Analysis
Thyroid hormone receptor interacting protein 13 is associated with prognosis and immunotherapy efficacy in human cancers: a pan-cancer analysis
ShengYao Zheng1 . HongYi Wang2 . Yingyi Wang1
Received: 7 September 2024 / Accepted: 14 April 2025
Published online: 20 April 2025
@ The Author(s) 2025 OPEN
Abstract
Thyroid hormone receptor-interacting protein 13 (TRIP13) is involved in the regulation of mitosis and is overexpressed in multiple cancers. However, there is no systematic assessment of the role of TRIP13 in the immunotherapy response across human cancers. Therefore, a pan-cancer analysis involving expression, prognosis, immune-related mechanisms, and biomarker values was performed to explore the associations between TRIP13 expression and the immunotherapy response. TRIP13 is highly expressed in various types of cancer, increasing patient outcomes in eight types of cancer. TRIP13 expression was correlated with significant tumor mutation burden and microsatellite instability, and its mutations were linked with poor prognosis in patients with adrenocortical carcinoma. TRIP13 promoted endothelial cell and hematopoietic stem cell infiltration in human cancers. Additionally, TRIP13 mutation significantly increased the infiltration of CD8 +T cells in kidney renal clear cell carcinoma, which might contribute to poor prognosis. Furthermore, three key genes that interact with TRIP13 were identified: CDC20, RAD1, and MAD2L1, which are related to the cell cycle and ultimately promote tumorigenesis and proliferation. The expression of TRIP13 was significantly greater in kidney renal clear cell carcinoma, liver hepatocellular carcinoma, and pancreatic adenocarcinoma cells than in corresponding normal cells according to qPCR. Taken together, these findings indicate that TRIP13 is associated with poor prognosis in eight human cancers and serves as a novel biomarker for predicting immunotherapy efficacy. Our first pan-cancer study contributes to personalized precision medicine in cancer immunotherapy, promoting subsequent clinical management and improving patient prognosis.
Keywords TRIP13 . Cancer immunotherapy . Cancer immune landscape . Mutation . Tumor microenvironment
Abbreviations
| ACC | Adrenocortical carcinoma |
| AUC | Area under the receiver operator characteristic curve |
| BLCA | Bladder cancer |
| BRCA | Breast cancer |
Supplementary Information The online version contains supplementary material available at https://doi.org/10.1007/s12672-025- 02385-7.
☒ Yingyi Wang, Zhengsy981222@163.com; ShengYao Zheng, 910012606@qq.com; HongYi Wang, wanghongyi201707@163.com | 1Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. 2China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
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(2025) 16:580 | https://doi.org/10.1007/s12672-025-02385-7
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| CAF | Cancer-associated fibroblasts |
| CD40/CD40L | CD40/CD40 ligand |
| CD8+T cells | Cytotoxic T lymphocytes |
| CESC | Cervical squamous cell carcinoma and endocervical adenocarcinoma |
| CHOL | Cholangiocarcinoma |
| CTL | Cytotoxic T lymphocyte |
| DSS | Disease-specific survival |
| DFS | Disease-free survival |
| DLBC | Diffuse large B-cell lymphoma |
| ECs | Endothelial cells |
| ESCA | Esophageal cancer |
| FASN | Fatty acid synthase |
| GEPIA2 | Gene expression profiling interactive analysis 2.0 |
| GBM | Glioblastoma |
| GEO | Gene expression omnibus |
| GDC | Genome data sharing |
| GTEx | Genotype-tissue expression |
| HNSC | Head and neck squamous cell carcinoma |
| HPA | Human protein atlas |
| HR | Hazard ratio |
| ICB | Immune checkpoint blockade |
| IFNG | Interferon gamma |
| KICH | Kidney chromophobe |
| KIRC | Kidney renal clear cell carcinoma |
| KIRP | Kidney renal papillary cell carcinoma |
| LAML | Acute myeloid leukemia |
| LASSO | Least absolute shrinkage and selection operator |
| LIHC | Liver hepatocellular carcinoma |
| LUAD | Lung adenocarcinoma |
| LUSC | Lung squamous cell carcinoma |
| MAD2L1 | Mitotic arrest deficient 2 like 1 |
| MESO | Mesothelioma |
| MSI | Microsatellite instability |
| NSUN2 | NOP2/sun RNA methyltransferase family member 2 |
| OV | Ovarian cancer |
| PAAD | Pancreatic adenocarcinoma |
| PANC-1 | Pancreatic cancer cell line |
| PCPG | Pheochromocytoma and paraganglioma |
| PFS | Progression-free survival |
| PI3K/AKT | Phosphoinositide 3-kinase/protein kinase B |
| PRAD | Prostate adenocarcinoma |
| RAD1 | RAD1 checkpoint DNA exonuclease |
| READ | Rectal adenocarcinoma |
| ROC | Receiver operator characteristic |
| RRM1 | Ribonucleotide reductase regulatory subunit M1 |
| SARC | Sarcoma |
| SKCM | Skin cutaneous melanoma |
| STRING | Search tool for the retrieval of interacting genes/proteins |
| STAD | Stomach adenocarcinoma |
| TCGA | The cancer genome atlas |
| TGCT | Testicular germ cell tumors |
| THCA | Thyroid carcinoma |
| THYM | Thymoma |
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| TIDE | Tumor immune dysfunction and exclusion |
| TIMER2.0 | Tumor immune estimation resource |
| TMB | Tumor mutation burden |
| TRIP13 | Thyroid hormone receptor interacting protein 13 |
| UCEC | Uterine corpus endometrial carcinoma |
| UCS | Uterine carcinosarcoma |
| UVM | Uveal melanoma |
1 Introduction
Immunotherapy has revolutionized the treatment landscape for patients with advanced cancers. The response rates of immune checkpoint inhibitors vary from 15 to 30% in most solid tumors and from 45 to 60% in melanoma and MSI-H tumors [1]. While some patients experience excellent responses, others show limited efficacy or no response to immunotherapy at all [2]. Therefore, more specific mechanisms for separating patients into responsive and nonresponsive groups before immunotherapy can be identified are needed. Tumor-infiltrating leukocytes have been found to respond to heterogeneity in immunotherapy response [3], poor prognosis [4], and even progressive metastases via cell numbers, cell location, and functional cell orientation [5]. However, how tumor-infiltrating leukocytes affect checkpoint inhibition responses remains to be elucidated.
Genetic alterations, including the tumor mutation burden (TMB) [6] and microsatellite instability (MSI) [7], affect cancer- immune features by activating oncogenic pathways [8]. These immune changes modulate the immune microenvironment [9], resulting in different immunotherapy responses [10]. As an AAA + ATPase, thyroid hormone receptor-interacting protein 13 (TRIP13) is highly expressed in hepatocellular carcinoma, kidney renal clear cell carcinoma (KIRC), and lung cancer patients with poor outcomes and immunotherapy resistance [11]. Studies have shown that the PI3K/AKT signaling pathway is inhibited after TRIP13 knockdown in osteosarcoma cells, after which proliferation is reduced [12]. TRIP13 also participates in tumorigenesis through the PI3K/AKT pathway [11, 13], which is capable of mediating the CD40/CD40L pathway [14]. CD40/CD40L is one of the foremost molecular pairs involved in stimulating immune checkpoints [15], the targeted inhibition of which attenuates immune cell effector function and regulates the tumor microenvironment [16]. In summary, TRIP13 likely serves as a crucial regulator of cancer immunotherapy efficacy because of its impact on the PI3K/AKT signaling pathway.
Here, this pan-cancer analysis was the first study of the relationship between TRIP13 expression and the immunotherapy response. We used The Cancer Genome Atlas (TCGA) project and the Genotype-Tissue Expression (GTEx) database to reveal the mRNA and protein expression of TRIP13. We subsequently investigated the prognosis of patients with cancer, including overall survival (OS), disease-free survival (DFS), progression-free survival (PFS), and disease-specific survival (DSS). To determine whether TRIP13 was associated with significant differences in TMB and MSI, the correlation between the mutation group and poor prognosis compared with the wild-type group was also calculated. Immune score, immune checkpoint, and differential immune infiltration analyses were subsequently performed to document immune roles in cancer progression with or without mutations. Protein-protein interactions and gene-gene correlations were used to determine which genes might play a primary role in TRIP13-induced progression. Finally, we compared the areas under the receiver operating characteristic (AUC) curves (AUCs) of different biomarkers for immunotherapy. Potential drug targets for TRIP13 mutations were identified in both uterine cancer and lung adenocarcinoma. Our first pan-cancer study revealed novel specific gene effects on the immune landscape of human cancer, promoting further personalized management of cancer immunotherapy.
2 Materials and methods
2.1 Analysis of gene expression
TIMER2.0 (tumor immune estimation resource) website (http://timer.cistrome.org/) [17-19] provides information on the expression of TRIP13 mRNA in cancer tissue and corresponding normal tissue. We downloaded the mRNA data from the GEPIA2 (Gene Expression Profiling Interactive Analysis 2.0) website (http://gepia2.cancer-pku.cn) for analysis in cases where insufficient tissue was obtained from TIMER2.0 [20, 21]. Moreover, GEPIA2 is also a source of mRNA expression
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data related to different pathological stages (stages I, II, III, and IV). We obtained protein expression data for nine specific cancer tissues and corresponding normal tissues from the CPTAC database [22].
2.2 Survival prognosis analysis
We utilized the “survival map” module of GEPIA2 [20] to calculate OS and DFS by dividing the patients into two groups according to the TRIP13 expression level. We also downloaded the mRNA sequencing data from the Genome Data Sharing (GDC) data portal (https://portal.gdc.cancer.gov/) [23] for univariate Cox regression analysis. We presented the OS, DFS, PFS, and DSS results through the “forest plot” R package using R software version 4.0.3 (The R Foundation for Statistical Computing, 2020).
