Epithelial-Mesenchymal Transition Gene Signature Is Associated with Neoadjuvant Chemoradiotherapy Resistance and Prognosis of Esophageal Squamous Cell Carcinoma

Song, Kewei; Gu, Baohong; Ma, Chenhui; Wang, Bofang; Wang, Na; Yu, Rong; Chen, Hao

doi:https://doi.org/10.1155/2022/3534433

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

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The Role of Epithelial-Mesenchymal Transition (EMT)-Related Biomarkers in Disease

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Research Article | Open Access

Volume 2022 | Article ID 3534433 | https://doi.org/10.1155/2022/3534433

Epithelial-Mesenchymal Transition Gene Signature Is Associated with Neoadjuvant Chemoradiotherapy Resistance and Prognosis of Esophageal Squamous Cell Carcinoma

Kewei Song,¹Baohong Gu,¹Chenhui Ma,¹Bofang Wang,¹Na Wang,¹Rong Yu,¹and Hao Chen^2,3

Academic Editor: Zhen-Jian Zhuo

Received28 May 2022

Accepted02 Aug 2022

Published27 Aug 2022

Abstract

Background. Neoadjuvant chemoradiotherapy (neo-CRT) in combination with surgery increases survival compared to surgery alone, as indicated by the esophageal squamous cell carcinoma (ESCC) treatment recommendations. However, the benefits of neo-CRT are diverse among patients. Consequently, the development of new biomarkers that correlate with neo-CRT might be important for the treatment of ESCC. Methods. The differentially expressed genes (DEG) between responsive and resistant samples from the GSE45670 dataset were obtained. On the TCGA dataset, survival analysis was performed to identify prognosis-related-EMT-genes. For EMT score model construction, lasso regression analysis in the TCGA cohort was used to identify the genes. In the TCGA-ESCC cohort, age, stage, and EMT score were used to construct a nomogram. Results. In total, 10 prognosis-related-EMT-genes were obtained. These 10 genes consisted of 6 risky genes and 4 protective genes. Based on the lasso analysis and univariate Cox regression, an EMT score model consisting of 7 genes (CLEC18A, PIR, KCNN4, MST1R, CAPG, ALDH5A1, and COX7B) was identified. ESCC patients with a high EMT score have a worse prognosis. These genes were differentially expressed between responsive and resistant patients and had a high accuracy for distinguishing resistant and responsive patients. Conclusions. The identified genes have the potential to function as molecular biomarkers for predicting ESCC patients’ resistance to neo-CRT. This research may aid in the elucidation of the molecular processes driving resistance and the identification of targets for improving the prognosis for ESCC.

1. Introduction

There will be 20,640 new cases of esophageal cancer diagnosed in the United States in 2022, and 16410 people will die from esophageal cancer, according to the 2022 Cancer Statistics for the United States [1]. ESCC, a major histological subtype of EC, accounts for roughly 90% of EC occurrences [2, 3]. A number of factors contribute towards the development of ESCC, including smoking, alcohol abuse, and hot water [4, 5]. ECSS can be difficult to diagnose because there are no specific symptoms and a lack of early detection methods that allow an early diagnosis [6]. Only 15-25% of patients with ESCC survive five years after they were initially diagnosed with the disease [7]. To increase the survival time of ESCC, it is urgently necessary to discover the genetic changes of ESCC and identify new biomarkers.

The most common treatment for locally advanced ESCCs is surgery [7]. It is important to note that disease recurrences are common after surgery, and that the prognosis has not changed significantly over the past few decades [8]. The use of neo-CRT in conjunction with surgery improves prognosis greatly as compared to surgery alone, and it is suggested in treatment recommendations [9]. In a trial including 113 patients with esophageal cancer, the addition of neo-CRT increased the 3-year survival rate from 6% to 32% [10]. It should be mentioned, however, that neo-CRT had two major disadvantages. Initially, the outcomes of neo-CRT treatment are variable. Some patients could be resistant to neo-CRT and have a worse prognosis in terms of survival [11]. In addition, studies have revealed that neo-CRT is linked with an increased risk of postoperative complications [12]. Therefore, it would be beneficial to ESCC patients if novel biomarkers could be identified that would predict their response to neo-CRT.

It is quite common for cancer cells to activate diverse signaling pathways and develop chemotherapy resistance, which helps them stay alive in spite of chemotherapy [13]. In studies, it has been observed that chemotherapy resistance can be caused by the epithelial–mesenchymal transition (EMT) [14–16]. EMT is generally defined as the loss of epithelial characteristics in a cell and the acquisition of mesenchymal characteristics in that same cell [17]. There is increasing evidence that EMT is linked to tumorigenesis, cancer invasion, and drug resistance [18, 19]. It has been found that there is a significant association between EMT genes and metastatic disease, as well as the clinical stage of ESCC [20]. However, more studies are needed to investigate the role of EMT in ESCC.

