Immunological Role of <i>TP53</i> Somatic Mutation Classification in Human Cancers

Fang, Jianfei; Yang, Ying; Xie, Lina; Yin, Wenjuan

doi:https://doi.org/10.1155/2023/1904309

Journal of Oncology

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Abstract Introduction Methods Results Discussion Conclusions Data Availability Ethical Approval Conflicts of Interest Authors’ Contributions Acknowledgments Supplementary Materials References Copyright Related Articles

Research Article | Open Access

Volume 2023 | Article ID 1904309 | https://doi.org/10.1155/2023/1904309

Immunological Role of TP53 Somatic Mutation Classification in Human Cancers

Jianfei Fang,¹Ying Yang,²Lina Xie,¹and Wenjuan Yin¹

Academic Editor: Fu Wang

Received25 Sept 2022

Revised21 Nov 2022

Accepted30 Jan 2023

Published13 Feb 2023

Abstract

Background. TP53 is a very common tumor suppressor gene and has implicated in various cancers. A systematic immunological analysis of TP53 somatic mutation classification in multiple cancers is still lacking. Methods. To assess the immunological value of TP53 somatic mutation classification in various cancers, we integrated a series of bioinformatics methods to analyze the role of TP53 gene across the public databases, such as UCSC Xena, Cancer Cell Line Encyclopedia (CCLE), Ensembl, and Genotype−Tissue Expression (GTEx). Results. The results revealed that the TP53 expression level had significant difference in tumor tissues and normal tissues, and it had a high expression level in most malignant tumors. Moreover, the missense mutation is the most common type of TP53 mutation in most cancers. In addition, the Cox proportional hazards model analysis and Kaplan−Meier (KM) survival analysis demonstrated that the TP53 expression is a high-risk factor in brain lower-grade glioma (LGG), prostate adenocarcinoma (PRAD), and uterine carcinosarcoma (UCS), which is opposite in uterine corpus endometrial carcinoma (UCEC). Besides, compared to the TP53 nontruncating mutation classification samples, we found that TP53 truncating mutation samples had lower TP53 expression levels in certain types of cancer. Notably, TP53 was associated with the mismatch repair (MMR) gene in some cancers which contained truncating or nontruncating mutation. Based on the classification of truncating or nontruncating mutation, we also discovered that TP53 expression was positively or negatively correlated with the immune score, stromal score, and the levels of immune cell infiltration in different cancers. Conclusions. Our research reveals an overarching landscape of immunological value on TP53 status in various malignant tumors. According to our results, we demonstrate that TP53 also plays an immunological role in various cancers.

1. Introduction

Cancer is a malignant disease with a high mortality rate. Up till now, there is no effective treatment to absolutely cure cancer patients which most likely predicts poor quality of life [1]. However, immunotherapy has been reported to achieve great improvement and regarded as one of the major breakthroughs in cancer treatments [2–4]. It is feasible to seek out potential immunotherapy biomarker through the utility of databases which contained sequencing data.

TP53 is a well-studied tumor suppressor gene and is always the hotspot of tumor research. Nowadays, studies on TP53 function have concentrated on the correlation between variation pattern of TP53 and prognosis [5–7]. TP53 has a prominent role in preventing tumor development and maintaining genomic stability (GS) [8, 9]. The cancer-associated function of the p53 protein depends on its five function domains, including transactivation domain, proline rich domain, DNA-binding domain, oligomerization domain, and carboxy-terminal regulatory domain [10, 11]. In various cancers, TP53 was explicitly linked to cancer development and prognosis [12–14], but these conclusions were still controversial [15]. The most widely accepted concept is that the nontruncated mutation commonly induces high expression of TP53 [13]. Previous studies have demonstrated that TP53 mutations are associated with their expression, cancer prognosis [13], and immune − related research [16]. Integrated studies combined with these features with classification of TP53 somatic mutation are few.

Our work makes it possible to explore the association between TP53 mutation classification and immunological function by multiple databases, such as UCSC Xena, CCLE, Genotype – Tissue Expression (GTEx), and Ensembl. This study may provides new perspective on the relationship between gene variation classification and microenvironment for researchers. We present the following article in accordance with the MDAR reporting checklist.

2. Methods

2.1. TP53 Gene Expression Data

The expression data across 33 types of cancers which contains 11057 samples were downloaded from UCSC Xena. The expression data across 31 health tissues which contains 7858 samples and 28 tumor cell lines were downloaded from GTEx and CCLE. Expression data were extracted using in-house Perl scripts and further analysis using R software (version 4.1.2, R Foundation for Statistical Computing, Vienna, Austria).