2.3 Genetic alteration analysis
TRIP13 mutations might be the regulators of the immune cell response to tumor cells via the generation of new antigens, thus promoting immunotherapy heterogeneity. With the help of the cBioPortal website (https://www.cbioportal.org/) [24], we calculated the mutation frequency, mutation types, copy number changes, and protein 3D structures of all TCGA tumors. The OS, DFS, PFS, and DSS of the cancer cohorts included in the TCGA cohort were obtained via the “Comparison” module with the corresponding Kaplan-Meier charts. Furthermore, R software version 4.0.3 was used to correlate TMB and MSI with TRIP13 mRNA expression in the TCGA cohort [10, 25].
2.4 Immune checkpoint and immune score analysis
To reveal the immune-related molecular alterations associated with TRIP13 expression and immunotherapy efficacy, the R package called “immuneeconv” (6 novel algorithms were integrated into it) was used to evaluate the immune score. The expression levels of eight immune checkpoint-related genes in various cancers and their control tissues were compared. The Spearman correlation analysis was used to assess the correlation between TRIP13 and immune checkpoint genes (R software version 4.0.3).
2.5 TRIP13-related gene enrichment analysis
We selected “Homosapiens” as the organism with the protein name entered as TRIP13 when we utilized the STRING (version 11) database (https://string-db.org/) [26] to perform protein-protein interaction analysis with a low confidence score (0.150). The following information describes the investigation details: evidence was the meaning of the edge; no more than 50 interactors were included in the network.
One hundred genes related to TRIP13 expression (via the “Similar Gene Detection” module of GEPIA2) were included in the comparison of TRIP13-binding or TRIP13-interacting genes. We presented a few examples derived from the correlated expression list (via the “Similar Gene Detection” module of GEPIA2) in the form of correlation scatter plots. To prove the potential prognostic value of TRIP13, a Kaplan-Meier (K-M) plotter [27] was used to perform K-M survival analysis to compare the survival duration with the help of a log-rank test in adrenocortical carcinoma (ACC), kidney chromophobe (KICH), and lung adenocarcinoma (LUAD) patients. A patient group with low expression or high expression (http:// kmplot.com/analysis/). The expression refers to the average expression of the selected genes in the correlation scatter plots. We generated a risk score figure along with the survival time and expression of selected genes after least absolute shrinkage and selection operator (LASSO) regression of the example genes. A K-M curve was also designed to illustrate the potential clinical prediction value in ACC patients, accompanied by log-rank P values and hazard ratios (HR) with 95% confidence intervals (CI). A time-dependent receiver operating characteristic (ROC) curve served as another detection tool for the potential prognostic value of the selected genes. Row counts and clinical information from 92 ACC patients were derived from TCGA. A P value <0.05 was regarded as significant (R software version 4.0.3).
We also explored the Spearman correlation ratio of TRIP13 and the selected gene examples in different types of cancer through TIMER 2.0 (“Exploration”-“Gene Corr” module and “TRIP13” as the only input gene). With the assistance of Jenn, we constructed an interactive Venn diagram for the comparison results. Using the STRING database, we constructed a PPI network and defined the critical value as an interaction score of 0.4 (medium position reliability). We utilized the Metascape database (https://metascape.org/gp/index.html) [28] to conduct Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses [29].
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2.6 Comparison of different biomarkers for the immunotherapy response
With the help of the TIDE website (http://tide.dfci.harvard.edu/setquery/) [30, 31], we obtained bar graphs of the predictive efficacy of TRIP13 in different immunotherapy cohort studies, as well as the predictive K-M curves related to cytotoxic T lymphocyte (CTL) infiltration and TRIP13 expression. Containing 83 non-responders and 24 responders, GSE194040 was used to explore the expression of TRIP13 in the treatment of ganitumab plus paclitaxel for breast cancer patients. We also utilized the muTarget website (https://www.mutarget.com/) [32] to observe the changes in the expression of FDA-approved drug target genes after TRIP13 mutation.
2.7 Validation of differential TRIP13 expression in vivo and in vitro
We used the human protein atlas (HPA) database (https://www.proteinatlas.org/) [33] to download the immunohistochemical images and the immunofluorescence images. For immunohistochemistry, we derived normal tissue data from the “Tissue” module and tumor tissue data from the “Pathology” module on the website. We also obtained immunofluorescence images from the “SUBCELL” module. Finally, we adjusted the contrast of the immunohistochemistry images in Adobe Photoshop (version 2021).
2.8 Cell culture
786-O, HK-2, L02, MHCC-97H, HPDE, and PANC-1 cells were purchased from the Chinese Academy of Science (Shanghai). The methods used to validate gene expression have been used in previous studies, and we employed them in the validation of KIRC [34], LIHC [35], and PAAD [36]. We cultured the human KIRC cell line 786-O in Dulbecco’s modified Eagle’s medium with 10% fetal bovine serum and 1% penicillin-streptomycin and the human normal renal tubular epithelial cell line HK-2 in keratinocyte-forming cell culture medium supplemented with 1% keratin-forming cell growth supplement. The human liver hepatocellular carcinoma (LIHC) cell line MHCC-97H and the normal human hepatocyte cell line L02 were cultured in Dulbecco’s modified Eagle’s medium supplemented with 10% fetal bovine serum and 1% penicillin- streptomycin. The human PAAD (pancreatic adenocarcinoma) cell line PANC-1 was cultured in Dulbecco’s modified Eagle medium supplemented with 10% fetal bovine serum and 1% penicillin-streptomycin, and the human pancreatic normal ductal epithelial cell line HPDE was cultured in RPMI-1640 medium supplemented with 10% fetal bovine serum and 1% penicillin-streptomycin. All the cells were cultured for 24 h at 37 ℃ in a 5% CO2 humidified incubator until the cell density reached 105/mL to compare the relative expression of TRIP13.
2.9 Total RNA isolation and quantitative RT-qPCR
We scraped the cells and centrifuged them (16,000 x g, 10 min, 4 ℃) and extracted the supernatant. Then, we used the RNAsimple Total RNA Kit (DP419) (TIANGEN Biotech (Beijing) Co., Ltd., China) for total RNA extraction. cDNA was synthesized via quantification according to the instructions of the RevertAid First Strand cDNA Synthesis Kit (K1622) (Thermo Fisher, China). Real-time quantitative PCR (RT-qPCR) was performed with a Roche LightCycler 480 system (Roche, Basel, Switzerland). The fold changes were calculated according to the formula 2-44Ct method. The sequences of primers used are listed in Supplementary Table 1. The normalization gene used was ß-actin. The number of samples in each group was three.
3 Results
3.1 TRIP13 is highly expressed in most types of human cancer
To ascertain the effects of human TRIP13 on the immunotherapy response, we analyzed TRIP13 mRNA expression via TIMER 2.0 by comparing cancer tissue and normal tissue. TRIP13 was found to have significantly higher transcription levels (P <0.001) in several cancer types, including bladder (BLCA), breast (BRCA), colorectal (COAD), esophageal (ESCA), head and neck (HNSC), kidney clear cell (KIRC) and papillary (KIRP), liver (LIHC), lung adenocarcinoma (LUAD) and squamous cell (LUSC), prostate (PRAD), rectal (READ), and uterine (UCEC) cancers. However, for cholangiocarcinoma (CHOL) and
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Fig. 1 TRIP13 mRNA and protein are highly expressed in most types of cancer. A We used TIMER2.0 to assess TRIP13 expression. If there is no other explanation, * in this article means P <0.05, database, we compared ** means P <0.01 and *** means P <0.001. B In the TCGA and GTEx databases, we compared the mRNA expression of TRIP13 in tumor and normal tissues. C We present TRIP13 protein expression in breast cancer, KIRC, colon cancer, hepatocellular carcinoma, LUAD, OV, PAAD, and UCEC samples via CPTAC. D From left to right, the mRNA expression of ACC, BRCA, KICH, KIRC, KIRP, LIHC, LUAD, and THCA is shown according to the major pathological stages (stages I, II, III, and IV)
glioblastoma (GBM), the number of normal tissue samples used for comparison was small (9 and 5 samples, respectively), so it is recommended that more normal tissue samples be included in future studies. Moreover, TRIP13 was highly expressed in CESC (cervical squamous cell carcinoma and endocervical adenocarcinoma) (P <0.01), PAAD, and thyroid carcinoma (THCA) (P <0.05) (Fig. 1A).
In GEPIA2, we confirmed that TRIP13 was highly expressed in most types of cancer, including DLBC (diffuse large B-cell lymphoma), OV (ovarian serous cystadenocarcinoma), SARC (sarcoma), SKCM (skin cutaneous melanoma), THYM (thyroid carcinoma), and UCS (uterine carcinosarcoma). However, in LAML (acute myeloid leukemia) and TGCT (testicular germ cell tumors), TRIP13 was expressed at low levels in cancer tissues (Fig. 1B). From the perspective of protein expression, we verified the high TRIP13 expression in nine tumors (breast cancer, clear cell RCC (i.e., KIRC), colon cancer, ovarian cancer, hepatocellular carcinoma, LUAD, OV, PAAD, and UCEC) with CPTAC datasets (Fig. 1C). We also revealed that TRIP13 was expressed differently according to diverse pathological stages in patients with ACC, BRCA, KICH, KIRC, KIRP, LIHC, LUAD, and THCA (Fig. 1D). In particular, in ACC, KICH, KIRC, KIRP, and LUAD, the expression of TRIP13 was positively correlated with pathological stage, which illustrated that TRIP13 might be involved in the progression of these types of cancer.
