Expression Of Rad6, Ddb2 And Cancer Stem Cell / Csc Protein (Cd44+/Cd24-) On Therapy Response To The Administration Of Chemotherapy In Ovarian Cancer: A Prospective Flow Cytometry Study

doi:10.21203/rs.3.rs-1440070/v1

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Expression Of Rad6, Ddb2 And Cancer Stem Cell / Csc Protein (Cd44⁺/Cd24^-) On Therapy Response To The Administration Of Chemotherapy In Ovarian Cancer: A Prospective Flow Cytometry Study

https://doi.org/10.21203/rs.3.rs-1440070/v1

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Objective: Ovarian cancer is the 8^th deadliest women’s cancer in the world. In 2018 there were 295.414 new cases with 184.799 deaths. In Indonesia, there are 13.310 (7,1%) ovarian cancer of 188.231 female cancer with an incidence of 9,7/100,000. Almost all patients experienced chemoresistance, recurrence, and poor prognosis after cytoreductive surgery followed by platinum-based chemotherapy. Chemo-resistant cancer cells have characteristic expressions of RAD6, DDB2, and cancer stem cell proteins (CSCs, CD44⁺/CD24^-). RAD6 is a UBE2 enzyme required for DNA repair, mutagenesis, and proliferation. DNA damage-binding protein 2 (DDB2) protein is an amino acid that repairs the excision of nucleotides in DNA. CD44⁺/CD24^- protein is a transmembrane glycoprotein. Increased expression of CD44⁺/CD24^-, RAD6, and decreased DDB2 are believed to be associated with chemoresistance, recurrence, and poor prognosis of the disease.

Methods: This study is a prospective cohort of 32 people in each group at the Obstetrics-gynecology and pathology Department of Cipto Mangunkusumo, Tarakan, Dharmais, and Fatmawati Hospital. All suspected ovarian cancer patients will undergo cytoreductive debulking and histopathological examination. Chemotherapy will be given for six series followed by six months of observation. We determine therapy response with the RECIST Criteria (Response Criteria in Solid Tumors) then classify it into chemo-resistant or chemo-sensitive groups. The patient will perform Flow cytometry blood tests to examine the expression of RAD6 and DDB2 and CD44⁺/CD24^-. We used univariate, bivariate, and multivariate (logistic regression) analysis in this research.

Results: There was a significant relationship between increased levels of CD44⁺/CD24^-,RAD6, and reduced DDB2 protein (p<0,05) with chemoresistance of ovarian cancer. Logistic regression test showed that the results of CD44⁺/CD24^‑ also had significant results compared to other variables.

Conclusion: These results indicate that CD44⁺/CD24^-, RAD6, and DDB2 are significantly associated with ovarian cancer chemoresistance, and CD44⁺/CD24^-is the primary marker to predict ovarian cancer chemoresistance.

Ovarian Cancer

CD44+/CD24-

RAD6

DDB2

chemoresistance

Ovarian cancer is the 8th deadliest of women's cancer worldwide. In 2018, there were 295.414 new cases with 184.799 deaths. In Indonesia, there were 13.310 (7,1%) ovarian cancer cases of 188.231 cancer with an incidence of 9,7 per 100.000 (1). Usually, 70% of patients present with an advanced stage (III-IV) (2).

The standard treatment for ovarian cancer is cytoreductive surgery followed by platinum-based chemotherapy. Almost all patients experienced disease recurrence. Recent studies said that standard therapy had a chemosensitivity and chemoresistance rate of 77,4% and 18,1%, respectively. Recent studies said that ovarian cancer patients had a 12-month progression-free survival (PFS) with overall survival (OS) was about 30 months (3, 4). The low survival rate of ovarian cancer patients is due to chemotherapy resistance caused by cancer stem cells (CSCs) protein.

CSCs are a new term that is believed to play an essential role in the initiation, tumor growths, metastasize, recurrence, and the presence of resistance. Meng et al. said that ovarian stem cell cancer with CD44⁺/CD24⁻ was 71–93% resistant to all chemotherapy agents, a recurrence rate of 83% (p = 0,003), and median PFS of 6 months (5). Hu et al. found that CD44⁺ was positively expressed in the chemotherapy-resistant ovarian cancer significantly 91,17% with p < 0,05.(6)

Attention to CSCs mechanism has focused on the DNA damage response (DDR) in the tumorigenic process. Increased DDR can prevent the formation of CSCs and chemo-resistant cells. The DDR pathway consists of post replication repair (PRR), nucleotide excision repair (NER), etc. The process of NER occurs through several DNA binding proteins, such as DNA damage binding protein 2 (DDB2 or XPE) while in the PRR process, there is an expression of E2 ubiquitin-conjugating enzymes (UBE2) protein, such as RAD6 (7).