2.2. TP53 Variation and Classification Criteria

In this part, the varscan2 variation files with TP53 gene form UCSC Xena (accessed 18 February 2022) were used for analyzing base alterations. According to the mutation classification method in previous studies [13, 15], we distinguished between truncating (including nonsense mutation, frameshift mutation, and splice site mutation) and nontruncating (including missense mutation, in-frame deletion mutation, and in-frame insertion mutation) TP53 mutation.

2.3. Correlation between TP53 and Prognosis

We obtained relevant clinical data from the UCSC Xena. Overall survival (OS) data of patients were used to estimate the prognosis status of different TP53 classification. The patients were divided into two sections by the mean value of TP53 expression as a cutoff value [17]. The KM method and log-rank test were used for survival analysis. Moreover, Cox proportional hazards model analysis was performed to analyze the hazard ratio among different types of cancer.

2.4. Correlation between TP53 and Mismatch Repair Genes

We calculated the Pearson correlation coefficient to estimate the relationship between TP53 and mismatch repair (MMR) genes, including MLH1, MSH2, MSH6, and PMS2. was considered for the statistical significance.

2.5. Correlation between TP53 and Immunity

We used the ESTIMATE algorithm to infer the degree of tumor infiltration [18, 19]. Moreover, the CIBERSORT method was further used for exploring the correlation between TP53 and immunity [20]. The annotation file with Ensembl gene converted to gene symbol was downloaded by Ensembl database.

2.6. Correlation between TP53 and Biological Function

It is convenient to investigate the function of TP53 in various cancers using GSEA package. All analyses were completed by R software (version 4.1.2, R Foundation for Statistical Computing, Vienna, Austria).

2.7. Statistical Analysis

The gene expression values were converted to fragments per kilobase per million for subsequent analysis. was considered for the statistical significance. All statistical analysis was processed by the R software (version 4.1.2, R Foundation for Statistical Computing, Vienna, Austria).

3. Results

3.1. The Landscapes of TP53 Expression across Multiple Datasets

In various normal samples, we found significant difference of TP53 expression, as shown in Figure 1(a) (). The skin tissue expresses TP53 on the highest level and the brain tissue expressed the lowest. We further extracted TP53 expression values from cancer cell lines using CCLE datasets, and the consequence was presented in Figure 1(b), displaying the analogous difference in cell lines ().

(a)

(b)

In addition, we discovered the similar significance of TP53 expression in 33 cancer types (). As shown in Figure 2(a), pheochromocytoma and paraganglioma (PCPG) and kidney chromophobe (KICH) are on the lowest level while UCEC is on the highest level.

(a)

(b)

To further investigate the difference among tumor-normal samples, we analyzed the UCSC Xena data. In view of the fact that some types of cancers included only a few normal samples (for instance, less than 10 or no normal samples datasets), 16 types of cancer were retained for further analysis (Figure 2(b)). Compared with normal samples, we observed higher expression of TP53 in most cancer types, for instance, lung squamous cell carcinoma (LUSC, ), rectum adenocarcinoma (READ, ), stomach adenocarcinoma (STAD, ), colon adenocarcinoma (COAD, ), kidney renal papillary cell carcinoma (KIRP, ), uterine corpus endometrial carcinoma (UCEC, ), prostate adenocarcinoma (PRAD, ), bladder urothelial carcinoma (BLCA, ), lung adenocarcinoma (LUAD, ), liver hepatocellular carcinoma (LIHC, ), kidney renal clear cell carcinoma (KIRC, ), thyroid cancer (THCA, ), and esophageal cancer (ESCA, ). In contrast, TP53 expression was downregulated in kidney chromophobe (KICH, ). Moreover, there were no significant difference in TP53 expression levels in breast invasive cancer (BRCA, ) and head and neck squamous cell carcinoma (HNSC, ).

3.2. TP53 Mutation in Pan-Cancer Cohorts

After eliminating those cancers with the small number samples (for instance, cancer types with less than thirty samples were excluded), 19 cancer types were screened for depicting the TP53 mutation types in pan-cancer, and the landscape was exhibited in Figure 3. Consistent with the previous studies [13, 21], we found that missense mutations accounted for the most of TP53 variants in 19 types of cancer. Next, nonsense mutation ranked second.

