaluate the performance of five feature selection methods on simulated gene-pair data. Along with TSP, we consider other methods that retain more information in their score calculations, including the magnitude of gene expression change as well as within-class variation. Tree-based rule extraction was also applied to serum microRNA (miRNA) pairs in order to devise a noninvasive screening tool for pancreatic and ovarian cancer. Results Gene pair data were simulated using different types of signal and noise. Pairs were filtered using feature selection approaches, including top-scoring pairs (TSP), absolute differences between gene ranks, and Fisher scores. Methods that retain more information, such as the magnitude of expression change and within-class variance, yielded higher classification accuracy using a random forest model. We then demonstrate two powerful applications of gene pairs by first performing large-scale integration of 52 breast cancer datasets consisting of 10,350 patients. Not only did we confirm known oncogenes, but we also propose novel tumorigenic genes, such as BSDC1 and U2AF1, that could distinguish between tumor subtypes. Finally, circulating miRNA pairs were filtered and salient rules were extracted to build simplified tree ensemble learners (STELs) for four types of cancer. These accessible clinical frameworks detected pancreatic and ovarian cancer with 84.8 and 93.6% accuracy, respectively. Conclusion Rank-based gene pair classification benefits from careful feature selection methods that preserve maximal information. Gene pairs enable dataset integration for greater statistical power and discovery of robust biomarkers as well as facilitate construction of user-friendly clinical screening tools.

Integrative network analysis identifies potential targets and drugs for ovarian cancer

Abstract Background Though accounts for 2.5% of all cancers in female, the death rate of ovarian cancer is high, which is the fifth leading cause of cancer death (5% of all cancer death) in female. The 5-year survival rate of ovarian cancer is less than 50%. The oncogenic molecular signaling of ovarian cancer are complicated and remain unclear, and there is a lack of effective targeted therapies for ovarian cancer treatment. Methods In this study, we propose to investigate activated signaling pathways of individual ovarian cancer patients and sub-groups; and identify potential targets and drugs that are able to disrupt the activated signaling pathways. Specifically, we first identify the up-regulated genes of individual cancer patients using Markov chain Monte Carlo (MCMC), and then identify the potential activated transcription factors. After dividing ovarian cancer patients into several sub-groups sharing common transcription factors using K-modes method, we uncover the up-stream signaling pathways of activated transcription factors in each sub-group. Finally, we mapped all FDA approved drugs targeting on the upstream signaling. Results The 427 ovarian cancer samples were divided into 3 sub-groups (with 100, 172, 155 samples respectively) based on the activated TFs (with 14, 25, 26 activated TFs respectively). Multiple up-stream signaling pathways, e.g., MYC, WNT, PDGFRA (RTK), PI3K, AKT TP53, and MTOR, are uncovered to activate the discovered TFs. In addition, 66 FDA approved drugs were identified targeting on the uncovered core signaling pathways. Forty-four drugs had been reported in ovarian cancer related reports. The signaling diversity and heterogeneity can be potential therapeutic targets for drug combination discovery. Conclusions The proposed integrative network analysis could uncover potential core signaling pathways, targets and drugs for ovarian cancer treatment.

Genomic landscape, immune characteristics and prognostic mutation signature of cervical cancer in China

Abstract Purpose This study aimed to analyse the genomic alteration profiles and immune characteristics of a cohort of Chinese cervical cancer patients to understand why certain patients benefited from molecular targeted therapies and immunotherapy as well as their prognostic significance. Methods PD-L1 expression and clinicopathological information were obtained from 98 cervical cancer patients. Differences in PD-L1 expression and gene mutations between squamous cell carcinoma (SCC) and adenocarcinoma (AC) were analysed by the chi-square test or Fisher's exact test. Differences in gene mutations between our cohort and The Cancer Genome Atlas (TCGA) cohort were tested by Fisher's exact test. Logistic regression was used to analyse factors influencing TMB-high. Results Positive PD-L1 expression was significantly higher in cervical SCC than in cervical AC (87% vs. 39%, p &lt; 0.001). Frequently mutated genes in cervical cancer included the PIK3CA, KMT2D, and KMT2C genes, among others. PIK3CA gene mutation rates were significantly higher in SCC than in AC (p = 0.004). The TERT gene mutation rate was significantly higher in our cohort than in the TCGA cohort (12% vs. 1%, p &lt; 0.001). The independent predictors of high TMB were KMT2C and LRP1B gene mutations (p &lt; 0.05). We also found that PTEN mutations were associated with worse survival (median PFS, 12.16 vs. 21.75 months, p = 0.0024). Conclusion Cervical SCC and AC have different molecular profiles and immune characteristics, suggesting that targeted treatments for SCC and AC patients may improve clinical outcomes. KMT2C and LRP1B gene mutations are independent predictors of TMB-high status in cervical cancer. We also proposed the prognostic value of PTEN mutations.