RAD6 is a UBE2 protein required for DNA regulation, associated with mitotic abnormalities and cell transformation. Increased expression of RAD6 enhances stemness function, chemoresistance, progression, and metastasize. The RAD6 on DNA is associated with chemoresistance and poor clinical prognosis in ovarian cancer. Somasagara et al. reported that RAD6 expression < 5 and > 5 was associated with 37,5% and 70% recurrence, respectively (8).

DDB2 is an amino acid in nucleotide excision DNA repair. Low expression of DDB2 causes increased chemotherapy resistance and leads to a poor prognosis (7). Han C et al. found that DDB2 mRNA expression levels > 0,5 had a better prognosis than values <0,5 (9). This decrease in protein expression causes a reduction in life expectancy (10).

Most cancer cells are sensitive to chemotherapy, but some CSCs are not detected and develop into disease recurrence. The CSC resistance will make the cells continue to move into the mitotic and interphase (G1, S, G2), while the G0/rest phase is inactive, continuously replicating (11). The aim of this study was to find relationships between CD44⁺/CD24^−, RAD6 and DDB2 with chemotherapy response in ovarian cancer and the ability of the three markers to predict ovarian cancer chemotherapy response

Study Design

This study is a prospective cohort study at the obstetrics-gynecology and anatomical pathology department of Cipto Mangunkusumo Hospital, Tarakan Hospital, Dharmais Hospital, and Fatmawati Hospital from February 2018 until February 2022.

Participants

The research subjects were patients with ovarian carcinoma inclusion, stage II-IV ovarian cancer patients, and were willing to participate in the study. The sample exclusion criteria were pregnant patients and patients diagnosed with other types of cancer. The number of samples in this study was 32 people in each group with consecutive sampling methods.

Data Collection

Every patient suspected of ovarian cancer will undergo cytoreductive debulking and histopathological examination. If the pathology result is malignant, chemotherapy will be given for six series followed by six months observation. We determine therapy response with the RECIST Criteria (Response Criteria in Solid Tumors) then classify it into chemo-resistant or chemo-sensitive groups. The patient will perform Flow cytometry blood tests to examine the expression of RAD6 and DDB2 and CSC (CD44⁺/CD24⁻). In addition, demographic data, cancer stage, operation type, chemotherapy response, tumor cell differentiation (cancer stage), cancer histopathology, cancer size, cancer residue, ascites, lymph node metastasize, and serum Ca-125 levels were also taken. The staging of the disease was carried out using the FIGO criteria.

Flow cytometry

Blood is taken from peripheral blood at five cc and then centrifugated. The supernatant was discarded, and 50 µL was left, after which the cell mixture was resuspended. Their markers identified expression CD44⁺/CD24⁻, RAD6, and DDB2. Samples were reacted with fluorescent-labeled antibody against CD44⁺/CD24⁻ (monoclonal anti-human) CD44⁺ labeled as PerCP, CD24⁻ labeled as APC, RAD6 labeled as PE, and DDB2 labeled FITC. The four reagents were removed for leukocytes with CD45 labeled pacific blue. The samples in the Falcon tube were added with 2,5 µL of CD44 marker, 2,5 µL of CD24 marker, and 2,5 µL of RAD6 and DDB2, then incubated for 15 minutes in the dark at room temperature. After incubation, cells were lysed using 300 µL of lysing solution, then set again for 15 minutes in a dark room and at room temperature. Next, 1 mL of facs flow solution was added and centrifuged at 500 g for 5 minutes. The supernatant formed was discarded, then added with 500 µL perm wash buffer and centrifuged at 500 g for 5 minutes; the supernatant created was discarded. To be more optimal, 1mL perm wash buffer was added again and centrifuged at 500 g for 5 minutes. The last step was to add 200 µL of 1% paraformaldehyde in phosphate-buffered saline (PBS). After that, the analysis was carried out using a flow cytometer using four fluorochrome colors.

Cell identification was carried out using an automated flow cytometer (BD Facs Calibur). CSCs were identified through the positive expression of CD44⁺/CD24⁻ markers while the RAD6 and DDB2 through a positive expression of RAD6 and DDB2 markers with four different colors. Protein percentage is the percentage of expression of protein markers CSCs (CD44⁺/CD24⁻), RAD6, and DDB2 in the blood.