We also obtained immunohistochemistry and immunofluorescence results from the HPA database to validate TRIP13 expression and cellular localization. By immunohistochemistry, we detected high TRIP13 expression in the tumor tissue of patients with COAD, LIHC, and LUSC compared with normal tissue (Fig. 2A-C). This confirmed the results derived from TIMER 2.0. Furthermore, we found that more TRIP13 localized to the nucleus via immunofluorescence staining analysis of A549 cells (Fig. 2D). A549 cells are derived from non-small cell lung cancer, so the immunofluorescence staining results supported the differential TRIP13 expression shown in Fig. 1 and the immunohistochemistry results. SiHa cells represent cervical carcinoma, and their TRIP13 expression confirmed the differential expression of TRIP13, as shown in Fig. 1 (Fig. 2E). We also performed real-time RT-qPCR for verification in vitro (Fig. 2F). Compared with the corresponding normal groups (HK-2, L02, and HPDE), the tumor groups (786-O, MHCC-97H, and PANC-1) presented significantly increased expression of TRIP13 (P <0.01). Especially in LIHC, the increase in relative TRIP13 expression was the most notable. Taken together, these findings suggest that validating TRIP13 expression provides strong evidence that TRIP13 functions as a novel prognostic biomarker.
3.2 High TRIP13 expression is related to poor prognosis
To prove that high TRIP13 expression is related to poor prognosis in cancer patients, cancer patients were divided into two groups on the basis of the expression level of TRIP13 (the median level was the cutoff value). As shown in Fig. 3A, high expression of TRIP13 was related to poor OS in ACC, KICH, KIRP, LGG (brain lower grade glioma), LIHC, LUAD, MESO (mesothelioma), and SKCM patients within the TCGA project. In other words, TRIP13 was a risk factor for cancers according to the univariate Cox analysis as a forest plot embedded in the heatmap from GEPIA2 (Fig. 3A and Supplementary Fig. 1). Although the P values in the forest map and survival map were slightly different, they were both less than 0.05, resulting from database differences. For KICH and PAAD, the P values obtained from GEPIA 2 were more significant than 0.05, which requires a larger sample size for confirmation. For UCEC (P=0.0017), the P value from GDC was more significant.
DFS analysis supported the OS results in patients with ACC, KIRC, KIRP, LGG, and LIHC (Fig. 3B and Supplementary Fig. 2), but in LUAD, MESO and SKCM, high TRIP13 expression did not seem to be associated with poor prognosis in the TCGA cohort. A comparison of the PFS and OS results revealed that ACC, KIRC, KIRP, LGG, LIHC, LUAD and MESO patients with high TRIP13 expression had a poor prognosis (Supplementary Fig. 3). We also found that PCPG, PRAD, SARC, and UCEC patients with high TRIP13 expression in the TCGA cohort likely also had a poor prognosis. In terms of OS and DSS, high TRIP 13 expression in ACC, KIRC, KIRP, LGG, LUAD, MESO, and SKCM patients was related to a poor prognosis. In the SARC and UCEC cohorts, patients with high TRIP13 expression also had a poor prognosis, as shown in Supplementary Fig. 4.
Various definitions of prognostic markers reveal different clinical features associated with the effects of TRIP13 on prognosis. Overall, we believe that there is a relationship between high TRIP13 expression and poor prognosis, especially in patients with ACC, KIRC, KIRP, and LGG, but in specific types of tumors, recurrence or death after treatment likely
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A
TRIP13 Expression Level (log2 TPM)
10.0
**
*
*
*
7.5
5.0
2.5
O
F
J
0.0
ACC.Tumor (n=79)
BLCA.Tumor (n=408) -
BLCA.Normal (n=19) -
BRCA. Tumor (n=1093)
BRCA.Normal (n=112)
BRCA-Basal. Tumor (n=190)
BRCA-Her2. Tumor (n=82) ”
BRCA-LumA.Tumor (n=564) -
BRCA-LumB.Tumor (n=217)
CESC.Tumor (n=304)
CESC.Normal (n=3)
CHOL.Tumor (n=36) -
CHOL.Normal (n=9) -
COAD.Tumor (n=457) -
COAD.Normal (n=41)
DLBC. Tumor (n=48)
ESCA.Tumor (n=184)”
ESCA.Normal (n=11)-
GBM.Tumor (n=153) -
GBM.Normal (n=5)
HNSC.Tumor (n=520)
HNSC.Normal (n=44)-
HNSC-HPV+. Tumor (n=97)”
HNSC-HPV -. Tumor (n=421) -
KICH. Tumor (n=66)
KICH.Normal (n=25)
KIRC.Tumor (n=533)
KIRC.Normal (n=72)
KIRP.Tumor (n=290)
KIRP.Normal (n=32)
LAML. Tumor (n=173)
LGG.Tumor (n=516)
LIHC.Tumor (n=371) -
LIHC.Normal (n=50) -
LUAD.Tumor (n=515) -
LUAD.Normal (n=59)
LUSC.Tumor (n=501)
LUSC.Normal (n=51)-
MESO. Tumor (n=87)
OV.Tumor (n=303)
PAAD. Tumor (n=178)
PAAD.Normal (n=4)
PCPG.Tumor (n=179)”
PCPG.Normal (n=3)
PRAD.Tumor (n=497)
PRAD.Normal (n=52)
READ.Tumor (n=166)
READ.Normal (n=10)
SARC. Tumor (n=259)
SKCM.Tumor (n=103) -
SKCM.Metastasis (n=368)
STAD.Tumor (n=415)
STAD.Normal (n=35)
TGCT.Tumor (n=150)-
THCA.Tumor (n=501)
THCA.Normal (n=59) -
THYM.Tumor (n=120)
UCEC.Tumor (n=545)
UCEC.Normal (n=35)
UCS. Tumor (n=57) -
UVM. Tumor (n=80) ”
B
Tumor
0
Normal
Expression- log .. (TPM + 1)
PS:
-
5
.
Y
+
R
1.
D
V
4
4
”
-
2
y
.
:
2
i
0
(num(T)=163; num(N)=207)
GBM
DLBC (num(T)=47; num(N)=337)
(num(T)=426; num(N)=88)
OV
SARC
(num(T)=262; num(N)=2)
SKCM (num[T]=461; num(N)=558)
THYM (num(T)=118; num(N)=339)
UCS (num(T)=57; num(N)=78)
LAML (num(T)=173; num(N)=70)
TGCT (num[T]=137; num(N)=165)
C
*
2-
Z-score
1-
-1
2
Breast Cancer
Clear Cell RCC
Colon Cancer
Head and Neck Squamous Carcinoma
Hepatocellular Carcinoma
3
2-
*
**
Tumor Normal
Z-score
1 -
0 -
2
3
Lung adenocarcinoma
Ovarian Cancer
Pancreatic Adenocarcinoma
UCEC
D
ACC
BRCA
KICH
KIRC
Relative mRNA Expression
F value = 9.97
Pr(>F) = 1.46e-05
1
F value = 4.7 Pr(>F) = 0.000927
F value = 17.1 Pr(>F) = 3.45e-08
F value = 11.6
.
Pr(>F) = 2.14e-07
(
៛
4
”
៛
”
7
”
2
1
2
-
2
-
-
-
0
T
0
0
Stage |
Stage II
Stage III
Stage IV
Stage I
Stage II
Stage III
Stage IV
Stage X
Stage I
Stage II
Stage III
Stage IV
Stage I
Stage II
Stage III
Stage IV
KIRP
LIHC
LUAD
THCA
F value = 10.2
4
F value = 8.81
Relative mRNA Expression
Pr(>F) = 2.39e-06
Pr(>F) = 1.22e-05
F value = 5.74 Pr(>F) = 0.00073
5
F value = 4.81 Pr(>F) = 0.00261
M
5
A
6
¢
O
+
3
2
”
៛
2
”
9
₪
”
-
-
0.5
-
0
0
0.0
Stage I
Stage II
Stage III
Stage IV
Stage I
Stage II
Stage III
Stage IV
Stage I
Stage II
Stage III
Stage IV
Stage I
Stage II
Stage III
Stage IV
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Normal
Tumor
A459
Siha
A
D
A
E
ER
ER
Colon
Microtubule
Microtubule
B
Liver
TRIP13
TRIP13
Nucleus
Nucleus
C
Lung
Merge
Merge
TI
Relative expression of TRIP13
3
**
**
2
**
1
0
HK-2
786-O
L02
MHCC-97H
HPDE
PANC-1
depends on complicated interactions between the tumor tissue and the whole-body TRIP13, which still requires further investigation.