The linkage of NF-κB signaling pathway-associated long non-coding RNAs with tumor microenvironment and prognosis in cervical cancer

Abstract Background NF-κB signaling pathway participate closely in regulating inflammation and immune response in many cancers. Long non-coding RNAs (lncRNAs) associated with NF-κB signaling have not been characterized in cervical cancer. This study revealed the linkage between tumor microenvironment and NF-κB signaling-associated lncRNAs in cervical cancer. Materials and methods The expression profiles of cervical cancer samples from The Cancer Genome Atlas (TCGA) database were downloaded. NF-κB signaling-associated lncRNAs were screened as a basis to perform molecular subtyping. Immune cell infiltration was assessed by ESTIMATE, Microenvironment Cell Populations (MCP)-counter and single sample gene set enrichment analysis (ssGSEA). The key NF-κB signaling-associated lncRNAs were identified by univariate analysis, least absolute shrinkage and selection operator, and stepAIC. Results Three molecular subtypes or clusters (cluster 3, cluster 2, and cluster 1) were categorized based on 27 prognostic NF-κB signaling-associated lncRNAs. Cluster 2 had the worst prognosis, highest immune infiltration, as well as the highest expression of most of immune checkpoints. Three clusters showed different sensitivities to immunotherapy and chemotherapy. Six key NF-κB signaling-associated lncRNAs were screened to establish a six-lncRNA risk model for predicting cervical cancer prognosis. Conclusions NF-κB signaling-associated lncRNAs played an important role in regulating immune microenvironment. The subtyping based on NF-κB signaling-associated lncRNAs may assist in the selection of optimal treatments. The six key NF-κB signaling-associated lncRNAs could act as prognostic biomarkers in prognostic prediction for cervical cancer.

Genetic diagnosis of pseudomyxoma peritonei originating from mucinous borderline tumor inside an ovarian teratoma

Abstract Background Pseudomyxoma peritonei is a rare disease condition mainly caused by primary mucinous tumors from the appendix and rarely from the ovary, such as when mucinous ovarian tumors arise from within a teratoma. Molecular analyses of pseudomyxoma from the appendix showed that KRAS and GNAS pathogenic variants are common genetic features of pseudomyxoma peritonei. However, the origin of the tumors is difficult to be identified via genetic variants alone. This study presents a case of pseudomyxoma peritonei of ovarian origin, which was diagnosed by comprehensive genomic profiling with ploidy analysis in a series of primary, recurrent, and autopsy tumor specimens. Case presentation A 40-year-old woman was diagnosed with Stage IC2 mucinous ovarian tumor of borderline malignancy with mature cystic teratoma, upon clinical pathology. Immunohistochemical analysis suggested that the mucinous tumor was derived from the intestinal component of an ovarian teratoma. Three years later, intraperitoneal recurrence was detected, which subsequently progressed to pseudomyxoma peritonei. Genomic analysis detected KRAS (G12D), GNAS (R201C), and FBXW7 (R367*) variants in the primary tumor. In addition, the tumor showed aneuploidy with loss of heterozygosity (LOH) in all its chromosomes, which suggested that the primary ovarian tumor was derived from germ cells. Existence of one Barr body suggested the existence of uniparental disomy of the tumors throughout the genome, instead of a haploid genotype. All three pathogenic variants remained positive in the initial recurrent tumor, as well as in the paired DNA from the whole blood in pseudomyxoma peritonei. The pathogenic variant of KRAS (G12D) was also identified in the autopsy specimen of the appendix by droplet digital polymerase chain reaction. Conclusions This study pathologically and genetically confirmed that the primary ovarian borderline tumor was derived from the intestinal component of an ovarian teratoma, and that the subsequent pseudomyxoma peritonei progressed from the primary ovarian tumor. Integrative genomic analysis was useful to identify cellular origin of tumors, as well as to precisely interpret the process of disease progression.

A novel prognostic prediction model based on seven immune-related RNAs for predicting overall survival of patients in early cervical squamous cell carcinoma