3.3. Prognostic Impact of TP53 across TP53-Mut Cancers

In Figure 4, forest plots showed that TP53 was high-risk gene in LGG (hazard ratio = 1.614), PRAD (hazard ratio = 11.97), and UCS (hazard ratio = 2.748), while it was a low-risk gene in UCEC (hazard ratio = 0.5717). Furthermore, we carried out prognostic impact of TP53 expression using mean value as criteria and revealed the significant difference between high and low expression groups among LGG (), PRAD (), UCS (), and UCEC ().

3.4. TP53 Mutation Classification and Survival

We distinguished TP53 mutations into truncating and nontruncating classes to observe their effects on TP53 expression. In line with the earlier conclusion, the results (see Figure 5) showed that TP53 expression levels were upregulated in truncating mutation relative to nontruncating patients [15].

Furthermore, we performed survival analysis for the sake of the evaluation of TP53 mutation’s prognostic value. Unfortunately, no significance difference was detected between TP53 mutation types and overall survival time in any type of cancer (Figure 5). However, KM (Kaplan−Meier) results indicated a clear trend that individuals with truncating mutation had longer survival time in specific cancers, such as READ, BLCA, and PAAD.

3.5. Correlation between TP53 and Mismatch Repair Genes

Mismatch Repair (MMR) is a typical DNA repair mechanism [22]. Ectopic expression of MMR genes might induce the high frequency of somatic mutations [23, 24]. In truncating TP53-mut cancer types (see Figure 6(a)), we examined that MMR genes had significant positive correlation with HNSC (MLH1 correlation coefficient/-value = 0.27/0.00096; MSH6 correlation coefficient/-value = 0.17/0.04), LIHC (MSH2 correlation coefficient/-value = 0.47/0.003; PMS2 correlation coefficient/p-value = 0.33/0.04; MSH6 correlation coefficient/-value = 0.41/0.0104), and LGG (MLH1 correlation coefficient/-value = 0.28/0.047; MSH2 correlation coefficient/-value = 0.31/0.02; MSH6 correlation coefficient/-value = 0.35/0.012). In contrast, negative correlation with TP53 expression was discovered in four cancers, including COAD (MLH1 correlation coefficient/-value = −0.42/0.0009), UCEC (MLH1 correlation coefficient/-value = −0.47/0.0005), SKCM (PMS2 correlation coefficient/-value = −0.5/0.0097), and STAD (PMS2 correlation coefficient/-value = −0.25/0.04).

(a)

(b)

In nontruncating TP53-mut cancer types (Figure 6(b)), we also explored the positive correlation across multiple cancers. Interestingly, compared with different TP53 nontruncating classification, there was opposite correlations in UCEC (MSH6 correlation coefficient/-value = 0.24/0.005), SKCM (MSH6 correlation coefficient/-value = 0.37/0.03), and STAD (MSH2 correlation coefficient/-value = 0.43/3.267e − 05; PMS2 correlation coefficient/-value = 0.32/0.003; MSH6 correlation coefficient/-value = 0.38/0.0003).

3.6. Correlations between TP53 and Immunity

Tumor microenvironment (TME) played an important role during neoplasm occurrence and progression [16, 25, 26]. In this study, we calculate the stromal score, immune score, and estimate score in 19 TP53-mut cancers using the ESTIMATE method. As shown in Table 1, in TP53 truncating mutation cancers, such as BRCA, HNSC, LIHC, LUAD, LUSC, SKCM, PAAD, SARC, and STAD, TP53 was significantly positively correlated with the immune score, as well as stromal score and estimate score, while it was negatively correlated with UCS. Conversely, in nontruncating mutation cancers (for instance, BRCA, GBM, OV, and PRAD), TP53 was significantly negatively associated with the estimated TME scores, while LIHC and PAAD had the opposite consequences.

Subsequently, we investigated the immune cell infiltration levels among TP53-mut caners. The results implied the diverse significant correlation between levels of immune cell infiltration and TP53 expression among TP53 truncating or nontruncating mutant cancers. Significant correlation is screened and presented in Table 2.

3.7. Correlation between TP53 and Biological Function

We carried out a thorough inspection of Gene Set Enrichment Analysis (GSEA) to investigate the relationship between TP53 and biological function inTP53-mut tumor tissues, and the results are shown in Supplementary Figures (Available here).