Statistical Analysis

We analyze univariate (descriptive data), followed by bivariate and multivariate analysis. Each categorical variable was tested with the chi-square or Fisher test while the numerical variables were tested with an unpaired t-test or Mann-Whitney test if the data were not normally distributed. Multivariate analysis was also performed using logistic regression. Research ethics approval was obtained from the Health Research Ethics Committee of the Universitas Indonesia, Cipto Mangunkusumo Hospital.

Basic Participants Characteristics

The total sample in this study was 32 samples in each group. All samples had undergone chemotherapy with 32 (50%) chemoresistance and 32 (50%) chemosensitive. The distribution of profiles and clinical characteristics of ovarian cancer patients can be seen in Table 1.

Table 1

Essential Clinical Characteristics of Ovarian Cancer Patient
Variable	Number (%)
• Chemoresistant • Chemosensitive	32 (50) 32 (50)
Age (years old) • < 40 • 40–50 • > 50	4 (6,3) 19 (29,7) 41 (64,1)
Ca-125 • ≤35 • > 35	30 (46,9) 34 (53,1)
Ovarian cancer stage • Early stage: II • Advance stage: III - IV	5 (7,8) 59 (92,2)
Operation type: • Optimal Debulking • Suboptimal Debulking	56 (87,5) 8 (12,5)
Differentiation/cancer grade • Good • Intermediate • Poor	13 (20,3) 16 (25,0) 35 (53,1)
Tumor histology type • Serous • High-grade serous • Mucinous • Endometrioid • Clear cell • Others	24 (37,5) 14 (21,9) 3 (4,7) 12 (18,8) 10 (15,6) 1 (1,6)
Lymph nodes metastasize • Positive • Negative	32 (50) 32 (50)
Ascites • Positive • Negative	36 (56,3) 28 (43,7)
Tumor size • 5 cm • 5–10 cm • > 10 cm	17 (26,6) 15 (23,4) 32 (50)
Tumor residue • < 1cm • > 1cm	56 (87,5) 8 (12,5)

Flow cytometry of Ovarian cancer

Figure 2 and 3. shows the results of flow cytometry. The proportion of CD44⁺/CD24⁻, RAD6, and DDB2 values were calculated based on the percentage of the total cells.

Bivariate Analysis

Table 2 shows that CD44⁺/CD24⁻, RAD6, DDB2, Ca-125, type of surgery, lymph node metastasize, tumor size and tumor residue have significant difference results (p < 0,05) with each odds ratio (OR) and relative risk (RR) values

Table 2

Bivariate Analysis of The Variables in Ovarian Cancer Patients.
Variable	Therapy Response		P value	OR	RR
Variable	Chemoresistant (%)	Chemosensitive (%)	P value	OR	RR
CD44⁺/CD24⁻ expression • High (≥32.692) • Low (< 32.692)	25 (78,1) 7 (21,9)	8 (25) 24 (75)	0,001*	10,7	18,0
RAD6 expression • High (≥5.846.136) • Low (< 5.846.136)	15 (46,9) 17 (53,1)	5 (15,6) 27 (84,4)	0,007*	4,76	7,27
DDB2 Expression • High (≥7.370.316) • Low (< 7.370.316)	20 (62,5) 12 (37,5)	12 (37,5) 20 (62,5)	0,046*	2,78	4,0
Ca-125 Level • ≤35 • > 35	2 (6,25) 30 (93,75)	28 (87.5) 4 (12,5)	0,001*	105	42,4
Ovarian cancer stage • Early stage: II • Advance stage: III - IV	1 (3,13) 31 (96,87)	4 (12,5) 28 (87,5)	0,162	4,42	1,95
Surgery type • Optimal Debulking • Suboptimal Debulking	25 (84,4) 7 (15,6)	31 (96,87) 1 (3,13)	0,023*	8,68	5,14
Differentiation/cancer grade • Good • Intermediate - Poor	6 (18,75) 26 (81,25)	7 (21,88) 25 (78,12)	0,760	0,97	1,2
Lymph nodes metastasize • Positive • Negative	21 (65,63) 11 (34,37)	11 (34,37) 21 (65,63)	0,012*	3,65	6,25
Ascites • Positive • Negative	18 (56,25) 14 (43,75)	14 (43,75) 18 (56,25)	1,000	1	0,0
Tumor size • 5 cm • 5–10 cm • > 10 cm	0 (0) 32 (100)	17 (53,13) 15 (46,87)	0,001*	3,1	23,1
Tumor residue • < 1cm • > 1cm	25 (84,4) 7 (15,6)	31 (96,87) 1 (3,13)	0,023*	8,6	5,1
Note: *: p < 0,05, Significant results.

