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. 2021 Sep 17;16(9):e0257445. doi: 10.1371/journal.pone.0257445

TRIM59: A potential diagnostic and prognostic biomarker in human tumors

Zheng Jin 1, Liping Liu 1, Youran Yu 2, Dong Li 1, Xun Zhu 1, Dongmei Yan 1,*,#, Zhenhua Zhu 3,*,#
Editor: Salvatore V Pizzo4
PMCID: PMC8448305  PMID: 34534244

Abstract

TRIM59 is a protein that is highly expressed in a variety of tumors and promotes tumor development. However, the use of TRIM59 as tumor diagnosis and prognosis biomarker has not been fully explored. We collected datasets from the cancer genome atlas (TCGA) and gene expression omnibus (GEO) to investigate its potential as a biomarker for diagnosis and prognosis. A total of 46 studies, including 11,558 patients were included in this study. Here, we showed that TRIM59 was significantly upregulated in 15 type of human solid tumors in comparison to their adjacent tissues. Receiver operating characteristic curve (ROC) results provided further evidence for the use of TRIM59 as a potential tumor diagnosis biomarker. Overall survival (OS) was compared between TRIM59 high expression and low expression groups. High expression of TRIM59 indicated a poor prognosis in multiple solid tumors. Taken together, these analyses showed that TRIM59 was upregulated in various types of tumors and had the potential to be used as a diagnostic and prognostic biomarker in human solid tumors.

Introduction

Cancer has always been a major global health concern and creates a heavy financial burden for patients and the health care system [1]. It is estimated that there might be 18.1 million new cancer cases and 9.6 million cancer deaths in 2018 [2]. Cancers are always characterized by a group of abnormal expressed genes, and these genes have the potential to be used as diagnosis markers or prognosis predictors [3, 4].

TRIM59 is a member of TRIM protein family, which is comprised of a RING domain, b-box domain, coiled helix domain, and c-terminal non-specific domain [5]. Recent studies have shown that TRIM59 played a role in epigenetic modification [6], embryonic development [7] and autophagy [8], and was upregulated in multiple tumors [9]. It has been found that down-regulation of TRIM59 significantly inhibited tumor growth in vitro experiments and animal models [10, 11]. However, no large-scale clinical studies have been performed to demonstrate the relationship between TRIM59 and tumor diagnosis and prognosis.

In this study, the cancer genome atlas (TCGA) and gene expression omnibus (GEO) datasets were used to analyze the expression of TRIM59 in tumors and evaluate the value of TRIM59 as a biomarker for tumor diagnosis and prognosis.

Materials and methods

Data collection and extraction

Datasets containing tumor samples and corresponding normal samples were downloaded from TCGA. Microarrays containing overall survival (OS) information were searched in the GEO database. Inclusion criteria were as follows: (1) should be human tumor samples and each dataset should contain at least 30 independent samples; (2) for the convenience of data processing, only microarray data performed on GPL570 platform was retained; (3) characteristics of studies and survival information were reported or could be determined. Exclusion criteria include: repetitive studies containing same datasets or patient cohorts. A total of 26 GEO datasets were included in the study, including 6 breast cancer datasets (GSE20685, GSE20711, GSE16446, GSE42568, GSE48390, GSE58812), 8 lung cancer datasets (GSE102287, GSE29013, GSE30219, GSE31210, GSE3141, GSE19188, GSE37745, GSE50081), 4 ovarian cancer datasets (GSE26193, GSE32062, GSE63885, GSE18520), 3 gastric cancer datasets (GSE15459, GSE57303, GSE62254), 1 pancreatic cancer dataset (GSE17891), 1 Ewing sarcoma dataset (GSE17679), 1 adrenocortical carcinoma dataset (GSE19750), 2 brain cancer dataset (GSE108474, GSE7696).