In TP53 truncating vs nontruncating mutation cancers, the KEGG data indicated that TP53 positively regulated RIG-I-like receptor signal pathway [27] and cytosolic DNA-sensing pathway [28, 29] in BLCA and OV. In contrast, TP53 was predicted to be a negative regulator of the T cell receptor signaling pathway [30], cytosolic DNA-sensing pathway and RIG-I-like receptor signal pathway in GBM, and/or LUSC (Figure 7). In GBM and UCS, the GO data showed that TP53 expression was negatively correlated with adaptive immune response, immune response − regulating signal pathway and had a positive regulation with immune response. In READ, TP53 expression exhibited the opposite effect (see Figure 8).

4. Discussion

In this article, the expression level of TP53 gene was diverse in tumor or normal tissues. TP53 expression was higher in most cancers than in normal tissues except KICH.

Besides, we analyzed the variation of TP53 in 19 cancers and discovered that missense mutation was the dominant subtype, which is consistent with the previous conclusion [13]. At the same time, the evidences showed that patients with the higher expression of TP53 had a worse survival in UCS, LGG, and PRAD. On the contrary, in UCEC, the higher expression of TP53 gene was linked to better survival.

In previous studies, the prognosis roles of TP53 mutation were controversial [15]. Meanwhile, the expression level of TP53 is often related to the mutation types [13]. In order to examine the impact of TP53 mutations on prognosis, TP53 mutations were divided into truncating and nontruncating mutation groups referring to the published reliable classification method [13]. Our results demonstrated that the patients with truncating mutations presented lower TP53 expression. Besides, Kaplan−Meier analysis showed a clear trend that individuals with truncating TP53 mutation had longer survival time in BLCA (-value = 0.12), PAAD (-value = 0.24), and READ (-value = 0.12), consistent with the results of previous published literature [13].

Dan et al. [22] clarified that the abnormal expression of mismatch repaired genes induced the increased frequency of somatic mutation. In colorectal cancer, Perez et al. [31] indicated that mismatch-repaired deficiency can induce TP53 mutation. Fang et al. [32] reported that TP53 defection and mismatch-repaired deficiency commonly occurred in early carcinosarcoma. The correlation between TP53 mutation classification and the expression of the MMR genes was analyzed. In the truncating mutation group, TP53 expression is positively correlated with MMR genes expression in LGG, LIHC, and HNSC, while negative correlation was found in other four cancers, including COAD, SKCM, UCEC, and STAD. In contrast, in the nontruncating mutation group, we detected the positive correlation in most cancers. Compared to TP53 nontruncating classification, there were opposite correlations in UCEC, SKCM, and STAD.

We further explored the relationship between TP53 mutation classification and tumor microenvironment. According to the ESTIMATE algorithm [18, 19], we calculated the stromal score, immune score, and estimate score. In the TP53 truncating mutation group, TP53 is significantly positively correlated with immune score, as well as stromal score and estimate score in specific cancers, such as BRCA, HNSC, LIHC, LUAD, LUSC, SKCM, PAAD, SARC, and STAD. Whereas, it is negatively correlated in UCS. Conversely, in the nontruncating mutation group, TP53 is significantly negatively correlated with stromal/estimate/immune score in BRCA, GBM, OV, and PRAD, while LIHC and PAAD had opposite results.

Correlation between the degree of immune cell infiltration and TP53 expression was estimated further. In truncating or nontruncating mutation samples, obvious associations between the previous two factors were shown in most cancers. Finally, GSEA results indicated that TP53 (truncating or nontruncating) was involved in some immune−related function and pathways.

Some limitations of this paper include the limited databases and scarce experimental verification. In the future, we will adopt more valuable databases and experimental results to confirm and improve our work.

5. Conclusions

In conclusion, this might be the first comprehensive and systematic research to evaluate the immune − related mechanisms of TP53 mutation classification in different cancer. According to our results, TP53 is related to immunological function based on different mutation classification in various cancers. It is worth mentioning that these findings might extend better understanding of TP53 gene underlying the mechanism in the immune system.

Data Availability

The datasets generated and analyzed during the current study are available in the three public repositories, namely, UCSC Xena (https://xena.ucsc.edu/), CCLE (https://portals.broadinstitute.org/ccle/), and GTEx (https://commonfund.nih.gov/GTEx).

Ethical Approval

The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Authors’ Contributions

Conception and design were given by JFF and WJY. Administrative support was given by JFF. Provision of study materials or patients was done by JFF, YY, LNX, and WJY. Collection and assembly of data were done by JFF, YY, and LNX. Data analysis and interpretation were done by JFF and WJY. Manuscript writing was done by JFF. Final approval of manuscript was given by all authors. Jianfei Fang and Ying Yang contributed equally to this work. The authors have completed the MDAR reporting checklist.

