DEXD/H box helicase 60 (DDX60) is a new type of DEAD-box RNA helicase, which is induced to express after virus infection. It might involve in antiviral immunity by promoting RIG-I-like receptor-mediated signal transduction. In addition, previous studies had shown that the expression of DDX60 is related to cancer, but there was still a lack of relevant research in breast cancer. In this study, we used the information of patients with breast cancer in the TCGA database for statistical analysis and found that the breast cancer patients with low expression of DDX60 exhibited radiosensitivity. Comparing the radiotherapy groups with the nonradiotherapy groups, for patients with low expression of DDX60, the adjusted hazard ratio (HR) values for radiotherapy were 0.244 (0.064–0.921) and 0.199 (0.062–0.646) in the training and validation datasets, with the
Breast cancer is one of the most common cancers in the world, accounting for a large proportion of cancer deaths in the world. The GLOBOCAN2018 showed that more than 2 million people were newly diagnosed with breast cancer in 2018, and nearly 627000 people died of breast cancer [
DEAD-box (DDX) protein, which is the largest family of RNA lyase, contains conserved amino acid Asp-Glu-Ala-Asp sequence and has 37 members in human beings. DDX protein could interact with rRNA, mRNA, and other RNAs to participate in DNA repair and proliferation, mRNA synthesis, RNA splicing, and modification. Simultaneously, it could also involve in translation initiation, ribosome and splice assembly, and cell cycle arrest and apoptosis [
We assumed that the expression levels of DDX60 might associate with radiosensitivity of patients. Radiosensitive patients could obtain better and safer survival status after radiotherapy. In order to verify our hypothesis, we analyzed the relationship between DDX60 and radiosensitivity of breast cancer based on TCGA, hoping to provide reference for the clinical treatment of breast cancer patients.
In this study, the data of gene expression and clinical information of breast cancer patients were derived from the TCGA database (
In this study, radiosensitivity was defined as the improved survival benefits of patients receiving radiotherapy. Then, the genes that could predict individual radiosensitivity were defined as radiosensitive genes. Their coding products could be used as potential biomarkers for radiosensitivity prediction. Since the expression distribution of DDX60 gene was skewness in training data (Figure
Expression distribution of DDX60 gene of patients with breast cancer. Expression distribution of DDX60 gene in all data. Expression distribution of DDX60 gene in training data. Expression distribution of DDX60 gene in validation data. (a) All patients. (b) Training patients. (c) Validation patients.
Then, univariate and multivariate Cox regression analyses were performed for patients with high and low expression. In this study, R software was used to make the survival curves in training and validation datasets. In addition, the logrank test and Cox regression analysis were used in our analysis.