Data processing

Level 3 RNAseq and associated clinical information of each TCGA solid tumor project (LUAD-lung adenocarcinoma, BRCA-breast carcinoma, UCEC-uterine corpus endometrial carcinoma, LUSC-lung squamous cell carcinoma, HNSC-head and neck squamous cell carcinoma, KIRC-kidney renal clear cell carcinoma, PRAD-prostate adenocarcinoma, BLCA-bladder carcinoma, THCA-thyroid carcinoma, KIRP-kidney renal papillary cell carcinoma, LIHC-liver hepatocellular carcinoma, STAD-stomach adenocarcinoma, COAD-colon adenocarcinoma, READ-rectum adenocarcinoma, CHOL-cholangiocarcinoma, CESC-cervical squamous cell carcinoma and endocervical adenocarcinoma, ESCA-esophageal carcinoma, GBM-glioblastoma multiforme, LGG-lower grade glioma, OV-ovarian serous cystadenocarcinoma, PAAD-pancreatic adenocarcinoma, SARC-sarcoma, SKCM-skin cutaneous melanoma) was downloaded from UCSC Xena (https://xenabrowser.net/). Downloaded FPKM (Fragments Per Kilobase of exon model per Million mapped fragments) gene expression data was log2 transformed for the convenience of comparison. Samples’ expression and clinical information was matched, samples without complete survival or expression information were excluded. TRIM59 expression in each project was presented with boxplot. As for GEO datasets, RMA (Robust Multichip Average) normalized gene expression data of GPL570 platform was downloaded and matched with associated clinical information.

ROC, AUC and SROC analyses

R package “OptimalCutpoints” was first used to determine the optimal cutpoint in each TCGA project, at which the sample types (tumor or normal) could be best distinguished. Then the sample types were predicted by the TRIM59 expression level according to the cut point of each project using the “Youden” prediction model [12]. ROC and associated 95% confidence intervals (CIs) of each project was also calculated using the “optimal.cutpoints” function of package “OptimalCutpoints”. Module “midas” of Stata 15.0 (StataCorp LLC, USA) was used for the meta-analysis of ROC curves of all the projects. Sensitivity and specificity of the meta-analysis were evaluated, and the summary of ROCs (SROC) was calculated. Deeks’ funnel plot asymmetry test was used to investigate potential publication bias in SROC analysis.

Overall survival analysis

Datasets regarding TRIM59 expression and clinical characteristics of human cancers were downloaded from TCGA and GEO databases. According to the TRIM59 expression level in each data set, the samples were divided into high expression and low expression groups in comparison to the median expression level. The "survival" package was used to calculate the hazard ratio (HR) and 95% CIs for the high-expression TRIM59 group versus the low-expression TRIM59 group.

Statistical analysis

All the analyses were performed on R (Version 3.6.1) and Stata 15.0 (StataCorp LLC, USA). Normalized expression data were downloaded from TCGA or GEO datasets. Unpaired t test was used for comparison between groups. As for ROC analysis, R package “OptimalCutpoints” was first used to calculate the optimal cutpoint for tumor diagnosis, then ROC and 95% CIs were calculated for each project. Stata module “medias” was used for ROC meta-analysis. R package “survival” was used for survival analysis and package “meta” was used for survival meta-analysis.

Results

TRIM59 was highly expressed in human tumors

Previous studies have shown that TRIM59 was highly expressed in a variety of tumors and closely related to the occurrence and development of tumors. We analyzed 15 types of tumor datasets that included sufficient numbers of tumors and adjacent normal tissues in TCGA, confirming that TRIM59 was highly expressed in tumor samples in comparison to their adjacent tissues (Fig 1).

Fig 1. Relative expression of TRIM59 in human tumors based on TCGA database, comparisons were conducted using unpaired t test.

Fig 1

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****p < 0.0001. FPKM (Fragments Per Kilobase of exon model per Million mapped fragments).

High expression of TRIM59 showed high efficacy in the diagnosis of human tumors

To explore the value of high expression of TRIM59 in tumors, we tested if TRIM59 could be used to identify healthy and tumor samples. ROC analysis was performed based on the data obtained from TCGA. Results presented in Table 1 demonstrated that TRIM59 showed high diagnosis efficacy in multiple cancers, especially in CHOL, with a prediction accuracy up to 100%. The pooled sensitivity and specificity were 0.84 (95% CI:0.77–0.89) and 0.90 (95% CI:0.85–0.94) respectively (Fig 2). Additionally, the area under ROC (AUC) of the SROC was 0.94 (95% CI:0.91–0.96) (Fig 3A). In order to test the potential publication bias, Deeks’ funnel plot asymmetry test was performed and revealed that no significant substantial publication bias was found in SROC analysis (p = 0.75) (Fig 3B). These results suggested that the high expression of TRIM59 could be used as a diagnostic marker in different types of human cancers.

Table 1. Characteristics of studies and AUC analyses.