3.3 TRIP13 amplification and mutation drive poor prognosis and immunotherapy response alterations through antigenic changes
Genetic alterations cause protein structure alterations, resulting in changes in the antigens on the cell surface that probably affect the prognosis via immunoreaction. Figure 4A shows that the highest frequency of TRIP13 changes (~ 15%) occurred in LUSC patients, with amplification as the dominant type, which might be associated with poor prognosis. The frequency of TRIP13 alteration in the ACC was second highest. In HNSC, DLBC, MESO, PAAD, and THCA, amplification was the only type of alteration of TRIP13 (~4%). In most other cancers, such as ACC, ESCA, LUAD, BLCA, and OV, amplification was also the dominant type of alteration (Fig. 4A). Figure 4B shows the sites, types, and case numbers of the TRIP13 mutation. Missense mutations accounted for the greatest number of TRIP13 gene mutations, which were induced by T268I/N/S alterations in SKCM, UCEC, and LUAD cases (Fig. 4B). As a result, T (threonine) was replaced by I (isoleucine), N (asparagine), or S (serine) at the 268th site of the TRIP13 protein. Figure 4C shows the 3D structure of the TRIP13 protein and highlights the 268th site. Taking the ACC as an example, we showed that, compared with the wild-type group, the TRIP13 alteration group had a poorer prognosis [OS (P < 0.001), DFS (P=0.0287), PFS (P <0.001), and DSS (P <0.001)] (Fig. 4D). Furthermore, the expression of TRIP13 in the TRIP13
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A
log10(HR)
1.0
0.5
Overall Survival
0.0
-0.5
-1.0
ACC
BLCA
BRCA
CESC
CHOL
COAD
DLBC
ESCA
GBM
HNSC
KICH
KIRC
KIRP
LAML
LGG
LIHC
LUAD
LUSC
MESO
OV
PAAD
PCPG
PRAD
READ
SARC
SKCM
STAD
TGCT
THCA
THYM
UCEC
UCS
UVM
ACC
KICH
KIRP
LGG
LICH
0
Low TRIP13 Group
9
1.0
High TRIP13 Group
Low TRIP13 Group
9
High TRIP13 Group
Low High TRIP13 Group
9
Low TRIP13 Group
Logrank p=1e-08
Logrank p=0.0085
Logrank p=2.9e-05
High TRIP13 Group
Logrank p=2e-04
0.8
HR(high)=14
D(HR) 3.1e-06
la
0.8
HR(high)=1.5
p(HR)=0.009
0.8
HR(high)=2.2
0.8
HR(high)=1.9
Percent survival
n(high)=38
n(low)=37
Percent survival
Percent survival
n(high)=257 n(low)=258
p(HR)=4.5e-05
p(HR)=0.00025
Percent survival
n(high)=256 n(low)=254
Percent survival
n(high)=181
0.6
0.6
0.6
0.6
0.6
n(low)=181
0.4
0.4
Low TRIP13 Group
High TRIP13 Group Logrank p=0.012
0.4
0.4
0.4
HR(high)=8.9
0
p(HR)=0.039
n(high)=32
0
0.2
0
2
n(low)=32
0.0
0.0
o
0.0
0.0
0
50
100
150
0
50
100
150
0
50
100
150
0
50
100
150
200
0
20
40
60
80
100
120
Months
Months
Months
Months
Months
LUAD
MESO
OV
PAAD
SKCM
.C
Low TRIP13 Group
2
High TRIP13
Low TRIP13 Group
9
2
Logrank p=0.001
High TRIP13
Low TRIP13 Group
1.0
Low TRIP13 Group
Low TRIP13 Group
Logrank p=1.3e-06 HR(high)=3.3
High TRIP13 Group
HR(high)=1.7
Logrank p=0.0076
High TRIP13
Logrank p=0.04
High TRIP13 Group
Logrank p=0.036
0
p(HR)=0.0011
3
0
HR(high)=0.72
2
n(high)=239
p(HR)=3.6e-08 n(high)=41
p(HR)=0.0079
HR(high)=1.5
HR(high)=1.3
p(HR)=0.041
p(HR)=0.036
Percent survival
0.6
n(low)=239
Percent survival
Percent survival
n(high)=212
00
n(low)=41
n(low)=212
Percent survival
n(high)=89
n(high)=229
0.6
n(low)=89
Percent survival
0
n(low)=228
0.4
0
0
0.4
0.4
2
3
0.2
2
0.0
8
0.0
0.0
2
0
50
100
150
200
250
0
20
40
60
80
0
50
100
150
0
20
40
60
80
0
100
200
300
Months
Months
Months
Months
Months
B
Disease Free Survival
log10(HR)
0.4
0.0
ACC
BLCA
BRCA
CESC
CHOL
COAD
DLBC
ESCA
GBM
HNSC
KICH
KIRC
KIRP
LAML
LGG
LIHC
LUAD
LUSC
MESO
OV
PAAD
PCPG
PRAD
READ
SARC
SKCM
STAD
TGCT
THCA THYM
-0.4
UCEC
UCS
UVM
ACC
COAD
KICH
KIRC
KIRP
A
Low High TRIP13 Group
9
Low TRIP13 Group
Low TRIP13 Group
0
Low TRIP13
9
Logrank p=4.1e-06
High TRIP13
Logrank p=0.038
High TRIP13 Group
Logrank p=0.028
High TRIP13 Group
Low TRIP13 Group
HR(high)=0.6
Logrank p=0.0019
High TRIP13 Group
Logrank p=0.00013
A
HR(high)=5.5
p(HR)=2.9e-05
0
p(HR)=0.039
5
HR(high)=4.8
P(HR)=0.047
0.8
HR(high)=1.8
HR(high)=3.3
n(high)=38
n(high)=135
n(high)=32
p(HR)=0.0022
0
p(HR)=0.00029
Percent survival
Percent survival
n(low)=135
Percent survival
n(high)=139
0.6
n(low)=37
US
0.6
n(low)=32
Percent survival
n(high)=257
(low)=258
Percent survival
0.6
n(low)=141
0.4
0.
0.4
0.4
0
0
2
0.2
0.2
2
0.0
KA
0.0
0.0
O
0
50
100
150
0
50
100
150
0
50
100
150
0
20
40
60
80
100
120
140
0
50
100
150
200
Months
Months
Months
Months
Months
LGG
LIHC
PAAD
SARC
THCA
Low TRIP13 Group
2
High TRIP13
Low TRIP13 Group High TRIP13 Group
2
Low TRIP13 Group
1.
Low TRIP13 High TRIP13 Group
2
High TRIP13
Low TRIP13 Group
Logrank p=0.026
Logrank p=0.00068
Logrank p=0.0094
Logrank p=0.0078
High TRIP 13 Group
Logrank p=0.0034
OS
HR(high)=1,4
p(HR)=0.026
8
HR(high)=1.7
p(HR)=0.00073
00
HR(high)=1.8
p(HR)=0.011
0
HR(high)=1.6
n(high)=256
n(high)=181
n(high)=89 n(low)=89
p(HR)=0.0084
0
HR(high)=2.5
P(HR)=0.0047
Percent survival
Percent survival
Percent survival
Percent survival
n(high)=254
0.6
n(low)=254
Percent survival
n(high)=130
0
n(low)=181
0.6
n(low)=130
0
n(low)=251
D.6
0.4
0
D.
0.4
5
0
0
0.2
0.2
2
0.0
0.0
0.0
0.0
0.0
0
50
100
150
0
20
40
60
80
100
120
0
20
40
60
80
0
50
100
150
0
50
100
150
Months
Months
Months
Months
Months
alteration group was significantly greater than that in the wild-type group in ACC (P=0.034), verifying the association between the mutation and expression of TRIP13 (Supplementary Fig. 5). The same conclusion was reached for two other cancers, PRAD and SARC. The reason we were unable to obtain DFS data for patients with PRAD was the low number of cases.
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Fig. 4 Mutation characteristics of TRIP13 are associated with poor prognosis and immunotherapy response. The cBioPortal website was used to explore the characteristics of the TRIP13 gene mutation. The frequencies of mutation type (A) and site (B) are shown. The 3D structure of TRIP13 (C) is shown, along with the site with the highest alteration frequency (T268I/N/S). The underlying associations between mutation state and the DFS, PFS, OS, and DSS of ACC, PRAD, and SARC (D) patients were analyzed via the cBioPortal website. E In addition, Spearman correlation analysis between the TMB, MSI, and expression of TRIP13 in GDC was performed. The acronyms for each disease in Fig. 3A are as follows: lung squamous cell carcinoma (LUSC), invasive carcinoma (ACC), esophageal carcinoma (ESCA), lung adenocarcinoma (LUAD), bladder urothelial carcinoma (BLCA), ovarian serous cystadenocarcinoma (OV), cervical squamous cell carcinoma (CESC), skin cutaneous melanoma (SKCM), stomach adenocarcinoma (STAD), sarcoma (SARC), uterine corpus endometrial carcinoma (UCEC), uterine carcinoma (UCS), head and neck squamous cell carcinoma (HNSC), liver hepatocellular carcinoma (LIHC), breast invasive carcinoma (BRCA), diffuse large B-cell lymphoma (DLBC), glioblastoma multiforme (GBM), brain lower grade glioma (LGG), colorectal adenocarcinoma (COAD), testicular germ cell tumor (TGCT), kidney renal clear cell carcinoma (KIRC), mesothelioma (MESO), kidney renal papillary cell carcinoma (KIRP), prostate adenocarcinoma (PRAD), pancreatic adenocarcinoma (PAAD), and thyroid carcinoma
TMB and MSI have been used as reliable markers for predicting the immunotherapy response. With respect to TMB, as shown in the left half of Fig. 4E, high expression of TRIP13 in ACC, LUAD, STAD, PAAD, PRAD, BRAC, SARC, LGG, KIRC, BLCA, SKCM (P <0.001), LUSC, KICH (P <0.01) and CHOL (P <0.05) was related to high TMB. However, high TMB in ESCA (P <0.01) and THYM (P <0.001) patients was associated with low TRIP13 expression. In patients with STAD, LUSC, UCEC (P <0.001), ACC, BLCA, LIHC, and PRAD (P <0.05), MSI was positively correlated with high TRIP13 expression (the right half of Fig. 4E). However, the relationships between high TRIP13 expression and MSI in patients with DLBC (P <0.01), PCPG, and CESC (P <0.05) were negative. TMB contributed to the poor prognosis of ACC, LUAD, LGG, KIRC, and SKCM patients because of high TRIP13 expression. For ACC, LUSC, and LIHC, high TRIP13 expression was associated with poor prognosis. Although TMB and MSI seem to be indirect markers of the immunotherapy response, we revealed that TRIP13 was associated with immunoreaction via genetic alterations, at least for a few types of cancer.