ROC and AUC Curves

ROC curve in Fig. 1 and Table 3 data showed that the CD44⁺/CD24⁻ protein has the best ROC curve and AUC value. The AUC value of the CD44⁺/CD24⁻ is 0,783, which means it has a moderate level of accuracy, but the value is significant (p < 0,05). The sensitivity of this protein is 78%, and its specificity is 75% for detecting chemoresistance. RAD6 protein had an AUC of 0,586 (very weak accuracy), not significant (p > 0,05), with a sensitivity of 84% and specificity of 46%. DDB2 had an AUC value of 0,61 (weak accuracy), not significant (p > 0,05), sensitivity 37,5%, and specificity of 37,5%.

Table 3

AUC analysis of *cancer stem cells* (CD44⁺/CD24⁻), RAD6, and DDB2 variables
Variable	AUC	SD	95% CI	Sensitivity (%)	Specificity (%)	Cutoff value	P value
CD44⁺/ CD24⁻	0,783	0,60	0,66 − 0,89	78	75	32.692	0,001*
RAD6	0,586	0,74	0,44 − 0,73	84	46	5.846.136	0,237
DDB2	0,611	0,74	0,46 − 0,75	37.5	37.5	7.370.316	0,126
Note : *: p < 0,05, significant

Multivariate Analysis

In logistic regression, Table 4 found the Hosmer & Lemeshow test showed a p-value of 0,936 (p > 0.05), which means the regression model is fit or appropriate. We found the variables used as regression models are the CD44⁺/CD24⁻ and Ca-125. The following are the regression model:

Table 4

Logistic Regression Result
Variable	p value	Omnibus test p value	Hosmer & Lemeshow test p value	Result
CD44⁺/CD24⁻	0,001	0,00	0,936	Modeled
RAD6	0,007			Eliminated
DDB2	0,046			Eliminated
Ca-125	0,001			Modeled
Operation type	0,023			Eliminated
Lymph nodes metastasize	0,012			Eliminated
Cancer size	0,001			Eliminated
Cancer residue	0,023			Eliminated

$$\text{log}\frac{p}{1-p}=\beta 0+ \beta 1.x1+ \beta 2.x2\dots$$

$$\text{log}\frac{p}{1-p}=-5,991+(\left(-1,958\right).\left(CD44CD24\right))+(\left(4,418\right).\left(Ca125\right))$$

The results of Table 5 and Table 6 show that CD44⁺/CD24⁻ and Ca-125 has p value < 0,05, which means that they both have a significant effect on ovarian chemoresistancs. The value of Cox & Snell R Square is 0,571 and Nagelkerke's R Square is 0,762. This study’s maximum − 2 Log-Likelihood Estimator (MLE) value is 54,183.

Table 5

Logistic regression of CD44⁺/CD24⁻ and Ca-125
No	Variables	Beta value ($\beta$)	Standard deviation	Wald	p value	95% CI
X1	CD44⁺/ CD24⁻	-1,958	0,928	4,449	0,035	0,023 − 0,871
X2	Ca-125	4,418	0,969	20,780	0,000	12,4-553,9
Constant		-5,991 ($\beta 0$)	1,618	13,712	0,000	-

Table 6

Logistic Regression Model Characteristics of CD44⁺/CD24⁻ and Ca-125
No	Variable	-2 log Likeli-hood (initial)	-2 log Likeli-hood (final)	MLE	Cox & Snell R Square	Nagelkerke R square
X1	CD44⁺/CD24⁻	88,723	34,540	54,183	0,571	0,762
X2	Ca-125	88,723	34,540	54,183	0,571	0,762
Note: MLE = Maximum − 2 Log Likelihood Estimator

This study showed that there is overexpression of CD44⁺/CD24⁻ and RAD6 while there is lower expression of DDB2 in chemoresistance ovarian cancer patients. CD44⁺ (cluster of differentiation 44) and CD24⁻ expression is associated with increased ovarian cancer oncogenesis and progression (5, 12). CD44⁺ overexpression has also been found in pancreatic cancer (13), breast cancer (14), gastric cancer (15), urothelial bladder cancer(16) and colorectal cancer (17). It is associated with metastasize, recurrence, chemoresistance, and poor survival rates in the ovarian cancer (18). CD24⁻ is a cell surface adhesion molecule frequently detected in invasive ovarian carcinoma. High CD24⁻ expression in invasive ovarian cancer predicts shorter overall survival than low CD24⁻ markers (19).