ID Projects AUC (95% CI) Normal Samples Tumor Samples Cut-off value FP FN TP TN
1 TCGA-LUAD 0.928 (0.903, 0.952) 59 526 0.768746 5 98 428 54
2 TCGA-BRCA 0.955 (0.94, 0.969) 113 1104 0.882547 8 139 965 105
3 TCGA-UCEC 0.787 (0.737, 0.838) 35 548 1.070402 3 195 353 32
4 TCGA-LUSC 0.985 (0.972, 0.998) 49 501 0.846782 1 20 481 48
5 TCGA-HNSC 0.876 (0.837, 0.915) 44 502 0.971722 3 137 365 41
6 TCGA-KIRC 0.912 (0.878, 0.946) 72 535 0.573303 13 58 477 59
7 TCGA-PRAD 0.712 (0.644, 0.779) 52 499 0.689123 10 235 264 42
8 TCGA-BLCA 0.905 (0.845, 0.965) 19 411 0.828988 2 60 351 17
9 TCGA-THCA 0.742 (0.676, 0.809) 58 510 0.690223 10 189 321 48
10 TCGA-KIRP 0.783 (0.706, 0.859) 32 289 0.390211 11 62 227 21
11 TCGA-LIHC 0.921 (0.89, 0.952) 50 374 0.104429 3 60 314 47
12 TCGA-STAD 0.951 (0.918, 0.984) 32 375 0.691021 6 26 349 26
13 TCGA-COAD 0.941 (0.919, 0.963) 41 471 0.943438 2 74 397 39
14 TCGA-READ 0.93 (0.889, 0.971) 10 167 0.91301 0 24 143 10
15 TCGA-CHOL 1 (1, 1) 9 36 0.287042 0 0 36 9
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LUAD, lung adenocarcinoma; BRCA, breast carcinoma; UCEC, uterine corpus endometrial carcinoma; LUSC, lung squamous cell carcinoma; HNSC, head and neck squamous cell carcinoma; KIRC, kidney renal clear cell carcinoma; PRAD, prostate adenocarcinoma; BLCA, bladder carcinoma; THCA, thyroid carcinoma; KIRP, kidney renal papillary cell carcinoma; LIHC, liver hepatocellular carcinoma; STAD, stomach adenocarcinoma; COAD, colon adenocarcinoma; READ, rectum adenocarcinoma; CHOL, cholangiocarcinoma.

Fig 2. Forest plot showing mean sensitivity and specificity with corresponding heterogeneity statistics for the prediction of sample types with the expression level of TRIM59.

Fig 2

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Fig 3. Summary ROC analysis.

Fig 3

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(A) Summary ROC for the evaluation of prediction efficacy of TRIM59, with confidence and prediction regions around mean operating sensitivity and specific point. The ID in the figure corresponds to the projects in Table 1. (B) Deeks’ funnel plot asymmetry test showing the potential publication bias. The ID in the figure corresponds to the projects in Table 1.

Expression of TRIM59 indicated prognosis in human tumors

To investigate the relationship between TRIM59 and tumor prognosis, OS meta-analysis was conducted. A total of 21 TCGA and 26 GEO datasets containing 11,558 patients were included (Table 2). High expression of TRIM59 indicated poor prognosis in KIRP (HR 3.18; 95%CI 1.76–5.75), LGG (HR 1.98; 95%CI 1.39–2.83), LUAD (HR 1.40; 95%CI 1.04–1.88), lung cancer (GSE30219: HR 1.44; 95%CI 1.09–1.89; GSE31210: HR 2.37; 95%CI 1.22–4.60), and indicated good prognosis in CESC (HR 0.56; 95%CI 0.34–0.92) and SKCM (HR 0.71; 95%CI 0.54–0.92) (Fig 4).

Table 2. Characteristics of studies in OS analysis.