3.4 TRIP13 Affects immune infiltration associated with the immunotherapy response and poor prognosis
We then investigated how TRIP13 affects the immunotherapy response, leading to a poor prognosis related to high TRIP13 expression. In this study, we focused on immune infiltration in the tumor microenvironment, which supported the fundamentals of immunotherapy. Both tumor cell accumulation and the tumor microenvironment, which contains endothelium cells (ECs), fibroblasts, structural components, and osmotic immune cells, affect tumor development, invasion, and metastasis [37]. CAFs (cancer-associated fibroblasts) have been reported to act as regulators of the tumor microenvironment through interactions with immune cells [38], which might also be involved in the high TRIP13 expression related to poor prognosis. With the aid of the XCELL algorithm, the results revealed that high expression of TRIP13 was positively related to EC infiltration in most cancers, among which KIRC, LIHC, LUAD, and SKCM were confirmed to be related to poor prognosis (Fig. 5C). Moreover, the results also revealed that the higher the expression of TRIP13 in LGG was, the lower the degree of EC infiltration. However, TGCT and BRCA patients with high TRIP13 expression had fewer CAFs in the TME (Fig. 5A and B). It was previously demonstrated that high TRIP13 expression in these two cancers was not associated with poor prognosis. In terms of immune cell infiltration and TRIP13 expression, CD4+Th2 cell infiltration, as well as common lymphoid progenitor infiltration, was negatively associated with high TRIP13 expression in almost all cancers (Fig. 5C). Interestingly, high TRIP13 expression was associated with different patterns of immune cell infiltration, even in patients with a poor prognosis, as indicated by OS, PFS, DFS, and DSS. For example, in patients with KIRC and LUAD, high TRIP13 expression is related to poor prognosis and is accompanied by insufficient CD8 +T cells in KIRC and additional dendritic cell activation in LUAD. We speculated that various infiltration patterns might result from the inhibition or overexpression of several immune checkpoints. From the perspective of immune molecular modulators, we further developed a heatmap of immune checkpoints associated with TRIP13 by performing co-expression analysis. For ACC, CESC, GBM, LAML, LUSC, PCPG, SKCM, STAD, TGCT, and THYM, high TRIP13 expression was estimated to be positively correlated with the expression of several immune checkpoints, which might lead to severe immunosuppression, promoting tumorigenesis (Fig. 6A). For KIRC, LIHC, LUAD, and THCA, immune checkpoints were suppressed in patients with high TRIP13 expression. In addition, TRIP13 expression in CHOL, DLBC, MESO, OV, USC, and UVM did not correlate with any of the reported checkpoint genes (Fig. 6A). The activation of immune checkpoints was also noted to be associated with high expression of TRIP13, as disturbances in immune cell function provide insufficient antitumor effects.
We explored the effect of mutation on immune cell infiltration, possibly accounting for the high TRIP13 expression- related poor prognosis and alterations in immunotherapy efficacy. Via the mutation module of TIMER 2.0, we compared the infiltration ratios of different immune cells in the different samples with or without TRIP13 mutation. We first explored T cells and their related subtypes. As shown in Fig. 6B, TRIP13 gene mutation increased the degree
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| Structural variant data | |||||||||||
| Mutation data | |||||||||||
| CNA data | |||||||||||
A
15%
Mutation
Structural Variant
Amplification
Deep Deletion
Multiple Alterations
Alteration Frequency
10%-
5%-
Lung Squamous Cell Carcinoma
Adrenocortical Carcinoma
Esophageal Adenocarcinoma
Lung Adenocarcinoma
# TRIP13 Mutations
Bladder Urothelial Carcinoma
Ovarian Serous Cystadenocarcinoma
Cervical Squamous Cell Carcinoma
Skin Cutaneous Melanoma
Stomach Adenocarcinoma
Sarcoma
Uterine Corpus Endometrial Carcinoma
Uterine Carcinosarcoma
Head and Neck Squamous Cell Carcinoma
Liver Hepatocellular Carcinoma
Breast Invasive Carcinoma
Diffuse Large B-Cell Lymphoma
Glioblastoma Multiforme
Brain Lower Grade Glioma
Colorectal Adenocarcinoma
Testicular Germ Cell Tumors
Kidney Renal Clear Cell Carcinoma
Mesothelioma
Kidney Renal Papillary Cell Carcinoma
Prostate Adenocarcinoma
Pancreatic Adenocarcinoma
Thyroid Carcinoma
Acute Myeloid Leukemia
Kidney Chromophobe Cholangiocarcinoma
Pheochromocytoma and Paraganglioma
Thymoma
Uveal Melanoma
B
5
Missense
Splicing
T2681/N/S
Fusion
..
0
AAA
0
100
200
300
432aa
ACC
C
D
DFS: logrank p = 0.029
100-5%
PFS: logrank p = < 0.001
OS: logrank p = < 0.001
Do
DSS: logrank p = < 0.001
0
TRIP13 alteration group TRIP13 wild group
-
90%
AS
TRIP13 alteration group TRIP13 wild group
TRIP13 alteration group TRIP13 wild group
TRIP13 alteration group TRIP13 wild group
m
#
nona
60
009
50%
on
2
or
8
BOA
0%
0%
20%
200
2
100
4
Ön
PAS
%
e
Y
The
Disease Free (Months)
Progress Free Burviual (Months)
SARC
Overall Survival (Months)
Months of disease-specific sunivel
Ou
DFS: logrank p = 0.252
on
PFS: logrank p = 0.714
OS: logrank p = 0.011
DSS: logrank p = 0.131
den
9
en
1
1.
crm
7
T2681/N/S
10%
KOP
OPIS
H
KOP
0%
or 9%
EN
on
0%
7
Pa
or
AM
or
3º
20%
TRIP13 alteration group TRIP13 wild group
TRIP13 alteration group TRIP13 wild group
TRIP13 alteration group TRIP13 wild group
TRIP13 alteration group TRIP13 wild group
A
Börs
2
S
đẻ táo thọ thế tế số tố Thế
GA
M
PRAD
Overal Survival (Murthe)
BOM
l
BOAS
10%
p
NA
AN
-
10Ps.
A
6
O
PFS: logrank p = 0.654
OS: logrank p = 0.011
6
DSS: logrank p = < 0.001
OH
jów
Co
DOPE
TRIP13 alteration group TRIP13 wild group
4
OM
TRIP13 alteration group TRIP13 wild group
CON
TRIP13 alteration group TRIP13 wild group
pn
ONI
ON
7
%
E
TMB
à à à tó tó tóth tó tó thọ tảo tác táo to thảo Progress Free Tunivel (Menthe)
họ da 50 40 độ đã Tổ đà độ sốo tia são tão sắp thờ vịo MSI
Quetal Survival (Months)
ACC
STAD
LUAD
CHOL
STAD
ACC
CHOL
MESO
PAAD
LUSC
PRAD
UCEC
KICH
PAAD
BRCA
GEM
SARC
BLCA
LGG
PRAD
KIRC
LIHC
DLBC
-log10(P-value)
UVM
BLCA
LUAD
-log10(P-value)
12.5
MESO
10
SARC
10.0
SKCM
5
UCS
7.5
UCS
THYM
5.0
LUSC
KIRC
2.5
TGCT
Correlation
OV
PCPG
0.1
TGCT
Correlation
KIRP
.
0.2
COAD
READ
0.3
.
0.1
KIRP
0.2
COAD
0.4
READ
0.3
LAML
SKCM
OV
LGG
THCA
THCA
HNSC
KICH
CESC
HNSC
UCEC
BRCA
LIHC
LAML
G
GBM
ESCA
UVM
CESC
ESCA
PCPG
THYM
DLBC
-02
0.0
Correlation(TMB)
02
0,4
-0.4
-0.2
0.0
Comelation(MST)
0.2
0.4
Discover
A
Cancer associated fibroblast_TIDE
☒
☒
☒ ☒
☒
Cancer associated fibroblast_XCELL Cancer associated fibroblast_MCPCOUNTER
☒
☒
☒
Cancer associated fibroblast_EPIC
☒ p > 0.05
ACC (n=79)
BLCA (n=408)
BRCA (n=1100)
BRCA-Basal (n=191)
BRCA-Her2 (n=82)
BRCA-LumA (n=568)
BRCA-LumB (n=219)
CESC (n=306)
CHOL (n=36)
COAD (n=458)
DLBC (n=48)
ESCA (n=185)
GBM (n=153)
HNSC (n=522)
HNSC-HPV- (n=422)
HNSC-HPV+ (n=98)
KICH (n=66)
KIRC (n=533)
KIRP (n=290)
LGG (n=516)
LIHC (n=371)
LUAD (n=515)
LUSC (n=501)
MESO (n=87)
OV (n=303)
PAAD (n=179)
PCPG (n=181)
PRAD (n=498)
READ (n=166)
SARC (n=260)
SKCM (n=471)
SKCM-Metastasis (n=368)
SKCM-Primary (n=103)
STAD (n=415)
TGCT (n=150)
THCA (n=509)
THYM (n=120)
UCEC (n=545)
UCS (n=57)
UVM (n=80)
p < 0.05
Partial_Cor 1
0
-1
B
TRIP13 Expression Level (log2 TPM)
Purity
Cancer associated fibroblast_XCELL
TRIP13 Expression Level (log2 TPM)
Purity
Cancer associated fibroblast_XCELL
Rho = 0.174
6
p = 3.37e-08
Rho = - 0.38
p = 1.85e-35
Rho = 0.003
p = 9.70e-01
…
Rho = - 0.415
p = 1.68e-07
6
a
4
BRCA
4
TGCT
2
3
2
0.25
0.50
0.75
1.00 0.0
0.1
0.2
0.3
0.25
0.50
0.75
1.00 0.0
0.1
0.2
0.3
0.4
Purity
Infiltration Level
Purity
Infiltration Level
C
Stroma score
Microenvironment score
Immune score
T cell regulatory (Tregs)
T cell gamma delta
T cell NIK
T cell CD8+ naive
T cell CD8+ effector memory
T cell CD8+ central memory
T cell CD8+
T cell CD4+ naive
T cell CD4+ memory
T cell CD4+ effector memory
T cell CD4+ central memory
T cell CD4+ Th2
* p < 0.05
T cell CD4+ Th1
** p < 0.01
T cell CD4+ (non-regulatory)
Plasmacytoid dendritic cell
*** p<0.001
Neutrophil
Correlation
NK cell
0.6
Myeloid dendritic cell activated
0.3
Myeloid dendritic cell
0.0
Monocyte
-0.3
Mast cell
-0.6
Macrophage M2
Macrophage M1
Macrophage
Hematopoietic stem cell
Granulocyte-monocyte progenitor
Eosinophil
Endothelial cell
Common myeloid progenitor
Common lymphoid progenitor
Class-switched memory B cell
B cell plasma
B cell naive
B cell memory
B cell
ACC
BLCA
BRCA
CESC
CHOL
COAD
DLBC
ESCA
GBM
HNSC
KICH
KIRC
KIRP
LAML
LGG
LIHC
LUAD
LUSC
MESO
a
PAAD
PCPG
PRAD
READ
SARC
SKCM
STAD
TGCT
THCA
THYM
UCEC
UCS
UVM
of T-cell gamma delta infiltration (P < 0.05) in LUSC. Mutation of the TRIP13 gene in BLCA, COAD, and KIRC led to increased infiltration of CD8 + T cells (P < 0.05).