CD44⁺/CD24⁻ is a good predictor of ovarian cancer chemoresistance. We found that higher CD44⁺/CD24⁻ has a significant result (p < 0,05) with moderate accuracy (AUC 0,7 − 0,8) with a sensitivity of 78% and specificity of 75%. Meng et al., (2012) found that ovarian cancer cells with high CD44⁺/CD24⁻ expression have stem cells characteristics: higher aggressive, invasive, progressive, and multiplicative properties in each tumor histology type. This ovarian cancer cell also has higher chemoresistance properties, recurrence rate, and aggravating prognosis (5).

Li et al., (2017) found that high CD44⁺/CD24⁻ protein levels indicated breast tumor malignancy higher rates of cell proliferation, tumorigenesis, and metastasize. Breast cancer with CSC has resistance to chemotherapy and radiotherapy (20). Yan et al., (2013) also found that cells with high CD44⁺/CD24⁻ expression showed higher migration and invasion properties and were the cause of chemoresistance (21).

There was higher CD44⁺ expression in paclitaxel-resistant cell lines than paclitaxel-sensitive cell lines in a mouse model using ovarian cancer xenografts while patients with ovarian cancer showed that CD24⁻ cells were more resistant to cisplatin and increased tumorigenesis ability (22). A meta-analysis study showed that CD44⁺ protein was associated with poorer cancer-specific survival rates in patients undergoing chemoradiotherapy ⁽²³⁾. The study by Zhang et al., (2019) found that high expression of markers CD105, CD44⁺, and CD106 related to chemoresistance, poorly differentiated, and advanced-stage ovarian cancer (18).

RAD6 has a significant role in activating several DNA repair pathways and is substantial in chemoresistance in ovarian cancer (24). RAD6 overexpression is associated with mitotic abnormalities and tumor progression (25). We found that there was a significant increase in RAD6 levels (p < 0,05) in chemoresistance patients. However, ROC and AUC results were not significant (p > 0,05), and the accuracy was very weak (AUC 0,5 − 0,6), sensitivity 84%, specificity 46%.

Clark et al., (2018) investigated the role of RAD6 in chemoresistant ovarian cancer by inhibiting RAD6A and RAD6B in several ovarian cancer. These cells showed decreased expression of CSC markers, activation of DDR protein, and concomitant sensitivity to carboplatin responses suggesting that RAD6 expression increases after chemotherapy and causes chemoresistance in cancer cells through stimulating CSC protein expression and increasing DNA repair activity (25). The study by Somasagara et al., (2016) found association between chemoresistance and increased RAD6 in ovarian cancer cells through RAD6-mediated ubiquitin signaling, which led to increased DDR and CSC protein expression. In addition, a higher RAD6 (⩾5,1) was also associated with a disease recurrence rate of 70%. (26) Another study concluded that RAD6 related to the severity of ovarian cancer, breast cancer, and melanoma. Rad6 levels were significantly increased in severe ovarian cancer with platinum chemoresistance (27).

RAD6 overexpression can increase stem cell characteristics, making them more aggressive, metastasize and relapse. The epigenetic influence of RAD6 causes the ubiquitination of some histone variants which then regulates genes related to DNA repair, cell resistance, and chemoresistance (27). RAD6 is also closely related to RAD18, a protein E3 ubiquitin ligase that regulates the DNA repair pathway in Fanconi anemia and the BRCA gene in breast cancer (26) R.AD6 was involved in breast cancer chemoresistance in which researchers inhibited RAD6 with a small molecule inhibitor and found an increased sensitivity to cisplatin (28). In bladder cancer, it was also found that overexpression of enzymes from the UBE2 group, one of which was RAD6, could affect the growth of bladder cancer cells. An experiment was carried out by stopping the expression of UBE2, then the cells would stop growing in the G2/M phase and increase the apoptosis of these cancer cells (29).

DDB2 is a protein localized in the cell nucleus that contribute to gene transcription, cell cycle progression, and protein degradation (30). The decrease in DDB2 also affects ovarian cancer’s aggression, metastasize, and severity (8). This study found that DDB2 protein expression was found significantly lower in chemotherapy-resistant patients (p < 0,05). ROC and AUC analysis results are p > 0,05, weak accuracy (AUC 0.6–0.7), sensitivity 37,5%, specificity 37,5%.