Study Country Year Patients HR Lower Upper
TCGA (BLCA) USA 2014 402 0.86 0.64 1.16
TCGA (BRCA) USA 2014 1006 1.25 0.89 1.75
TCGA (CESC) USA 2014 264 0.56 0.34 0.92
TCGA (COAD) USA 2014 440 1.23 0.83 1.83
TCGA (ESCA) USA 2014 144 1.17 0.70 1.97
TCGA (GBM) USA 2014 152 1.07 0.75 1.53
TCGA (HNSC) USA 2014 496 1.26 0.97 1.66
TCGA (KIRC) USA 2014 522 1.28 0.95 1.73
TCGA (KIRP) USA 2014 284 3.18 1.76 5.75
TCGA (LGG) USA 2014 510 1.98 1.39 2.83
TCGA (LIHC) USA 2014 360 1.41 0.99 2.00
TCGA (LUAD) USA 2014 492 1.40 1.04 1.88
TCGA (LUSC) USA 2014 488 0.84 0.64 1.10
TCGA (OV) USA 2014 294 1.03 0.77 1.38
TCGA (PAAD) USA 2014 174 1.46 0.97 2.20
TCGA (READ) USA 2014 158 1.50 0.68 3.28
TCGA (SARC) USA 2014 258 0.93 0.63 1.38
TCGA (SKCM) USA 2014 458 0.71 0.54 0.92
TCGA (STAD) USA 2014 378 0.81 0.59 1.12
TCGA (UCEC) USA 2014 540 1.22 0.80 1.84
GSE20685 (Breast Cancer) China 2011 327 1.31 0.85 2.01
GSE20711 (Breast Cancer) Canada 2011 88 1.23 0.56 2.68
GSE16446 (Breast Cancer) Canada 2010 107 1.28 0.46 3.54
GSE42568 (Breast Cancer) Ireland 2013 104 1.02 0.53 1.99
GSE48390 (Breast Cancer) China 2014 81 0.86 0.26 2.79
GSE58812 (Breast Cancer) France 2015 107 0.77 0.37 1.60
GSE102287 (Lung Cancer) USA 2017 66 1.20 0.64 2.23
GSE29013 (Lung Cancer) USA 2011 55 1.31 0.52 3.30
GSE30219 (Lung Cancer) France 2013 293 1.44 1.09 1.89
GSE31210 (Lung Cancer) Japan 2011 226 2.37 1.22 4.60
GSE3141 (Lung Cancer) USA 2005 111 0.96 0.57 1.60
GSE19188 (Lung Cancer) Netherlands 2010 82 1.46 0.84 2.54
GSE37745 (Lung Cancer) Sweden 2012 196 1.14 0.82 1.58
GSE50081 (Lung Cancer) Canada 2013 181 1.21 0.77 1.90
GSE26193 (Ovarian cancer) France 2011 107 0.78 0.50 1.23
GSE32062 (Ovarian cancer) Japan 2012 260 1.03 0.72 1.48
GSE63885 (Ovarian Cancer) Poland 2014 75 0.85 0.52 1.37
GSE18520 (Ovarian adenocarcinomas) USA 2009 53 1.07 0.58 1.98
GSE15459 (Gastric Cancer) Switzerland 2009 192 0.89 0.59 1.33
GSE57303 (Gastric Cancer) China 2014 34 0.93 0.48 1.79
GSE62254 (Gastric Cancer) USA 2015 295 0.78 0.57 1.08
GSE17891 (Pancreatic Cancer) United Kingdom 2011 21 0.28 0.05 1.62
GSE17679 (Ewing Sarcoma) Finland 2011 88 1.56 0.90 2.70
GSE19750 (Adrenocortical carcinoma) USA 2013 22 0.39 0.15 1.05
GSE108474 (Brain Cancer) USA 2018 487 0.96 0.78 1.19
GSE7696 (Glioblastoma) Switzerland 2008 80 1.08 0.66 1.75
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BLCA, bladder carcinoma; BRCA, breast carcinoma; CESC, cervical squamous cell carcinoma and endocervical adenocarcinoma; COAD, colon adenocarcinoma; ESCA, esophageal carcinoma; GBM, glioblastoma multiforme; HNSC, head and neck squamous cell carcinoma; KIRC, kidney renal clear cell carcinoma; KIRP, kidney renal papillary cell carcinoma; LGG, lower grade glioma; LIHC, liver hepatocellular carcinoma; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; OV, ovarian serous cystadenocarcinoma; PAAD, pancreatic adenocarcinoma; READ, rectum adenocarcinoma; SARC, sarcoma; SKCM, skin cutaneous melanoma; STAD, stomach adenocarcinoma; UCEC, uterine corpus endometrial carcinoma.

Fig 4. Forest plot showing the overall survival significance of TRIM59.

Fig 4

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Patients were divided into high expression and low expression groups based on the median expression levels of TRIM59, then overall survival was compared between the high expression and low expression group.