Using the Cox proportional hazard model, we analyzed the relationship between 13 clinical factors and the overall survival of breast cancer patients, and listed the HR (95% CI) and
Associations of clinical indicators and DDX60 expression levels with total survival in training data.
Univariate analysis | Multivariate analysis | ||||
---|---|---|---|---|---|
HR (95% CI) | HR (95% CI) | ||||
Radiotherapy | |||||
Yes | 195 (55.71%) | 0.729 (0.372–1.426) | 0.356 | 0.663 (0.280–1.571) | 0.351 |
No | 155 (44.29%) | 1.000 | 1.000 | ||
Age | |||||
≥60 | 166 (47.43%) | 2.424 (1.227–4.789) | 0.011 | 2.404 (1.076–5.372) | 0.033 |
<60 | 179 (51.14%) | 1.000 | 1.000 | ||
NA | 5 (1.43%) | ||||
History of other malignancies | |||||
Yes | 14 (4.00%) | 2.768 (0.655–11.700) | 0.166 | 1.334 (0.255–6.966) | 0.732 |
No | 336 (96.00%) | 1.000 | 1.000 | ||
Histologic type | |||||
IDC | 232 (66.29%) | 0.830 (0.379–1.817) | 0.641 | 1.514 (0.569–4.033) | 0.406 |
MBC | 28 (8.00%) | 0.703 (0.185–2.671) | 0.604 | 1.971 (0.469–8.291) | 0.355 |
ILC | 83 (23.71%) | 1.000 | 1.000 | ||
NA | 7 (2.00%) | ||||
First surgical procedure | |||||
Lumpectomy | 83 (23.71%) | 0.387 (0.071–2.113) | 0.273 | 0.430 (0.069–2.666) | 0.364 |
Modified radical mastectomy | 120 (34.29%) | 2.442 (0.806–7.401) | 0.114 | 1.740 (0.452–6.697) | 0.421 |
Others | 64 (18.29%) | 1.565 (0.473–5.180) | 0.463 | 1.581 (0.425–5.887) | 0.495 |
A simple mastectomy | 63 (18.00%) | 1.000 | |||
NA | 20 (5.71%) | ||||
T stage | |||||
T3/T4 | 56 (16.00%) | 2.980 (1.448–6.133) | 0.003 | 1.328 (0.469–3.760) | 0.594 |
T1/T2 | 293 (83.71%) | 1.000 | 1.000 | ||
NA | 1 (0.29%) | ||||
N stage | |||||
N2/N3 | 61 (17.43%) | 4.091 (1.996–8.384) | <0.001 | 3.525 (1.134–10.956) | 0.030 |
N0/N1 | 286 (81.71%) | 1.000 | 1.000 | ||
NA | 3 (0.86%) | ||||
M stage | |||||
M1 | 8 (2.29%) | 4.233 (1.716–10.440) | 0.002 | 1.741 (0.401–7.557) | 0.459 |
M0 | 300 (85.71%) | 1.000 | 1.000 | ||
NA | 42 (12.00%) | ||||
ER status by IHC | |||||
Positive | 271 (77.43%) | 2.996 (1.018–8.814) | 0.046 | 1.870 (0.372–9.417) | 0.448 |
Negative | 64 (18.29%) | 1.000 | 1.000 | ||
NA | 15 (4.29%) | ||||
PR status by IHC | |||||
Positive | 234 (66.89%) | 2.029 (0.863–4.772) | 0.105 | 0.925 (0.260–3.290) | 0.905 |
Negative | 101 (28.86%) | 1.000 | 1.000 | ||
NA | 15 (4.29%) | ||||
HER2 status by IHC | |||||
Positive | 72 (20.57%) | 0.791 (0.296–2.118) | 0.641 | 0.856 (0.217–3.378) | 0.826 |
Overexpression | 51 (14.57%) | 1.043 (0.335–3.244) | 0.942 | 0.746 (0.151–3.682) | 0.724 |
Negative | 174 (49.71%) | 1.000 | 1.000 | ||
NA | 53 (15.14%) | ||||
Chemotherapy | |||||
Yes | 295 (84.29%) | 0.237 (0.100–0.565) | 0.001 | 0.214 (0.073–0.623) | 0.005 |
No | 55 (15.71%) | 1.000 | 1.000 | ||
DDX60 expression | |||||
High | 175 (50%) | 0.7003 (0.353–1.391) | 0.309 | 0.816 (0.379–1.757) | 0.604 |
Low | 175 (50%) | 1.000 | 1.000 |
Associations of clinical indicators and DDX60 expression levels with total survival in validation data.