DDB2 levels are high in the chemo-sensitive cancer patient group because DDB2 participates in the tumor suppression process in at least three ways, namely: promoting the nucleotide excision repair (NER) process, supporting apoptosis, and inducing cell aging after DNA damage has occurred.(31) The loss of DDB2 function in normal cells can lead to susceptibility to tumor growth. DDB2 gene mutation causes loss of function and gives rise to the phenotypic characteristics of Xeroderma Pigmentosum group E, which is characterized by malignant skin tumors. Mice with low DDB2 levels are not only hypersensitive to UV-related carcinogenesis processes but also have a high incidence of broad-spectrum spontaneous malignant tumors from internal organs. Thus, DDB2 acts as a mediator in the suppression of the p53 and BRCA1 pathways, thereby being a tumor cell suppressor, protecting against cancer by regulating the cell cycle and increasing the occurrence of apoptosis as opposed to being directly involved in the repair of DNA damage.(32)

Barakat et al. investigated the expression of DDB2 in several cisplatin-resistant ovarian cancer cell lines, and the results obtained were lower DDB2 expression than cisplatin-sensitive cells (33). The study also further explained that low DDB2 expression is associated with chemoresistance poor patient prognosis (9). DDB2 can reduce excess CSC protein (CD44⁺/CD24⁻) in large ovarian cancer. Overexpression of DDB2 in human ovarian cancer cells decreased the ability of these tumor cells to replicate (9). Han et al., (2014) stated that low DDB2 protein expression was associated with poor prognosis ovarian cancer patients. DDB2 deficiency causes ovarian tumors to relapse due to the expansion of the CSCs population (34).

Yang et al., (2018) found that DDB2 protein expression was associated with the onset, progression, and prognosis of colorectal cancer. Increased DDB2 expression was significantly associated with a better colorectal cancer prognosis (35). DDB2 was found to be able to inhibit colon cancer metastasizing through the mechanism of decreasing gene expression, which is an activator of Epithelial-to-Mesenchymal Transition (EMT) in colon cancer, and NF-κB found in breast cancer (9). Decreased DDB2 also affects EMT activation, triggering squamous cell carcinomas in the head and neck region (10). A Study on non-small cell lung cancer (NSCLC) found that DDB2 can facilitate the process of breaking the cancer cell growth. Conversely, if DDB2 decreases, the G2 cycle will continue, tumor growth and therapy resistance will occur (36).

This study applies multivariate analysis. Multivariate analysis with logistic regression found that two variables with almost the same effect and can be used as logistic regression models are the CD44⁺/CD24⁻ and Ca-125 variables. There have been no internationally published studies describing the CD44⁺/CD24⁻ and Ca-125 regression models. Furthermore, it may be necessary to carry out further research to confirm this regression model.

In this study, some shortcomings made us unable to prove that RAD6 and DDB2 can predict ovarian cancer chemoresistance. Although the results showed a significant difference (p < 0,05), the accuracy was still weak. It cannot yet be used as a good predictor of ovarian cancer chemoresistance and need further research.

We conclude that there is a significant relationship between increased levels of CD44⁺/CD24^−, RAD6, and reduced DDB2 protein (p < 0,05) with chemoresistance of ovarian cancer. Logistic regression results indicate that CD44⁺/CD24⁻ is significantly associated with ovarian cancer chemoresistance and can be used as a good predictor of ovarian cancer chemoresistance whereas RAD6 and DDB2 did not yet have similar results.

General: We would thank to Cipto Mangunkusumo, Tarakan Hospital, Dharmais hospital and Fatmawati Hospital for their support in this research. We would like to acknowledge Christine Pamphila, M.D., Rasendah, M.D., Kristabella Gianina, M.D., for their contribution to data analysis and data collection.

Ethics approval: Research ethics approval was obtained from the Health Research Ethics Committee of the Universitas Indonesia, Cipto Mangunkusumo Hospital. All patients have consented to participate.

Consent for publications: We have no individual person’s data in the manuscript. All authors have consented to publication.

Availability of data and materials: Data available on request from the authors

Funding statements: The funding of the research was from the authors

Conflict of interest: No conflict of interest.

Authors’ contributions:

Unedo H. M. Sihombing: Conceptualization, Data Curation, Funding acquisition, Investigation, Formal analysis, Resources, Supervision, Validation,
Andrijono: Conceptualization, Investigation, Resources, Supervision, Validation,
Gatot Purwoto: Conceptualization, Investigation, Resources, Supervision, Validation,
Supriadi Gandamihardja: Conceptualization, Investigation, Resources, Supervision, Validation,
Alida R. Harahap: Conceptualization, Investigation, Resources, Supervision, Validation,
Primariadewi Rustamadji: Conceptualization, Investigation, Resources, Supervision, Validation,
Aria Kekalih: Conceptualization, Formal analysis, Methodology, Software,
Dzicky Rifqi Fuady: Data curation, Methodology, Visualization, Writing-original draft, Writing-review & editing

Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68(6):394–424.
Brett M R, Brett M R, Jennifer B P, Thomas A S, Jennifer B P, Thomas A S. Epidemiology of ovarian cancer: a review. Cancer Biology & Medicine. 2017;14(1):9–32.
Vergote I, Trope CG, Amant F, Kristensen GB, Ehlen T, Johnson N, et al. Neoadjuvant chemotherapy or primary surgery in stage IIIC or IV ovarian cancer. N Engl J Med. 2010;363(10):943–53.
du Bois A, Baert T, Vergote I. Role of Neoadjuvant Chemotherapy in Advanced Epithelial Ovarian Cancer. J Clin Oncol. 2019;37(27):2398–405.
Meng E, Long B, Sullivan P, McClellan S, Finan MA, Reed E, et al. CD44+/CD24- ovarian cancer cells demonstrate cancer stem cell properties and correlate to survival. Clin Exp Metastasis. 2012;29(8):939–48.
Zhenhua Hu JG, Danye Zhang, Qing Liu, Limei Yan, Lili Gao, Juanjuan Liu, Dawo Liu, Shulan Zhang, Bei Lin. High Expression of Lewis y Antigen and CD44 Is Correlated with Resistance to Chemotherapy in Epithelial Ovarian Cancers. PloS one. 2013;8(2).
Abad E, Graifer D, Lyakhovich A. DNA damage response and resistance of cancer stem cells. Cancer Lett. 2020;474:106–17.
Somasagara RR, Tripathi K, Spencer SM, Clark DW, Barnett R, Bachaboina L, et al. Rad6 upregulation promotes stem cell-like characteristics and platinum resistance in ovarian cancer. Biochem Biophys Res Commun. 2016;469(3):449–55.
Han C, Zhao R, Liu X, Srivastava A, Gong L, Mao H, et al. DDB2 suppresses tumorigenicity by limiting the cancer stem cell population in ovarian cancer. Molecular Cancer Research. 2014;12(5):784–94.
Bommi PV, Ravindran S, Raychaudhuri P, Bagchi S. DDB2 regulates Epithelial-to-Mesenchymal Transition (EMT) in Oral/Head and Neck Squamous Cell Carcinoma. Oncotarget. 2018;9(78):34708–18.
Alberts B JA, lewis J, Morgan D, Raff M, Robert K, Walter P. Molecular Biology of the Cell (6 edition) 6 th ed: Garland Science; 2008.
Yan Y, Zuo X, Wei D. Concise review: emerging role of CD44 in cancer stem cells: a promising biomarker and therapeutic target. Stem cells translational medicine. 2015;4(9):1033–43.
Li L, Hao X, Qin J, Tang W, He F, Smith A, et al. Antibody against CD44s inhibits pancreatic tumor initiation and postradiation recurrence in mice. Gastroenterology. 2014;146(4):1108–18. e12.
Hu J, Li G, Zhang P, Zhuang X, Hu G. A CD44v + subpopulation of breast cancer stem-like cells with enhanced lung metastasis capacity. Cell death & disease. 2017;8(3):e2679-e.
Wang W, Dong L-P, Zhang N, Zhao C-H. Role of cancer stem cell marker CD44 in gastric cancer: a meta-analysis. International journal of clinical and experimental medicine. 2014;7(12):5059.
Hofner T, Macher-Goeppinger S, Klein C, Schillert A, Eisen C, Wagner S, et al., editors. Expression and prognostic significance of cancer stem cell markers CD24 and CD44 in urothelial bladder cancer xenografts and patients undergoing radical cystectomy. Urologic Oncology: Seminars and Original Investigations; 2014: Elsevier.
Wang Z, Tang Y, Xie L, Huang A, Xue C, Gu Z, et al. The prognostic and clinical value of CD44 in colorectal cancer: a meta-analysis. Frontiers in oncology. 2019;9:309.
Zhang J, Yuan B, Zhang H, Li H. Human epithelial ovarian cancer cells expressing CD105, CD44 and CD106 surface markers exhibit increased invasive capacity and drug resistance. Oncology letters. 2019;17(6):5351–60.
Li S-S, Ma J, Wong AS. Chemoresistance in ovarian cancer: exploiting cancer stem cell metabolism. Journal of gynecologic oncology. 2018;29(2).
Li W, Ma H, Zhang J, Zhu L, Wang C, Yang Y. Unraveling the roles of CD44/CD24 and ALDH1 as cancer stem cell markers in tumorigenesis and metastasis. Scientific reports. 2017;7(1):1–15.
Yan W, Chen Y, Yao Y, Zhang H, Wang T. Increased invasion and tumorigenicity capacity of CD44+/CD24-breast cancer MCF7 cells in vitro and in nude mice. Cancer cell international. 2013;13(1):1–7.