Discussion

In this study, we used TCGA datasets to demonstrate that TRIM59 was upregulated in multiple cancers in comparison to the adjacent normal tissues. To explore the diagnosis efficacy of TRIM59, we performed ROC and SROC analyses to identify that high expression of TRIM59 showed a high diagnosis efficacy in each tumor type (AUC > 0.5), and with an AUC of summary ROC of 0.94 (0.91–0.96). We also combined GEO datasets and performed a meta-analysis to reveal that high expression of TRIM59 showed significant (p<0.05) poor prognosis in KIRP, LGG, LUAD, lung cancer and showed better prognosis in CESC and SKCM. Especially in LUAD, the expression of TRIM59 in tumors with better prognosis was still higher than that in adjacent tissues (S1 Fig).

TRIM59 is closely related to cancers. A previous study used Immunohistochemistry (IHC) to determine the expression of TRIM59 in 291 cases of 37 tumor types, and found that TRIM59 expression was upregulated in tumor samples, particularly in lung, breast, liver, skin, tongue and mouth (squamous cell cancer) and endometrial cancers [9]. In subsequent studies, it was shown that upregulation of TRIM59 can promote tumor growth in tumor cell lines and animal models, while downregulation had the opposite effect. These cancers include pancreatic cancer [13], cholangiocarcinoma [14], ovarian cancer [15, 16], lung cancer [17–19], breast cancer [10, 20, 21], euroblastoma [22], medulloblastoma [23], hepatocellular carcinoma [24], glioblastoma [11], colorectal cancer [25, 26], bladder cancer [21], prostate cancer [27], cervical cancer [28], osteosarcoma [29], gastric cancer [30]. However, results from these studies were limited by a small number of tumor samples, or the use of in vitro experimentation or animal models could not fully address the relationship between TRIM59 and human cancers. Thanks to the gene expression data and associated prognosis information provided in public database, we identified that TRIM59 was highly expressed in most solid tumors and could indicate the prognosis in several cancers.

This study is a meta-analysis of multiple solid tumors, indicated that TRIM59 has the potential to be used as a diagnostic molecule for a variety of tumors, and special attention should be paid to the abnormal high expression of TRIM59 in specific tissues. Moreover, it plays a prognostic role in specific tumors, especially in KIRP/LGG/LUAD/Lung cancer/CESC/SKCM. Detection of TRIM59 expression in these tumor tissues is helpful for us to evaluate the prognosis of patients.

The limitation of this study lies in the fact that it is only the data analysis of biological database, and further verification of TRIM59 expression level and follow-up information are needed in clinical samples for each specific tumor type. Basic research on TRIM59 is also needed to improve our understanding of tumors.

Conclusions

In this study, based on TCGA datasets, we revealed that TRIM59 was upregulated in 15 types of solid tumors. Additionally, TRIM59 have a high efficacy in diagnosis and prognosis prediction in various tumor types. TRIM59 have the potential to be used as diagnosis marker or prognosis predictor in tumors.

Supporting information

S1 Fig. Expression of TRIM59 in LUAD-low expression and KIRP-low expression groups and associated adjacent tissues.

(TIF)

Click here for additional data file. (131.9KB, tif)

Data Availability

The data that support the findings of this study were derived from the following resources available in the public domain: https://www.ncbi.nlm.nih.gov/geo (Accession numbers: GSE20685, GSE20711, GSE16446, GSE42568, GSE48390, GSE58812, GSE102287, GSE29013, GSE30219, GSE31210, GSE3141, GSE19188, GSE37745, GSE50081, GSE26193, GSE32062, GSE63885, GSE18520, GSE15459, GSE57303, GSE62254, GSE17891, GSE17679, GSE19750, GSE108474, GSE7696); https://xenabrowser.net (Projects: LUAD, BRCA, UCEC, LUSC, HNSC, KIRC, PRAD, BLCA, THCA, KIRP, LIHC, STAD, COAD, READ, CHOL, CESC, ESCA, GBM, LGG, OV, PAAD, SARC, SKCM)

Funding Statement

This work was supported by the National Natural Science Foundation of China (No. 81871245) and Department of Education of Jilin Province (JJKH20190095KJ).