Among the few cancer types mentioned above, TRIP13 expression was positively correlated with poor prognosis in KIRC, supporting the findings of at least one OS, DFS, PFS, and DSS. Mutations in TRIP13 altered CD8 + T-cell and
Discover
A
CD274
.
:
:
#
*
*
*
:
#
*
*
.
:
*
:
CTLA4
*
#
:
#
:
:
*
*
:
*
:
:
*
#
:
*
* p < 0.05
HAVCR2
:
:
:
#
:
:
*
:
** p < 0.01
LAG3
:
#
:
*
#
*
#
:
#
*
Correlation
*
*
:
0.50
PDCD1
0.25
#
:
*
#
:
*
:
#
:
#
#
:
#
0.00
PDCD1LG2
#
:
:
:
.
#
#
*
:
:
:
*
-0.25
SIGLEC15
:
*
:
#
*
*
*
*
*
:
*
:
*
TIGIT
:
*
*
:
#
:
*
*
:
:
#
:
UVM
UCS
UCEC
THYM
THCA
TGCT
STAD
SKCM
SARC
READ
PRAD
PCPG
PAAD
OV
MESO
LUSC
LUAD
LIHC
LGG
LAML
KIRP
KIRC
KICH
HINSC
GBM
ESCA
DLBC
COAD
CHOL
CESC
BRCA
BLCA
ACC
B
0.2
0.075
.
Wilcoxon, p = 0.0049
T cell CD8+ effector memory_XCELL
Wilcoxon, p = 0.011
Wilcoxon, p = 0.043
0.015
Wilcoxon, p = 0.024
T cell CD8+_MCPCOUNTER
400
T cell CD8+ naive_XCELL
T cell gamma delta_XCELL
·
.
0.050
·
0.1
0.010
200
0.025
0.0
0.005
0
0.000
-0.1
0.000
WT TRIP13
Mutated TRIP13
WT TRIP13
Mutated TRIP13
WT TRIP13
Mutated TRIP13
WT TRIP13
Mutated TRIP13
T-cell gamma delta infiltration, potentially leading to unresponsiveness to immunotherapy, while the innate immune cell alterations caused by TRIP13 mutations could be considered novel drug targets for monotherapy.
3.5 TRIP13-correlated genes associated with poor prognosis
To investigate the association between the TRIP13 gene and the molecular mechanism related to tumorigenesis, we distinguished the proteins involved in TRIP13-binding and the genes correlated with TRIP13 expression to perform pathway enrichment analysis. The 50 TRIP13-binding proteins are presented as an interaction network in Fig. 7A. The top 100 genes from the GEPIA2 tool were included according to the expression of TRIP13. Cross-analysis of the above two types of genes revealed three crossover proteins, namely, CDC20, RAD1, and MAD2L1 (Fig. 7B). From GEPIA2, we chose six genes strongly related to TRIP13 expression: CCT5 (R=0.67), BRD9 (R=0.63), BRIX1 (R=0.60), NUP155 (R=0.58), NSUN2 (R=0.57), KIF2C (R=0.57) (P <0.001), and the above-mentioned three intersecting genes: CDC20 (R =0.5), RAD1 (R=0.49) and MAD2L1 (R=0.54) (Fig. 7C). We then utilized the gene expression data of OS in cancer patients to construct a predictive model for prognosis through the LASSO Cox regression model of ACC patients (Fig. 7E). Through multivariate Cox regression analysis, we derived the risk score formula as follows: risk score = (0.2888)*TRIP13 + (0.442 6)*CDC20. Using the previously obtained A values, we divided the cancer patients into two groups according to their expression levels (Fig. 7E). Patients in the high-expression subgroup died more frequently, and the survival time was shorter. We used R software to draw a transient ROC curve and calculated the AUC at 1, 3, and 5 years to estimate the model’s prediction performance. K-M curves indicated that patients in the higher groups had notably shorter OS than those in the lower groups did (Fig. 7D). The AUC reached 0.868, 0.94, and 0.885 at one, three, and five years, respectively, demonstrating that our model had relatively satisfactory value in the prediction of different follow-up durations. This model also demonstrated that the essential role of TRIP13-related genes, especially CDC20, in TRIP13 was correlated with poor prognosis.
Furthermore, we obtained the K-M curves of different types of cancer patients from the K-M plotter. For example, we generated three K-M curves to confirm that there was a positive relationship between high TRIP13 expression and poor prognosis in patients with ACC, KICH, and LUAD (Fig. 7D). A strong correlation between TRIP13 and the above six genes is shown as a heatmap in Fig. 7F. In the enrichment analysis, TRIP13 and coexpressed genes were highly related to the mitotic cell cycle process, regulation of the cell cycle process, and the cell cycle, highlighting that the TRIP13
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Fig. 7 TRIP13-related genes related to poor prognosis are highlighted. A We selected 50 TRIP13-binding proteins through the STRING tool. B We cross-analyzed TRIP13-binding genes and related genes. C We utilized GEPIA2 to calculate the correlation ratios between TRIP13 and selected genes, including CCT5, BRD9, BRIX1, NUP155, NSUN2, KIF2C, CDC20, RAD1 and MAD2L1. D K-M curves indicating survival differences among patients in different expression groups in the ACC, KICH and LUAD cohorts. E A LASSO Cox regression model was subsequently built with the help of ten prognostic genes. Finally, we performed time-dependent ROC analysis of our model with the AUC in the TCGA cohort and the corresponding K-M chart of OS in different expression groups. F Heatmap of the associations between the above nine selected genes and various cancers in TCGA. G KEGG and GO pathway analyses were performed for the TRIP13-binding gene and interaction gene
and TRIP13-related genes CDC20, RAD1, and MAD2L1 might be involved in the above processes, which in turn lead to tumorigenesis and proliferation (Fig. 7G).
3.6 TRIP13 acts as a novel biomarker for the immunotherapy response
Here, we aimed to find evidence for the predictive effect of TRIP13 on the efficacy of specific immunotherapies. With the help of the TIDE website, by comparing TRIP13 with standardized biomarkers, we evaluated the biomarker correlation of TRIP13 and the predictive efficacy AUC in different immunotherapy cohort studies (Fig. 8A). We found that out of 25 immune checkpoint blockade (ICB) sub-cohorts, nine had an AUC > 0.5 when TRIP13 alone was used. In addition, we found that TRIP13 predicted only a greater value than did TMB and that TRIP13 was comparable to T. Clonality (AUC > 0.5 in 8 ICB subgroups) and was lower than TIDE and MSI. Score, CD274, CD8, IFNG, B. Clonality, Merck18. In the cohort of patients receiving ganitumab plus paclitaxel treatment for breast cancer, we also discovered that the responders had higher levels of TRIP13 expression than did the non-responders (Supplementary Fig. 6). This evidence illustrated that TRIP13 could act as a potential immunotherapy response prediction marker alone (Supplementary Table 2). In addition, we analyzed the impact of CTL numbers with different TRIP13 expression levels on patient survival. Among the LAML and UCEC cohorts, lower CTL infiltration was related to a worse prognosis, especially in patients with higher TRIP13 expression (Fig. 8B and C).
Finally, we compared the changes in the expression levels of FDA-approved drug target genes after TRIP13 mutation. We found that the expression levels of RRM1, RXRA, SLC12A4, FASN, and TYMS (Supplementary Fig. 7A-E) increased with TRIP13 mutation in UCEC. However, in LUSC, the expression of HMGCR decreased (Supplementary Fig. 7F). These findings may provide insight and further research directions for curing patients with this disease.
4 Discussion
Immunotherapy has saved millions of cancer patients, but its response ratio varies by individual, urging us to ascertain more specific factors within mechanisms hidden in hereditary features. The multifunctional TRIP13 protein is related to several cytological processes in a variety of species, such as the recombination of DNA [39], the repair of double- stranded DNA [40], meiosis [41], and the remodeling of the human Shieldin complex [42]. Recently, researchers have discovered the connection between TRIP13 and cancer [43-46]. In addition, TRIP13 has been confirmed to be associated with poor prognosis in different cancers [47-49]. However, whether TRIP13 affects the immunotherapy response via specific molecular mechanisms is still unclear. Thus, we performed a comprehensive pan-cancer analysis of TRIP13, including data from the GEO, CPTAC, and TCGA databases, to explore the potential association between TRIP13 and immune infiltration across various cancers.