Gao M, Choi Y, Kang S, Youn J, Cho N. CD24 + cells from hierarchically organized ovarian cancer are enriched in cancer stem cells. Oncogene. 2010;29(18):2672–80.
Han S, Huang T, Li W, Wang X, Wu X, Liu S, et al. Prognostic value of CD44 and its isoforms in advanced cancer: a systematic meta-analysis with trial sequential analysis. Frontiers in oncology. 2019;9:39.
Spencer SM, Somasagara RR, Tripathi K, Bachaboina L, Scalici JM, Rocconi RP, et al. Rad6 inhibition sensitizes ovarian cancer cells to platinum drugs by attenuating activation of multiple DNA repair networks. Gynecologic Oncology. 2016;141:67–8.
Clark DW, Mani C, Palle K. RAD6 promotes chemoresistance in ovarian cancer. Molecular & Cellular Oncology. 2018;5(1):e1392403.
Somasagara RR, Spencer SM, Tripathi K, Clark DW, Mani C, Madeira da Silva L, et al. RAD6 promotes DNA repair and stem cell signaling in ovarian cancer and is a promising therapeutic target to prevent and treat acquired chemoresistance. Oncogene. 2017;36(48):6680–90.
Omy TR, Jonnalagadda S, Reedy M, Palle K. RAD6-mediated epigenetic reprogramming contributes to therapy-induced chemo-resistance in ovarian cancer. AACR; 2021.
Haynes B, Gajan A, Nangia-Makker P, Shekhar MP. RAD6B is a major mediator of triple negative breast cancer cisplatin resistance: Regulation of translesion synthesis/Fanconi anemia crosstalk and BRCA1 independence. Biochimica et Biophysica Acta (BBA)-Molecular Basis of Disease. 2020;1866(1):165561.
Gong YQ, Peng D, Ning XH, Yang XY, Li XS, Zhou LQ, et al. UBE2T silencing suppresses proliferation and induces cell cycle arrest and apoptosis in bladder cancer cells. Oncology letters. 2016;12(6):4485–92.
Gilson P, Drouot G, Witz A, Merlin J-L, Becuwe P, Harlé A. Emerging roles of DDB2 in cancer. International Journal of Molecular Sciences. 2019;20(20):5168.
Stoyanova T, Roy N, Bhattacharjee S, Kopanja D, Valli T, Bagchi S, et al. p21 cooperates with DDB2 protein in suppression of ultraviolet ray-induced skin malignancies. Journal of Biological Chemistry. 2012;287(5):3019–28.
Kattan Z, Marchal S, Brunner E, Ramacci C, Leroux A, Merlin JL, et al. Damaged DNA binding protein 2 plays a role in breast cancer cell growth. PloS one. 2008;3(4):e2002.
Barakat BM, Wang QE, Han C, Milum K, Yin DT, Zhao Q, et al. Overexpression of DDB2 enhances the sensitivity of human ovarian cancer cells to cisplatin by augmenting cellular apoptosis. International journal of cancer. 2010;127(4):977–88.
Han C, Zhao R, Liu X, Srivastava AK, Gong L, Mao H, et al. Loss of DDB2 enhances the tumorigenicity of ovarian cancer cells through expanding cancer stem-like cell population. AACR; 2014.
Yang H, Liu J, Jing J, Wang Z, Li Y, Gou K, et al. Expression of DDB2 Protein in the Initiation, Progression, and Prognosis of Colorectal Cancer. Digestive Diseases and Sciences. 2018;63(11):2959–68.
Zou N, Xie G, Cui T, Srivastava AK, Qu M, Yang L, et al. DDB2 increases radioresistance of NSCLC cells by enhancing DNA damage responses. Tumor Biology. 2016;37(10):14183–91.

No competing interests reported.

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Expression Of Rad6, Ddb2 And Cancer Stem Cell / Csc Protein (Cd44⁺/Cd24^-) On Therapy Response To The Administration Of Chemotherapy In Ovarian Cancer: A Prospective Flow Cytometry Study

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Abstract

Figures

Introduction

Materials And Methods

Study Design

Participants

Data Collection

Flow cytometry

Statistical Analysis

Results

Basic Participants Characteristics

Flow cytometry of Ovarian cancer

Bivariate Analysis

ROC and AUC Curves

Multivariate Analysis

Discussion

Conclusion

Declarations

References

Additional Declarations

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