Acknowledgments

The study was supported by Zhejiang Provincial Natural Science Foundation of China (No. LQ21F010001). The authors thank Dr. Rui Zhu for correcting grammatical errors and providing language editing.

Supplementary Materials

Figure S1: KEGG pathway analysis of TP53 in multiple cancers. Peaks on the upward curve indicate positive regulation and peaks on the download curve indicate negative regulation. Figure S2: GO analysis of TP53 in multiple cancers. Peaks on the upward curve indicate positive regulation and peaks on the download curve indicate negative regulation. (Supplementary Materials)

References

F. Bray, J. Ferlay, I. Soerjomataram, R. L. Siegel, L. A. Torre, and A. Jemal, “Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries,” CA: A Cancer Journal for Clinicians, vol. 68, no. 6, pp. 394–424, 2018.
View at: Publisher Site | Google Scholar
J. Long, J. Lin, A. Wang et al., “PD-1/PD-L blockade in gastrointestinal cancers: lessons learned and the road toward precision immunotherapy,” Journal of Hematology and Oncology, vol. 10, no. 1, p. 146, 2017.
View at: Publisher Site | Google Scholar
A. Ribas and J. D. Wolchok, “Cancer immunotherapy using checkpoint blockade,” Science, vol. 359, no. 6382, pp. 1350–1355, 2018.
View at: Publisher Site | Google Scholar
H. Sun, S. Y. Liu, J. Y. Zhou et al., “Specific TP53 subtype as biomarker for immune checkpoint inhibitors in lung adenocarcinoma,” EBioMedicine, vol. 60, Article ID 102990, 2020.
View at: Publisher Site | Google Scholar
K. Sabapathy and D. P. Lane, “Therapeutic targeting of p53: all mutants are equal, but some mutants are more equal than others,” Nature Reviews Clinical Oncology, vol. 15, no. 1, pp. 13–30, 2018.
View at: Publisher Site | Google Scholar
Z. Y. Dong, W. Z. Zhong, X. C. Zhang et al., “Potential predictive value of TP53 and KRAS mutation status for response to PD-1 blockade immunotherapy in lung adenocarcinoma,” Clinical Cancer Research, vol. 23, no. 12, pp. 3012–3024, 2017.
View at: Publisher Site | Google Scholar
M. P. Smal, A. I. Rolevich, S. L. Polyakov, S. A. Krasny, and R. I. Goncharova, “FGFR3 and TP53 mutations in a prospective cohort of Belarusian bladder cancer patients,” Experimental Oncology, vol. 36, no. 4, pp. 246–251, 2014.
View at: Google Scholar
G. M. Balaburski, R. D. Hontz, and M. E. Murphy, “p53 and ARF: unexpected players in autophagy,” Trends in Cell Biology, vol. 20, no. 6, pp. 363–369, 2010.
View at: Publisher Site | Google Scholar
E. Bernard, Y. Nannya, R. P. Hasserjian et al., “Implications of TP53 allelic state for genome stability, clinical presentation and outcomes in myelodysplastic syndromes,” Nature Medicine, vol. 26, no. 10, pp. 1549–1556, 2020.
View at: Publisher Site | Google Scholar
A. N. Bullock and A. R. Fersht, “Rescuing the function of mutant p53,” Nature Reviews Cancer, vol. 1, no. 1, pp. 68–76, 2001.
View at: Publisher Site | Google Scholar
B. George, H. Kantarjian, N. Baran, J. D. Krocker, and A. Rios, “TP53 in acute myeloid leukemia: molecular aspects and patterns of mutation,” International Journal of Molecular Sciences, vol. 22, no. 19, p. 10782, 2021.
View at: Publisher Site | Google Scholar
G. Zhou, Z. Liu, and J. N. Myers, “TP53 mutations in head and neck squamous cell carcinoma and their impact on disease progression and treatment response,” Journal of Cellular Biochemistry, vol. 117, no. 12, pp. 2682–2692, 2016.
View at: Publisher Site | Google Scholar
X. Wang and Q. Sun, “TP53 mutations, expression and interaction networks in human cancers,” Oncotarget, vol. 8, no. 1, pp. 624–643, 2017.
View at: Publisher Site | Google Scholar
M. Huang, J. Jin, F. Zhang et al., “Non-disruptive mutation in TP53DNA-binding domain is a beneficial factor of esophageal squamous cell carcinoma,” Annals of Translational Medicine, vol. 8, no. 6, p. 316, 2020.
