Detection and analysis of multiple biomarkers in ovarian cancer: clinical significance in diagnosis, treatment, and prognosis evaluation

Ji, Rui; Yong, Li; He, Chenyun; Zhu, Xinghua; He, Aiqin; Lu, Yunyan

doi:10.21037/gs-20-811

Cited by 5 publications

(2 citation statements)

References 39 publications

(29 reference statements)

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“…Among the mentioned OC tumor markers, CK7 and CA125 have the highest positive rate in malignant OC tissues compared with benign ovarian cancer tissues. Studies also shown that patients with negative expressions of CA125 and CK7 had significantly longer recurrence- free survival (RFS) time than those with positive expressions [ 44 ]. Thus, using CA125 and CK7 as malignant OC marker was practical for early diagnosis and lineage determination.…”

Section: Discussionmentioning

confidence: 99%

An experimental model for ovarian cancer: propagation of ovarian cancer initiating cells and generation of ovarian cancer organoids

Chen

Cheng

et al. 2022

BMC Cancer

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Background Ovarian cancer (OC) is the most lethal gynecological cancer due to the recurrence of drug-resistance. Cancer initiating cells (CICs) are proposed to be responsible for the aggressiveness of OC. The rarity and difficulty of in vitro long-term cultivation of CICs challenge the development of CIC-targeting therapeutics. Reprogramming cancer cells into induced cancer initiating cell (iCICs) could be an approach to solve these. Several inducible CICs have been acquired by activating the expression of stemness genes in different cancer cells. However, few reports have demonstrated the feasibility in OC. Methods Patients with primary OC receiving surgery were enrolled. Tumor tissue were collected, and OCT4, SOX2, and NANOG expressions were assessed by immunohistochemistry (IHC) staining to investigate the association of stemness markers with overall survival (OS). An high-grade serous ovarian cancer (HGSOC) cell line, OVCAR-3 was reprogrammed by transducing Yamanaka four factors OCT4, SOX2, KLF4 and MYC (OSKM) to establish an iOCIC model, iOVCAR-3-OSKM. CIC characteristics of iOVCAR-3-OSKM were evaluated by RT-PCR, sphere formation assay and animal experiments. Drug-resistance and migration ability were accessed by dye-efflux activity assay, MTT assay and migration assay. Gene profile was presented through RNA-sequencing. Lineage differentiation ability and organoid culture were determined by in vitro differentiation assays. Results In OC patients, the co-expression of multiple stem-related transcription factors (OCT4, SOX2, and NANOG) was associated with worse OS. iOVCAR-3-OSKM cells generated by reprogramming successfully exhibited stemness characteristics with strong sphere-forming and tumorigenesis ability. iOVCAR-3-OSKM cells also showed malignant potential with higher drug resistance to chemodrug, Paclitaxel (PTX) and migration ability. iOVCAR-3-OSKM was maintainable and expandable on feeder-dependent culture condition, it also preserved ovarian lineage differentiation abilities, which could well differentiate into OC cells with CK-7 and CA125 expressions and develop into an organoid mimic poor prognostic OC histological feature. Conclusions The establishment of iOVCAR-3-OSKM not only allows us to fill the gap in the information on induced CICs in OC but also provides a potential strategy to develop personalized CICs and organoid models for treating OC in the near future.

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Section: Discussionmentioning

confidence: 99%

An experimental model for ovarian cancer: propagation of ovarian cancer initiating cells and generation of ovarian cancer organoids

Chen

Cheng

et al. 2022

BMC Cancer

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“…У диференційній діагностиці для верифікації первинних муцинозних пухлин яєчників та метастазуючих муцинозних пухлин товстої кишки в тканину яєчника використовуються ЦКР 7, ЦКР 20, CDX2, MUC5AC та вілін [24,25]. ЦКР 7 експресується усіма типами пухлин яєчників, включаючи муцинозні, які також мають позитивну реакцію до ЦКР 20, віліну, у той же час CDX-2 має негативну реакцію (рис.1).…”

Section: імуногістохімічні особливості раку яєчників ім нікітіна тв к...unclassified

Immunohistochemical features of ovarian cancer

Nikitina,

Kopytsia,

Sukhostavets

et al. 2023

Buk. Med. Herald

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Ovarian cancer is the leading cause of death among women with gynecological oncological diseases. Diagnosis at the early stages of the disease allows for better chances of treatment and prognosis of ovarian cancer. It is reflected in the reduction of morbidity and mortality rates. An immunohistochemical study is used for differential diagnosis and accurate establishment of the histotype of ovarian cancer. Objective: to systematize literature data about the immunohistochemical features of ovarian cancer. In 90% of cases, ovarian cancer is epithelial-stromal, among which the main histological types are distinguished: serous (low- and high-grade), endometrioid, mucinous, and clear cell. In serous and other ovarian tumors, cytokeratins (CK) are widely used. CK 7 is present in all tumors, including mucinous tumors, expressing CK 20. Mucinous tumors also express villin and are negative for CDX-2. Endometrioid tumors react positively to vimentin, which is not observed in serous and mucinous tumors. Clear cell carcinoma expresses CK 8, 18, and EMA (epithelial membrane antigen).Conclusion. Therefore, immunohistochemical features of ovarian cancer ensure accuracy in the differential diagnosis of one or another histological type.

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Systematic analysis of the relationship between ovarian cancer prognosis and alternative splicing

et al. 2021

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Background Ovarian cancer(OC) is the gynecological tumor with the highest mortality rate, effective biomarkers are of great significance in improving its prognosis. In recent years, there have been many studies on alternative splicing (AS) events, and the role of AS events in tumor has become a focus of attention. Methods Data were downloaded from the TCGA database and Univariate Cox regression analysis was performed to determine AS events associated with OC prognosis.Eight prognostic models of OC were constructed in R package, and the accuracy of the models were evaluated by the time-dependent receiver operating characteristic (ROC) curves.Eight types of survival curves were drawn to evaluate the differences between the high and low risk groups.Independent prognostic factors of OC were analyzed by single factor independent analysis and multi-factor independent prognostic analysis.Again, Univariate Cox regression analysis was used to analyze the relationship between splicing factors(SF) and AS events, and Gene Ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) enrichment analysis were performed on OS-related SFs to understand the pathways. Results Univariate Cox regression analysis showed that among the 15,278 genes, there were 31,286 overall survival (OS) related AS events, among which 1524 AS events were significantly correlated with OS. The area under the time-dependent receiver operating characteristic curve (AUC) of AT and ME were the largest and the RI was the smallest,which were 0.757 and 0.68 respectively. The constructed models have good value for the prognosis assessment of OC patients. Among the eight survival curves, AP was the most significant difference between the high and low risk groups, with a P value of 1.61e − 1.The results of single factor independent analysis and multi-factor independent prognostic analysis showed that risk score calculated by the model and age could be used as independent risk factors.According to univariate COX regression analysis,109 SFs were correlated with AS events and adjusted in two ways: positive and negative. Conclusions SFs and AS events can directly or indirectly affect the prognosis of OC patients. It is very important to find effective prognostic markers to improve the survival rate of OC.

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Detection and analysis of multiple biomarkers in ovarian cancer: clinical significance in diagnosis, treatment, and prognosis evaluation

Cited by 5 publications

References 39 publications

An experimental model for ovarian cancer: propagation of ovarian cancer initiating cells and generation of ovarian cancer organoids

An experimental model for ovarian cancer: propagation of ovarian cancer initiating cells and generation of ovarian cancer organoids

Immunohistochemical features of ovarian cancer

Systematic analysis of the relationship between ovarian cancer prognosis and alternative splicing

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