Abstract Background In this study, we aimed to mine immune-related RNAs expressed in early cervical squamous cell carcinoma to construct prognostic prediction models. Methods The RNA sequencing data of 309 cervical squamous cell carcinoma (CSCC) cases, including data of individuals with available clinical information, were obtained from The Cancer Genome Atlas (TCGA) database. We included 181 early-stage CSCC tumor samples with clinical survival and prognosis information (training dataset). Then, we downloaded the GSE44001 gene expression profile data from the National Center for Biotechnology Information Gene Expression Omnibus (validation dataset). Gene ontology annotation and the Kyoto Encyclopedia of Genes and Genomes pathway analyses were used to analyze the biological functions of differentially expressed immune-related genes (DEIRGs). We established protein–protein interactions and competing endogenous RNA networks using Cytoscape. Using the Kaplan–Meier method, we evaluated the association between the high- and low-risk groups and the actual survival and prognosis information. Our univariate and multivariate Cox regression analyses screened for independent prognostic factors. Results We identified seven prognosis-related signature genes (RBAKDN, CXCL2, ZAP70, CLEC2D, CD27, KLRB1, VCAM1), the expression of which was markedly associated with overall survival (OS) in CSCC patients. Also, the risk score of the seven-gene signature discripted superior ability to categorize CSCC patients into high-risk and low-risk groups, with a observablydifferent OS in the training and validation datasets. We screened two independent prognostic factors (Pathologic N and prognostic score model status) that correlated significantly by univariate and multivariate Cox regression analyses in the TCGA dataset. To further explore the potential mechanism of immune-related genes, we observed associated essential high-risk genes with a cytokine–cytokine receptor interaction. Conclusions This study established an immune-related RNA signature, which provided a reliable prognostic tool and may be of great significance for determining immune-related biomarkers in CSCC.

Establishment and genetically characterization of patient-derived xenograft models of cervical cancer

Abstract Purpose Patient-derived xenograft (PDX) models were established to reproduce the clinical situation of original cancers and have increasingly been applied to preclinical cancer research. Our study was designed to establish and genetically characterize cervical cancer PDX models. Methods A total of 91 fresh fragments obtained from 22 surgically resected cervical cancer tissues were subcutaneously engrafted into female NOD-SCID mice. Hematoxylin and eosin (H&amp;E) staining was performed to assess whether the established PDX models conserved the histological features of original patient cervical cancer tissues. Moreover, a Venn diagram was applied to display the overlap of all mutations detected in whole-genome sequencing (WGS) data from patient original cervical cancer (F0) and F2-, F3-PDX models. The whole exome sequencing (WES) and the “maftools” package were applied to determine the somatic mutations among primary cervical cancers and the established PDX models. Results Our study successfully developed a panel of cervical cancer PDX models and the latency time of cervical cancer PDX model establishment was variable with a progressive decrease as the passage number increased, with a mean time to initial growth of 94.71 days in F1 engraftment to 40.65 days in F3 engraftment. Moreover, the cervical cancer PDX models preserved the histological features of their original cervical cancer. WGS revealed that the genome of original cervical cancer was preserved with high fidelity in cervical cancer PDX models throughout the xenografting and passaging process. Furthermore, WES demonstrated that the cervical cancer PDX models maintained the majority somatic mutations of original cervical cancer, of which the KMT2D, LRP1B, NAV3, TP53, FAT1, MKI67 and PKHD1L1 genes were identified as the most frequently mutated genes. Conclusions The cervical cancer PDX models preserved the histologic and genetic characteristics of their original cervical cancer, which helped to gain a deeper insight into the genetic alterations and lay a foundation for further investigation of the molecular targeted therapy of cervical cancer.

Identification of the role of endoplasmic reticulum stress genes in endometrial cancer and their association with tumor immunity

Abstract Background Endometrial cancer (EC) is one of the worldwide gynecological malignancies. Endoplasmic reticulum (ER) stress is the cellular homeostasis disturbance that participates in cancer progression. However, the mechanisms of ER Stress on EC have not been fully elucidated. Method The ER Stress-related genes were obtained from Gene Set Enrichment Analysis (GSEA) and GeneCards, and the RNA-seq and clinical data were downloaded from The Cancer Genome Atlas (TCGA). The risk signature was constructed by the Cox regression and the least absolute shrinkage and selection operator (LASSO) analysis. The significance of the risk signature and clinical factors were tested by time-dependent receiver operating characteristic (ROC) curves, and the selected were to build a nomogram. The immunity correlation was particularly analyzed, including the related immune cells, pathways, and immune checkpoints. Functional enrichment, potential chemotherapies, and in vitro validation were also conducted. Result An ER Stress-based risk signature, consisting of TRIB3, CREB3L3, XBP1, and PPP1R15A was established. Patients were randomly divided into training and testing groups with 1:1 ratio for subsequent calculation and validation. Based on risk scores, high- and low-risk subgroups were classified, and low-risk subgroup demonstrated better prognosis. The Area Under Curve (AUC) demonstrated a reliable predictive capability of the risk signature. The majority of significantly different immune cells and pathways were enriched more in low-risk subgroup. Similarly, several typical immune checkpoints, expressed higher in low-risk subgroup. Patients of the two subgroups responded differently to chemotherapies. Conclusion We established an ER Stress-based risk signature that could effectively predict EC patients’ prognosis and their immune correlation.