Univariate analysis | Multivariate analysis | ||||
---|---|---|---|---|---|
HR (95% CI) | HR (95% CI) | ||||
Radiotherapy | |||||
Yes | 210 (60.00%) | 0.596 (0.320–1.108) | 0.102 | 0.515 (0.221–1.200) | 0.126 |
No | 140 (40.00%) | 1.000 | 1.000 | ||
Age | |||||
≥60 | 170 (48.57%) | 2.296 (1.207–4.369) | 0.011 | 2.593 (1.132–5.940) | 0.025 |
<60 | 180 (51.43%) | 1.000 | 1.000 | ||
History of other malignancies | |||||
Yes | 22 (6.29%) | 2.159 (0.760–6.131) | 0.149 | 2.724 (0.767–9.676) | 0.121 |
No | 327 (93.43%) | 1.000 | 1.000 | ||
NA | 1 (0.29%) | ||||
Histologic type | |||||
IDC | 229 (65.43%) | 0.861 (0.397–1.866) | 0.704 | 1.948 (0.732–5.184) | 0.182 |
MBC | 33 (9.43%) | 1.509 (0.561–4.061) | 0.416 | 3.344 (0.948–11.794) | 0.061 |
ILC | 82 (23.43%) | 1.000 | 1.000 | ||
NA | 6 (1.71%) | ||||
First surgical procedure | |||||
Lumpectomy | 82 (23.43%) | 1.570 (0.607–4.059) | 0.352 | 1.392 (0.429–4.514) | 0.582 |
Modified radical mastectomy | 112 (32.00%) | 1.159 (0.447–3.008) | 0.761 | 1.070 (0.329–3.484) | 0.911 |
Others | 56 (16.00%) | 0.498 (0.154–1.607) | 0.243 | 0.413 (0.094–1.821) | 0.248 |
A simple mastectomy | 76 (21.71%) | 1.000 | |||
NA | 24 (6.86%) | ||||
T stage | |||||
T3/T4 | 57 (16.29%) | 1.429 (0.694–2.942) | 0.333 | 1.402 (0.555–3.543) | 0.475 |
T1/T2 | 293 (83.71%) | 1.000 | 1.000 | ||
N stage | |||||
N2/N3 | 66 (18.86%) | 2.112 (0.982–4.539) | 0.056 | 3.440 (1.066–11.099) | 0.052 |
N0/N1 | 277 (79.14%) | 1.000 | 1.000 | ||
NA | 3 (2.00%) | ||||
M stage | |||||
M1 | 3 (0.86%) | 14.508 (3.343–62.960) | <0.001 | 2.975 (0.685–12.920) | 0.151 |
M0 | 297 (84.86%) | 1.000 | 1.000 | ||
NA | 50 (14.29%) | ||||
ER status by IHC | |||||
Positive | 266 (76.00%) | 0.726 (0.316–1.667) | 0.450 | 1.193 (0.375–3.793) | 0.765 |
Negative | 61 (17.43%) | 1.000 | 1.000 | ||
NA | 23 (6.57%) | ||||
PR status by IHC | |||||
Positive | 233 (66.57%) | 0.6274 (0.321–1.227) | 0.173 | 0.424 (0.146–1.232) | 0.120 |
Negative | 91 (26.00%) | 1.000 | 1.000 | ||
NA | 26 (7.43%) | ||||
HER2 status by IHC | |||||
Positive | 63 (18.00%) | 1.205 (0.468–3.107) | 0.699 | 1.354 (0.402–4.561) | 0.632 |
Overexpression | 40 (11.43%) | 1.281 (0.370–4.440) | 0.696 | 0.934 (0.143–6.090) | 0.944 |
Negative | 192 (54.96%) | 1.000 | 1.000 | ||
NA | 55 (15.71%) | ||||
Chemotherapy | |||||
Yes | 302 (86.29%) | 0.678 (0.265–1.738) | 0.419 | 0.517 (0.139–1.579) | 0.247 |
No | 47 (13.43%) | 1.000 | 1.000 | ||
NA | 1 (0.29%) | ||||
DDX60 expression | |||||
High | 178 (50.86%) | 0.590 (0.310–1.123) | 0.108 | 0.527 (0.251–1.105) | 0.090 |
Low | 172 (49.14%) | 1.000 | 1.000 |
The results showed that there was no significant correlation between the expression levels of DDX60 and the overall survival of breast cancer patients. Radiotherapy is an effective treatment for breast cancer, but our results showed that radiotherapy did not significantly increase overall survival. Therefore, we inferred that not all breast cancer patients could acquire positive prognostic outcomes through radiotherapy. And those patients who obtained the improved survival benefits after receiving radiotherapy were the radiosensitive patient groups that we mentioned earlier. By looking for radiosensitive groups, we could determine which groups were more suitable for radiotherapy and then adjust the strategy of radiotherapy to significantly improve their survival rate. Table
Relationship between expression levels of DDX60 and clinical indicators.