The expression of TRIP13 in most cancers is high. By combining public datasets and self-testing data, we found that TRIP13 expression in the tumor tissues of BLCA, BRCA, CHOL, COAD, ESCA, GBM, HNSC, KIRC, KIRP, LIHC, LUAD, LUSC, PRAD, READ, STAD, UCEC (P <0.001), CESC (P <0.01), PAAD, THCA, GBM, DLBC, OV, SARC, SKCM, THYM and UCS (P <0.05) was greater than that in the corresponding control tissues, which was consistent with the existing results in LUAD [44], GBM [50], CESC [45], SKCM [51], COAD [52], KIRC [48], BLCA [53] and ESCA [54]. Nevertheless, the LAML and TGCT tissues presented the opposite results. Thus far, there have been no reports on the relationships between TRIP13 and LAML or TGCT. However, notably, previous studies have shown that TRIP13 may be a therapeutic target by affecting the proliferation of chronic lymphocytic leukemia cells [55]. We also revealed that TRIP13 expression in lung cancer tissues, both LUAD and LUSC, was greater than that in their counterparts. Through protein expression analysis, we found that breast cancer, KIRC, colon cancer, OV, hepatocellular carcinoma, LUAD, OV, PAAD, and UCEC
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A
TRIP13
C
P-value = 0
1
P-value = 0
-
P-value = 0
.
O
R = 0.49
R = 0.58
R =0.5%*
2
0
.
0
LRRC61
NIF3L1
TDRD1
PSMA8
CTDP1
LRRK2
LNX1
log2(RAD1 TPM)
-
log2(NUP155 TPM)
-
log2(NSUN2 TPM)
-
GLYCTK
SELENBP
TTC4
RAD1
ERCC4
PSMD2
CPSF3L
+
·
”
e
TLDC1
MPPED2
USP14
DMC1
STAMBP
RHOXF2
UFD1L
PA
-
LOXL4
PPP2CA
CYB5R2
CIT
MAD2L1
C11orf54
WDYHV1
-
S
HDHD3
UBXN7
CDC20
COMT
PARK2
C4orf33
ARL11
2
P=value = 0 R = 0.54
1
P=value = 0
0
P=value = 0
R = 0.57
.
-
R = 0.5
DARS
DDAH2
BUB1B
TARDBP
MAD2L1BP
LASP1
FNDC3B
6
log2(MAD2L1 TPM)
4
·
MAD2L2
PBLD
RAD51C
PSMD12
ARSA
ANKRD55
log2(CDC20 TPM)
UBA1
log2(KIF2C TPM)
·
4
NPLOC4
4
n
¥
e
₦
-
B
0
P-value = 0
0
2
P-value = 0
.
correlated
interacted
R = 0.67.
R = 0.6
.
P-value = 0
R = 0.63
.
log2(CCT5 TPM)
-
log2(BRIX1 TPM)
·
log2(BRD9 TPM)
-
97
3
47
®
.
.
*
2
V
0
*
0
·
0
·
0
2
4
6
8
·
2
4
6
8
0
2
4
6
*
D
log2(TRIP13 TPM)
Overall Survival ACC
Overall Survival KICH
Overall Survival LUAD
9
2
1.0
Low TRIP13 Group
High TRIP 13 Group
0.8
0.8
0.8
Logrank p=0,001 HR(high)=1.7
P(HR)=0.0011
Percent survival
Percent survival
Percent survival
n(high)=239
0.6
0.6
0.6
n(low)=239
0.4
Low TRIP13 Group High TRIP13 Group
0.4
Low TRIP13 Group
0.4
Logrank p=1e-08
High TRIP 13 Group
Logrank p=0,012 HR(high)=8.9
3
HR(high)=14
p(HR)=3.1e-06
3
p(HR)=0.039
0.2
n(high)=38
n(low)=32
8
n[low)=37
₼(high)=32
4
8
0.0
0
50
100
150
0
50
100
150
0
50
100
150
200
250
Months
Months
Months
E
5-
0
3
5
8
8
1.0
BRD9
.
1.00
CDC20
High groups
Coefficients
0.5
4
Low_risk
Riskscore
Overall survival probability
0.75
Low groups
#
TRIP13
0.0
NUP155
3
MAD2L1
0.50
-0.5
CCTS
2
Logrank p = 4.79e-06
HR(High groups)=10.057
1
0.25
95%CI(3.74, 27.042)
-1.0
BRIX1
.
0
1
2
3
4
Status
0.00
Median time:2.6
Alive
High groups- Low groups
39
17
4
0
0
L1 Norm
ON
10
Dead
40
33
20
8
4
2
0
2.5
5
7.5
10
12.5
Time
Time (years)
1.00
5
8
8
8
12
8
8
6
6
6
5
5
4
3
3
2
2
2
1
1
Partial Likelihood Deviance
True positive fraction
0.75-
11
0
0.50
10
CDC20
9
0.25-
Type
1-Years,AUC=0.868,95%CI(0.746-0.99)
8
TRIP13
3-Years,AUC=0.94,95%CI(0.894-0.986)
0.00-
5-Years,AUC=0.885,95%CI(0.779-0.991)
0.25
0.50
-6
-5
-4
-3
-2
0.00
-1
z-score of expression
False positive fraction
0.75
1.00
Log(A)
-2 -1 0 1 2
F
G
RAD1
NUP155
Spearman_Cor
BIR BOROBARRAU WAY REFERATAIRWA Meiotic cell cycle
Positive regulation of cell cycle Establishment of chromosome localization Kinesins
Centromere complex assembly DNA metabolic process
APC/C-mediated degradation of cell cycle proteins Regulation of cell division Sister chromatid cohesion
8 p > 0.05
DNA IR-damage and cellular response via ATR G2/M Checkpoints Centrosome cycle
Protein localization to chromosome, centromeric region Gi/S Transition
PID AURORA A PATHWAY
ACC (n=79)
BLCA (n=408)
BRCA (n=1100)
BRCA-Basal (n=191)
BRCA-Her2 (n=82)
BRCA-LumA (n=558)
BRCA-LumB (n=219)
CESC (n=306)
CHOL (n=35)
COAD (n=458)
DLBC (n=48)
ESCA (n=185)
GBM (n=153)
HNSC (n=522)
HNSC-HPV- (n=422)
HNSC-HPV+ (n=98)
KICH (n=66)
KIRC (n=533)
KIRP (n=290)
LGG (n=516)
LIHC (n=371)
LUAD (n=515)
LUSC (n=501)
MESO (n=87)
PAAD (n=179)
PCPG (n=181)
PRAD (n=498)
READ (n=166)
SARC (n=260)
SKCM (n=471)
SKCM-Primary (n=103)
STAD (n=415)
TGCT (n=150)
THCA (n=509)
THYM (n=120)
UCEC (n=545)
UCS (n=57)
UVM (n=80)
0
10
20
30
40
50
60
70
log10(P)
Mitotic cell cycle process Cell Cycle
Regulation of cell cycle process
NSUN2
1
0
MAD2L1
-1
KIF2C
CDC20
CCT5
BRIX1
BRDO
p < 0.05
OV (n=303)
SKCM-Metastasis (n=368)
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A
B
Random
Custom
Zhao2019_PD1_Glioblastoma_Pre
Pos=8,Neg=7
· Zhao2019_PD1_Glioblastoma_Post
Pos=6,Neg=3
Continuous z= - 2.85 , p=0.00443
VanAllen2015_CTLA4_Melanoma
Pos=19,Neg=23
TRIP13 High
TRIP13 Low
TIDE
Uppaluri2020_PD1_HNSC_Pre
Pos=8,Neg=15
1.0
1.0
Uppaluri2020_PD1_HNSC_Post
Pos=9,Neg=13
Ruppin2021_PD1_NSCLC
Pos=7,Neg=15
Survival Fraction
0.8
Survival Fraction
0.8
Riaz2017_PD1_Melanoma_Ipi.Prog
Pos=4,Neg=22
Riaz2017_PD1_Melanoma_Ipi.Naive
MSI.Score
Pos=6,Neg=19
0.6
0.6
Prat2017_PD1_NSCLC-HNSC-Melanoma_Nanostring
Pos=21,Neg=12
Nathanson2017_CTLA4_Melanoma_Pre
Pos=4,Neg=5
0.4
0.4
Nathanson2017_CTLA4_Melanoma_Post
Pos=4,Neg=11
Miao2018_ICB_Kidney_Clear
Pos=20,Neg=13
0.2
0.2
TMB
McDermott2018_PDL1_Kidney_Clear
Pos=20,Neg=61
CTL Top (n=31)
CTL Top (n=9)
Mariathasan2018_PDL1_Bladder_mUC
Pos=68,Neg=230
0.0
CTL Bottom (n=31)
0.0
CTL Bottom (n=8)
Liu2019_PD1_Melanoma_Ipi.Prog
Pos= 16,Neg=31
0
500
1000
1500
0
200
600
1000
1400
Liu2019_PD1_Melanoma_Ipi.Naive
OS (day)
OS (day)
Pos=33,Neg=41
CD274
Lauss2017_ACT_Melanoma
Pos=10,Neg=15
Kim2018_PD1_Gastric
Pos=12,Neg=33
Hugo2016_PD1_Melanoma
Pos=14,Neg=12
Hee2020_PD1_NSCLC_Oncomine
Pos=9,Neg=12
CD8
Gide2019_PD1_Melanoma
C
Pos=19,Neg=22
Gide2019_PD1+CTLA4_Melanoma
Pos=21,Neg=11
Chen2016_PD1_Melanoma_Nanostring_Ipi.Prog
Pos=6,Neg=9
Chen2016_CTLA4_Melanoma_Nanostring
Pos=5,Neg=11
IFNG
Braun2020_PD1_Kidney_Clear
Pos=201,Neg=94
Continuous z= 2.67 , p=0.00754
TRIP13 High
TRIP13 Low
1.0
1.0
0.8
T.Clonality
Survival Fraction
Survival Fraction
0.8
0.6
0.6
0.4
0.4
B.Clonality
0.2
CTL Top (n=43)
0.2
CTL Bottom (n=43)
CTL Top (n=228)
0.0
0.0
CTL Bottom (n=227)
Merck18
0
20
40
60
80
100
0
50
100
150
200
OS (month)
OS (month)
0
0.2
0.4
0.6
0.8
1
AUC
Highcharts.com
were the types of cancer in which TRIP13 was highly expressed. TRIP13 expression varied across ACC, BRCA, KICH, KIRC, KIRP, LIHC, LUAD, and THCA. However, the results should be validated in a self-testing dataset.