View at: Publisher Site | Google Scholar
L. A. Donehower, T. Soussi, A. Korkut et al., “Integrated analysis of TP53 gene and pathway alterations in the cancer genome atlas,” Cell Reports, vol. 28, no. 11, pp. 3010–3084.e5, 2019.
View at: Publisher Site | Google Scholar
J. F. Fang, H. N. Yuan, Y. F. Song, P. B. Sun, X. L. Zheng, and X. J. Wang, “E − 26 transformation-specific related gene expression and outcomes in cytogenetically normal acute myeloid leukemia: a meta-analysis,” Chinese Medical Journal, vol. 130, no. 12, pp. 1481–1490, 2017.
View at: Publisher Site | Google Scholar
N. O. Siemers, J. L. Holloway, H. Chang et al., “Genome − wide association analysis identifies genetic correlates of immune infiltrates in solid tumors,” PLoS One, vol. 12, no. 7, Article ID 179726, 2017.
View at: Publisher Site | Google Scholar
K. Yoshihara, M. Shahmoradgoli, E. Martinez et al., “Inferring tumour purity and stromal and immune cell admixture from expression data,” Nature Communications, vol. 4, no. 1, p. 2612, 2013.
View at: Publisher Site | Google Scholar
A. M. Newman, C. L. Liu, M. R. Green et al., “Robust enumeration of cell subsets from tissue expression profiles,” Nature Methods, vol. 12, no. 5, pp. 453–457, 2015.
View at: Publisher Site | Google Scholar
A. Petitjean, M. I. W. Achatz, A. L. Borresen-Dale, P. Hainaut, and M. Olivier, “TP53 mutations in human cancers: functional selection and impact on cancer prognosis and outcomes,” Oncogene, vol. 26, no. 15, pp. 2157–2165, 2007.
View at: Publisher Site | Google Scholar
H. Dan, S. Zhang, Y. Zhou, and Q. Guan, “DNA methyltransferase inhibitors: catalysts for antitumour immune responses,” OncoTargets and Therapy, vol. 12, Article ID 10903, 2019.
View at: Publisher Site | Google Scholar
D. T. Le, J. N. Durham, K. N. Smith et al., “Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade,” Science, vol. 357, no. 6349, pp. 409–413, 2017.
View at: Publisher Site | Google Scholar
P. Zhao, L. Li, X. Jiang, and Q. Li, “Mismatch repair deficiency/microsatellite instability-high as a predictor for anti-PD-1/PD-L1 immunotherapy efficacy,” Journal of Hematology and Oncology, vol. 12, no. 1, p. 54, 2019.
View at: Publisher Site | Google Scholar
T. Wu and Y. Dai, “Tumor microenvironment and therapeutic response,” Cancer Letters, vol. 387, pp. 61–68, 2017.
View at: Publisher Site | Google Scholar
X. Lei, Y. Lei, J. K. Li et al., “Immune cells within the tumor microenvironment: biological functions and roles in cancer immunotherapy,” Cancer Letters, vol. 470, pp. 126–133, 2020.
View at: Publisher Site | Google Scholar
V. Thorsson, D. L. Gibbs, S. D. Brown et al., “The immune landscape of cancer,” Immunity, vol. 48, no. 4, pp. 812–830, 2018.
View at: Publisher Site | Google Scholar
K. Onomoto, K. Onoguchi, and M. Yoneyama, “Regulation of RIG-I-like receptor-mediated signaling: interaction between host and viral factors,” Cellular and Molecular Immunology, vol. 18, no. 3, pp. 539–555, 2021.
View at: Publisher Site | Google Scholar
B. Briard, D. E. Place, and T. D. Kanneganti, “DNA sensing in the innate immune response,” Physiology, vol. 35, no. 2, pp. 112–124, 2020.
View at: Publisher Site | Google Scholar
J. Kwon and S. F. Bakhoum, “The cytosolic DNA-sensingcGAS-STING pathway in cancer,” Cancer Discovery, vol. 10, no. 1, pp. 26–39, 2020.
View at: Publisher Site | Google Scholar
G. Gaud, R. Lesourne, and P. E. Love, “Regulatory mechanisms in T cell receptor signalling,” Nature Reviews Immunology, vol. 18, no. 8, pp. 485–497, 2018.
View at: Publisher Site | Google Scholar

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Copyright © 2023 Jianfei Fang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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