Training data ( | Validation data ( | |||||||
---|---|---|---|---|---|---|---|---|
High | Low | High | Low | |||||
Radiotherapy | 0.185 | 0.667 | 1.548 | 0.214 | ||||
Yes | 100 | 95 | 113 | 97 | ||||
No | 75 | 80 | 65 | 75 | ||||
Age | 0.561 | 0.454 | 0.175 | 0.675 | ||||
≥60 | 79 | 87 | 84 | 86 | ||||
<60 | 96 | 88 | 94 | 86 | ||||
History of other malignancies | 0.000 | 1.000 | 0.084 | 0.772 | ||||
Yes | 7 | 7 | 10 | 12 | ||||
No | 168 | 168 | 167 | 160 | ||||
Histologic type | 1.467 | 0.480 | 2.829 | 0.243 | ||||
IDC | 122 | 110 | 116 | 113 | ||||
MBC | 12 | 16 | 21 | 12 | ||||
ILC | 39 | 44 | 38 | 44 | ||||
First surgical procedure | 0.701 | 0.873 | 1.211 | 0.750 | ||||
Lumpectomy | 40 | 43 | 40 | 42 | ||||
Modified radical | ||||||||
Mastectomy | 58 | 62 | 59 | 53 | ||||
Others | 33 | 31 | 25 | 31 | ||||
A simple mastectomy | 34 | 29 | 40 | 36 | ||||
T stage | 1.091 | 0.296 | 0.186 | 0.666 | ||||
T3/T4 | 24 | 32 | 27 | 30 | ||||
T1/T2 | 151 | 142 | 151 | 142 | ||||
N stage | 0.675 | 0.411 | 0.156 | 0.693 | ||||
N2/N3 | 34 | 27 | 36 | 30 | ||||
N0/N1 | 140 | 146 | 141 | 136 | ||||
M stage | 0.070 | 0.791 | 0.002 | 0.963 | ||||
M1 | 5 | 3 | 1 | 2 | ||||
M0 | 154 | 146 | 153 | 144 | ||||
ER status by IHC | 0.197 | 0.657 | 0.867 | 0.352 | ||||
Positive | 133 | 138 | 138 | 128 | ||||
Negative | 34 | 30 | 27 | 34 | ||||
PR status by IHC | 0.041 | 0.840 | 3.496 | 0.062 | ||||
Positive | 118 | 116 | 126 | 107 | ||||
Negative | 49 | 52 | 38 | 53 | ||||
HER2 status by IHC | 2.748 | 0.253 | 2.706 | 0.259 | ||||
Positive | 42 | 30 | 38 | 25 | ||||
Overexpression | 24 | 27 | 20 | 20 | ||||
Negative | 82 | 92 | 93 | 99 | ||||
Chemotherapy | 4.228 | 0.040 | 0.537 | 0.464 | ||||
Yes | 155 | 140 | 156 | 146 | ||||
No | 20 | 35 | 21 | 26 |
In order to further evaluate which groups were more suitable for radiotherapy, subgroup analysis was performed. The data in both training and validation datasets were classified as two groups with low and high expression levels of DDX60 gene. The two groups, respectively, adopted the Cox proportional hazard model. Table
Association analysis of radiotherapy and survival under different expressions of DDX60.
Data | DDX60 expression | Unadjusted (RT vs NRT) | Adjusted (RT vs NRT) | ||
---|---|---|---|---|---|
HR (95% CI) | HR (95% CI) | ||||
Training | High ( | 4.266 (0.943–19.290) | 0.060 | 3.582 (0.627–20.467) | 0.054 |
Low ( | 0.312 (0.123–0.789) | 0.014 | 0.244 (0.064–0.921) | 0.040 | |
Validation | High ( | 1.848 (0.520–6.571) | 0.343 | 2.421 (0.460–12.773) | 0.297 |
Low ( | 0.314 (0.135–0.729) | 0.007 | 0.199 (0.062–0.646) | 0.007 | |
All data | High ( | 2.831 (1.074–7.461) | 0.035 | 1.687 (0.485–5.866) | 0.411 |
Low ( | 0.341 (0.186–0.626) | <0.001 | 0.395 (0.186–0.840) | 0.016 |
Figure
Survival curves under different expression levels of DDX60 in training and validation data. The logrank test was used to estimate the
Figure
Survival curves under different expression levels of DDX60 for all patients. The logrank test was used to estimate the
In addition, according to Tables
Figure
Associations between DDX60 expression levels and clinical assessment factors. The chi-square test was used for comparisons of rates of different groups. RT: radiotherapy; NRT: nonradiotherapy; HIGH: high expression of DDX60 gene; LOW: low expression of DDX60 gene.