To explore the prognosis related to high TRIP13 expression, we conducted prognostic analysis on the basis of OS, DFS, PFS, and DSS. Overall, our results revealed that patients with ACC, KIRC, KIRP, LGG, LIHC, LUAD, MESO, and SKCM had a worse prognosis if their TRIP13 expression was higher than the median level. This conclusion was confirmed by previous LGG [50], KIRC [48], SKCM [51], LIHC [56], and LUAD [57] studies or predictions, but the role of TRIP13 in the ACC, KIRP, and MESO has not yet been determined. In addition, we found that high TRIP13 expression was related to poor PFS and DSS in LUAD patients but not in LUSC patients. Consistent with previous research, TRIP13 may play an independent prognostic role in LUAD but not in LUSC [58]. In GBM, our results were different from those of a previous analysis: a previous study reported that high TRIP13 expression might contribute to worsening OS in GBM patients [50], but TRIP13 expression was not associated with OS in our study. This difference was possibly derived from various criteria for the endpoints. The difference in P values caused by the above database requires a larger sample size. A larger, more homogenous cohort would be beneficial to strengthen the findings.
We then used genetic alteration analysis to determine how high TRIP13 expression was related to the immunotherapy response through enough mutations occurring in patients. Mutation and amplification were the most common changes in almost all cancers. Changes in the copy number of TRIP13 have been reported in non-small cell lung cancer [59]. Mutations in TRIP13 reportedly cause infertility in women [60] and Wilms tumors in children [61], suggesting an important role for TRIP13 in cell division. Moreover, the mutation rate of 8 CBX family genes in gastric cancer was less than 5%,
Discover
and the highest mutation rate was in TRIP13 (14%). Studies have revealed that MSI might become the key to assessing tumor malignancy, efficacy, and prognosis, not only in colon cancer [62-65]. With respect to MSI, a positive correlation was detected between TRIP13 expression in MSI and LUSC, STAD, UCEC (P <0.001), ACC, BLCA, LIHC, and PRAD (P <0.05). However, the relationship between DLBC and CESC was the opposite (P <0.01). TRIP13 is independent of the MSI status when it promotes colorectal cancer metastasis. TMB is also an emerging tumor predictor marker [66, 67] that influences sensitivity to immunotherapy. In the left half of Fig. 3E, we provided the first landscape of TMB in all cancer types in TCGA, including the updated ones. The relationship between TMB and TRIP13 in the pan-cancer analysis has not yet been found online. While we identified a significant relationship between TRIP13 alterations and the immunotherapy response, further experimental validation is necessary to clarify the precise role of these genetic changes in modulating the immune response across different cancers.
CAFs, one of the most dominant non-cancerous components in the tumor mass [68], are essential components of the tumor microenvironment; they increase secretory activity, regulate the extracellular matrix after activation by cancer cells [69], and act as barriers to drug and immune cell infiltration. Notably, high expression of TRIP13 and less estimated CAF infiltration in patients with TGCT and BRCA were positively associated. High expression of TRIP13 was positively correlated with ECs infiltration in most cancers, among which the poor prognosis of KIRC, LIHC, LUAD, and SKCM patients was shown to be related to high expression of TRIP13. Moreover, TRIP13 was reported to be significantly correlated with the abundance of CD8 +T cells (cor=0.302, P < 0.001) and B cells (cor =0.43, P < 0.001) in LIHC [70]. We did not obtain identical results from different databases, and further in vitro experiments are warranted. The results of the immune checkpoint and immune score analyses revealed that TRIP13 expression in ACC, CESC, GBM, LAML, LUSC, PCPG, SKCM, STAD, TGCT, and THYM was positively associated with the expression of most immune checkpoint genes. However, KIRC, LIHC, LUAD, and THCA were the opposite, and the expression of TRIP13 in CHOL, DLBC, MESO, OV, USC, and UVM was not correlated with any checkpoint genes. These findings might provide alternative targets for the next step of cancer therapy. Mutation of TRIP13 increased innate immune reactions and adaptive immune reactions, such as CD8 +T cells, in patients with BLCA, COAD, and KIRC. In addition, LUSC had similar results, with T-cell gamma delta increasing when TRIP13 was mutated. The tumor microenvironment is highly complex and varies across cancer types, as evidenced by the divergent relationships between TRIP13 expression and immune checkpoints in different cancers. A more nuanced understanding of how TRIP13 interacts with specific immune components across different contexts is necessary, which requires more data concerning bulk RNA sequencing or even single-cell RNA sequencing of immunotherapy cohorts.
Finally, we identified three specific TRIP13-binding proteins associated with TRIP13 expression: CDC20, RAD1, and MAD2L 1. CDC20 is an essential developmental gene that, like TRIP13, also functions to regulate the cell cycle [71]. Studies have confirmed the status of CDC20 in multiple cancers, such as PAAD [72], PRAD [73], and LIHC [74], and it has now become a new anti-cancer drug target [71]. With respect to the connection between CDC20 and TRIP13, it is interesting that CDC20 and TRIP13 are among the 16 genes thought to cause oocyte maturation arrest, fertilization failure, etc. [75]. Tight functional linkages may imply linkages in carcinogenesis mechanisms. However, RAD1 and MAD2L1 have been poorly studied in oncology, which may point to a direction for further research. While these findings suggest potential interactions, further experimental work is needed to establish the specific relationships among CDC20, RAD1, MAD2L1, and TRIP13.
We found that out of 25 immune checkpoint blockade (ICB) sub-cohorts, nine with an AUC> 0.5 utilizing TRIP13 alone were more reliable than a few well-known markers were. A previous study reported that TRIP13 was related to immune reactions in LIHC patients via unicox regression [70]. However, there is no evidence concerning the comparison of different well-known immune markers and TRIP13. Although TRIP13 cannot predict immunotherapy efficacy for all patients, this discovery implies potential clinical value in the future. Among the LAML and UCEC cohorts, lower CTL infiltration was related to a worse prognosis, especially in patients with higher TRIP13 expression. In gastric cancer, TRIP13 is associated with immune dysfunction in the intestinal type [76]. However, reports have shown that TRIP13 expression in LAML and UCEC patients increases immune dysfunction in vivo. Our results provide more details on CTL infiltration, which is highly related to ICBs; however, these findings are currently based on bioinformatics analyses. Further functional studies are necessary to establish a causal relationship between TRIP13 expression and the immune response, particularly in the context of immunotherapy.
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5 Conclusion
Our pan-cancer analysis identified TRIP13 as a key regulator of tumor progression, poor prognosis, and immunotherapy response. High TRIP13 expression was linked to worse clinical outcomes, particularly in cancers such as ACC, KIRC, LGG, LIHC, and LUAD. We also found that TRIP13 mutations and amplifications influenced TMB, MSI, and immune responses, affecting immunotherapy efficacy. The role of TRIP13 in modulating immune cell infiltration and checkpoint expression varies across cancer types, contributing to immune evasion in some cases. Key interacting proteins, including CDC20, RAD1, and MAD2L1, further suggested that TRIP13 is involved in cell cycle regulation and tumorigenesis. These findings highlight TRIP13 as a potential biomarker and therapeutic target, although further experimental validation is needed to confirm its clinical utility in cancer immunotherapy.
Author contributions SY Z, HY W conceived and designed the study, SY Z, HY W collected the data and performed the analysis, SY Z, HY W finished the manuscript, YY W assisted the data analysis and revised the article. All authors have read and approved the final version of the manuscript.
Funding None.
Data availability The datasets supporting the conclusions of this article are available in TIMER2.0 (tumor immune estimation resource) website (http://timer.cistrome.org/), GEPIA2 (Gene Expression Profiling Interactive Analysis 2.0) website (http://gepia2.cancer-pku.cn), the genome data sharing (GDC) data portal (https://portal.gdc.cancer.gov/), cBioPortal website (https://www.cbioportal.org/), STRING (version 11) database (https://string-db.org/), Kaplan-Meier plotter (http://kmplot.com/analysis/), Metascape database (https://metascape.org/gp/index.html), TIDE website (http://tide.dfci.harvard.edu/setquery/), and the human protein atlas (HPA) database (https://www.proteinatlas.org/).
Declarations
Conflict of interest The authors declare no competing interests.
Ethical approval and consent to participate This study did not require ethical approval, as publicly available data were analyzed.
Consent for publication Not applicable.
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativeco mmons.org/licenses/by-nc-nd/4.0/.
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