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Decision Letter 0

Suhwan Chang

4 Mar 2021

PONE-D-20-32431

TRIM59: a potential diagnostic and prognostic biomarker in human tumors

PLOS ONE

Dear Dr. Zhenhua Zhu,

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Reviewer #1: This mansucript reports a meta-analysis of TRIM-59 expression in a number of large publicly-available cancer datasets, particularly TCGA and GEO. The results show that TRIM-59 expression is consistently higher in tumor tissue relative to its matching benign tissue in each study, however it is expressed in both tissue types. In addition, expression level was looked at as a prognostic marker and found to be higher in cases with worse outcome for a number of tumor types, but not all.

The strengths of the study are the large number of cases examined from reliable data sources. Also analyses were performed to evaluate for biases typical of these types of study designs.

The main weakness of the study is the claim that the data shows this to be a good diagnostic biomarker for cancer, however the study was not designed to evaluate for this being a diagnostic biomarker. IN fact, the data seem to indicate that this is a very poor diagnostic biomarker, as figure 1 shows many benign tissue types to have higher TRIM-59 levels than some tumor types. Also, there is an opportunity to look at great detail for certain tumor types but the opportunity was not taken.

Specific areas to consider:

1. figure 1, what does FPKM stand for? should indicate in the figure itself or in the figure caption.

2. All claims about this being a good diagnostic biomarker should be excluded, as none of this data shows such. In fact, the data actually could be interpreted that it shows the opposite at this point in time (some benign levels are higher than some cancer levels and the ROC is not good enough for diagnostic purposes). This study was not designed to determine if this is a diagnostic biomarker or not. From this data, ONe can conclude that TRIM-59 is worth examining as a diagnostic biomarker (and that can be stated here), but studies need to be properly designed to evaluate for this.

3. In realtion to point 2, how did the expression levels in benign compare to the good outcome cancers for each tumor type??? This was not included, but should be looked at to further see if this is still a good diagnostic biomarker when that comparison is done.

4. In the discussion, maybe more about the limitations of this study, including that such meta-analyses are evaluating large datasets of collections of many tumor types, and that clinical utility would need to be looked at for each different tumor type in relation to clinical needs rather than the entire collection as a whole.

**********

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PLoS One. 2021 Sep 17;16(9):e0257445. doi: 10.1371/journal.pone.0257445.r002

Author response to Decision Letter 0

22 Mar 2021

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

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Answer: We have modified the manuscript according to the style templates as required.

2. In the Methods section, please provide the accession numbers of the datasets downloaded from GEO for your study.

Answer: We have provided the accession numbers as required. (line 66-74)

3. To comply with PLOS ONE submission guidelines, in your Methods section, please provide additional information regarding your statistical analyses. For more information on PLOS ONE's expectations for statistical reporting, please see https://journals.plos.org/plosone/s/submission-guidelines.#loc-statistical-reporting.

Answer: We have added additional information regarding statistical analyses in the method section as required. (line 76-95)

[Note: HTML markup is below. Please do not edit.]

Review Comments to the Author

01

Reviewer #1: This mansucript reports a meta-analysis of TRIM-59 expression in a number of large publicly-available cancer datasets, particularly TCGA and GEO. The results show that TRIM-59 expression is consistently higher in tumor tissue relative to its matching benign tissue in each study, however it is expressed in both tissue types. In addition, expression level was looked at as a prognostic marker and found to be higher in cases with worse outcome for a number of tumor types, but not all.

The strengths of the study are the large number of cases examined from reliable data sources. Also analyses were performed to evaluate for biases typical of these types of study designs.

The main weakness of the study is the claim that the data shows this to be a good diagnostic biomarker for cancer, however the study was not designed to evaluate for this being a diagnostic biomarker. IN fact, the data seem to indicate that this is a very poor diagnostic biomarker, as figure 1 shows many benign tissue types to have higher TRIM-59 levels than some tumor types. Also, there is an opportunity to look at great detail for certain tumor types but the opportunity was not taken.

Answer: Thank you for your genius opinions. What we should first explain is that in the previous data processing of TCGA datasets, UCSC pan-tumor mean-normalized gene expression data was used. In the process of normalization, gene values are mean-centered per gene. This is useful for correlation analysis between different tumor types, but it weakens the differences in actual expression levels between samples. Therefore, in present analysis, we used the original FPKM expression data of each sample for analysis (line 89-91). As shown in figure 1, all the tumors showed significant higher expression of TRIM59 than their corresponding adjacent tissues. Moreover, comparisons between tumor types are not very useful due to differences in tumor material procession and batch effects. In clinical practice, we only need to consider the amount of TRIM59 expression in the suspected tumor tissue and the adjacent normal tissue. The purpose of this study was to investigate the feasibility of TRIM59 as a diagnostic and prognostic molecule for a variety of tumors.