Radiotherapy, as an indispensable treatment for breast cancer, could not only inhibit tumor growth but also decrease the mortality rate of patients. Under certain circumstances, radiotherapy could help some early-staged breast cancer patients and elderly patients with advanced breast cancer avoid operations [
Our research discovered that radiotherapy was associated with the overall survival of patients by dividing patients into different groups according to their expression levels of DDX60. To some extent, the patients with low expression levels of DDX60 possessed radiosensitivity. In addition, the relationship between other clinical indicators and overall survival was also analyzed. The results found that overall survival was related to the age of the patients and TNM stages. The more accurate understanding of the association between the above clinical indicators and radiosensitivity of breast cancer could further promote the development of precise and individualized radiotherapy for breast cancer patients.
In our research, by adopting median division, we further divided the training data and validation data into low- and high-expression groups. Nine cutoff values based on different quantiles were chosen to evaluate the effects of radiotherapy on survival (Figure
Among all the patients who had received radiotherapy, based on the TCGA data, we compared indicators including radiation dose, type, and site between low- and high-expression groups, and no significant difference was observed, indicating that patients in these groups received the same type of radiation therapy (Table
In general, radiotherapy for breast cancer could be categorized into preoperative treatment, postoperative treatment, and palliative treatment. Among them, preoperative treatment could recognize the location of tumor, predict a series of biomarkers, and classify the risks for adjuvant treatment to avoid the delay of the local treatment [
At present, although the relationship between DDX60 and radiotherapy for breast cancer remains unclear, the associations between DDX60 and antiviral immunity, colorectal cancer, and oral squamous cell carcinoma have been proved. Research showed that the helicase domain of purified DDX60 could bind viral RNA to DNA, to serve the purpose of antiviral immunity through promoting RIG-I-like receptor-mediated signaling [
In this study, we found that the low DDX60 expression group possessed radiosensitivity. Based on the mechanism of DDX60 interfering with breast cancer and other cancers, it was initially suspected that DDX60 might further influence resistance of tumor cells to radiation by influencing RNA synthesis, DNA repairing, and proliferation [
Herein, internal verification strategy was adopted, making up for the deficiency of small sample size to an extent. Additionally, under the circumstance that there were few studies concerning the relationship between DDX60 and radiosensitivity of breast cancer, our research has made some progress in the field of the relationship between gene and radiosensitivity, which might promote the individualized development of radiotherapy for breast cancer. However, the specific mechanism of how DDX60 interacts with radiosensitivity remains to be discovered. Meanwhile, a series of related external validation experiments need to be conducted, such as laboratory experiments and clinical experiments. Although our study had some limitations, the study still could offer some new directions for future research on both DDX60 and radiosensitivity of breast cancer.
The datasets used in the present study are available from The Cancer Genome Atlas database (
The funding body did not play any roles in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.
The authors declare that there are no conflicts of interest regarding the publication of this paper.
Study conception and design were carried out by Dongrun Xin, Jingfang Liu, Zaixiang Tang, and Hualong Qin. Real data were acquired and analysis was performed by Dongrun Xin, Jingfang Liu, and Jincheng Gu. Drafting of the manuscript was done by Dongrun Xin, Yujie Ji, Jiawei Jin, Lu Sun, Qingliang Tai, Jingfang Liu, Jincheng Gu, Jianping Cao, and Ye Tian. Dongrun Xin and Jingfang Liu contributed equally to this work.
The authors acknowledge the contributions of the TCGA Research Network. This work was supported in part by the National Natural Science Foundation of China (81773541 and 81573253) and funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions at Soochow University, the State Key Laboratory of Radiation Medicine and Protection (GZK1201919) to Zaixiang Tang, the National Natural Science Foundation of China (81872552, U1967220) to Jianping Cao, the Jiangsu Provincial Key Project in Research and Development of Advanced Clinical Technique (BL2018657) to Ye Tian, and the Undergraduate Training Program for Innovation and Entrepreneurship (2019xj062) to Dongrun Xin.
Table S1: comparisons of radiation dose, type, and site in patients under radiotherapy. Figure S1: survival curves under different expression levels of DDX60 for all patients. The total samples were stratified by age (≥60 and <60). The logrank test was employed to estimate