Specific areas to consider:

figure 1, what does FPKM stand for? should indicate in the figure itself or in the figure caption.

Answer: Thank you for your kind reminding. FPKM (Fragments Per Kilobase of exon model per Million mapped fragments) , FPKM=(total exon Fragments)/(mapped reads(Millions)×exon length(KB)) . In transcriptome sequencing data, FPKM reflects the expression level of genes. We have indicated it in figure caption. (line 332)

All claims about this being a good diagnostic biomarker should be excluded, as none of this data shows such. In fact, the data actually could be interpreted that it shows the opposite at this point in time (some benign levels are higher than some cancer levels and the ROC is not good enough for diagnostic purposes). This study was not designed to determine if this is a diagnostic biomarker or not. From this data, ONe can conclude that TRIM-59 is worth examining as a diagnostic biomarker (and that can be stated here), but studies need to be properly designed to evaluate for this.

Answer: Thank you for your kind reminding. As shown in figure 1, tumor tissues showed significant higher expression than corresponding adjacent tissues in each tumor type. And by setting a threshold, the tumors can be well distinguished from adjacent tissues. For these reasons, as suggested by you, we concluded that TRIM59 is worth examining as a diagnostic biomarker in multiple tumors.

In realtion to point 2, how did the expression levels in benign compare to the good outcome cancers for each tumor type??? This was not included, but should be looked at to further see if this is still a good diagnostic biomarker when that comparison is done.

Answer: We appreciate for your kind reminding. We agree that this comparison is indeed necessary. After comparison, it was found that the expression level of TRIM59 in LUAD with better prognosis was still significantly higher than that in normal adjacent tissues, however no significant difference was observed between good prognosis group and adjacent tissue in KIRP (S1 Fig, line 166-168). Because the datasets did not contain sufficient adjacent samples, such comparisons could not be made in LGG/CESC/SKCM.

In the discussion, maybe more about the limitations of this study, including that such meta-analyses are evaluating large datasets of collections of many tumor types, and that clinical utility would need to be looked at for each different tumor type in relation to clinical needs rather than the entire collection as a whole.

Answer: Thank you. We have made substantial modifications to the discussion section as suggested. And we did not discuss all tumors as a whole in the prognostic analysis in the present version of manuscript.

Attachment

Submitted filename: Response to Reviewers.docx

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Decision Letter 1

Salvatore V Pizzo

2 Sep 2021

TRIM59: a potential diagnostic and prognostic biomarker in human tumors

PONE-D-20-32431R1

Dear Dr. Zhu,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Salvatore V Pizzo

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Acceptance letter

Salvatore V Pizzo

10 Sep 2021

PONE-D-20-32431R1

TRIM59: a potential diagnostic and prognostic biomarker in human tumors

Dear Dr. Zhu:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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on behalf of

Dr. Salvatore V Pizzo

Academic Editor

PLOS ONE

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Fig. Expression of TRIM59 in LUAD-low expression and KIRP-low expression groups and associated adjacent tissues.

    (TIF)

    Click here for additional data file. (131.9KB, tif)
    Attachment

    Submitted filename: Response to Reviewers.docx

    Click here for additional data file. (19.6KB, docx)

    Data Availability Statement

    The data that support the findings of this study were derived from the following resources available in the public domain: https://www.ncbi.nlm.nih.gov/geo (Accession numbers: GSE20685, GSE20711, GSE16446, GSE42568, GSE48390, GSE58812, GSE102287, GSE29013, GSE30219, GSE31210, GSE3141, GSE19188, GSE37745, GSE50081, GSE26193, GSE32062, GSE63885, GSE18520, GSE15459, GSE57303, GSE62254, GSE17891, GSE17679, GSE19750, GSE108474, GSE7696); https://xenabrowser.net (Projects: LUAD, BRCA, UCEC, LUSC, HNSC, KIRC, PRAD, BLCA, THCA, KIRP, LIHC, STAD, COAD, READ, CHOL, CESC, ESCA, GBM, LGG, OV, PAAD, SARC, SKCM)

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