The expression data of responsive and resistant samples was obtained from different databases. The differentially expressed EMT-related genes that are correlated with neo-CRT responsiveness were identified. Using lasso regression analysis, 7 genes (CLEC18A, PIR, KCNN4, MST1R, CAPG, ALDH5A1, and COX7B) were used to obtain the EMT score for estimating the ESCC prognosis. Besides, EMT score, age, and stage were used for the construction of a nomogram for predicting the 1-, 3-, and 5-year overall survival (OS) of ESCC. For diagnosis (resistant and responsive), the EMT score showed a more accurate value than genes.

2. Patients and Methods

2.1. Gene Expression Data

GSE45670 expression data was downloaded from the GEO [21] by the GEOquery package [22]. This dataset consisted of 28 esophageal squamous cell carcinoma (ESCC) samples and 10 normal samples. In those 28 patients who had ESCCs, neoadjuvant chemoradiation therapy (neo-CRT) that included cisplatin and vinorelbine was given. 11 of them responded completely to the therapy, while 17 others were resistant to treatment. Aside from that, the TCGAbiolinks package was used to download expression information and clinical records of 185 ESCC patients from the TCGA database [23]. GSE86099 used paclitaxel resistant cells and used mRNA transcription files to identify the crucial genes for developing paclitaxel resistance [24]. The detailed information of samples from GSE45670 and TCGA-ESCC is shown in Supplementary Table 1 and Supplementary Table 2.

2.2. DEG Identification and Enrichment Analysis

In order to more clearly illustrate the distribution of 11 responsive and 17 resistant samples, a principal component analysis was applied. In order to increase the quality of samples and the number of DEG, the low-quality samples were then removed by the PCA results. We used the edgeR package to detect DEG between responsive and resistant samples based on log2foldchange and value < 0.05 as cutoff criteria [25]. The enrichment analysis was conducted using the R package “clusterProfiler” [26]. The value < 0.05 was used to distinguish significantly enriched terms.

2.3. Survival-Related EMT Gene Identification

A total of 3600 EMT-related genes were retrieved from EMTome [27]. We determined the genes that were substantially linked with prognosis by samples from TCGA-ESCC. Among these survival-related genes, genes with “” were defined as risky genes, and genes with “” were defined as protective genes. A Venn diagram was used to show the overlap of DEGs, EMT genes, and survival-related genes. The overlapped genes were selected as the survival-related EMT genes.

2.4. Construction of EMT Score Model

We have determined the candidate prognostic genes by applying lasso regression analysis in the TCGA-ESCC cohort by using the glmnet package [28]. We then used univariate Cox regression analysis to calculate the coefficients for each gene. The mRNA expression and the coefficients associated with these genes were used in the calculation of the EMT score. ESCC patients from the TCGA dataset were divided into low and high subgroups based on the median value. The prognosis difference between low and high groups was compared, and the prognosis prediction ability of the EMT score was calculated.

2.5. Development of Nomogram

The TCGA-ESCC cohort included data on age, stage, and EMT score, which were used to construct a nomogram. Calibration curves were generated so that the concordance between the actual survival rate, and the anticipated survival rate could be evaluated. Additionally, the concordance index (C-index) was calculated to assess the capacity of models to forecast prognosis. These analyses were conducted by the package rms.

2.6. Immune Score and Immune Cell Infiltration Analyses

By expression profiles, the immune score and the stromal score were calculated using the “estimate” package [29]. By package GSVA [30], the infiltration levels of immune cell populations were determined.

2.7. Diagnostic Ability in the Classification of Resistant and Responsive Patients

In this study, we used the pROC package to estimate the area under curve (AUC) to evaluate the prediction ability of drug response to therapy. Then, we also calculated the AUC values of EMT score and genes in classifying ESCC and normal samples.

3. Results

3.1. DEG Identification

The flowchart of this study was shown in Figure 1(a). Principal component analysis (PCA) was applied to classify 11 responsive and 17 resistant samples in Figure 1(b). Then, 4 responsive samples (GSM1111699, GSM1111694, GSM1111695, and GSM1111693) and 4 resistant samples (GSM1111677, GSM1111680, GSM1111682, and GSM1111688) were removed since they were outliers (Figure 1(c)). We compared the gene expression between the 7 responsive and 13 resistant samples using the edgeR package. The log2foldchange and value < 0.05 accepted to consider genes to be differentially expressed, identifying a total of 2604 genes (1142 upregulated and 1462 downregulated in the resistant group) above this cut-off (Figures 1(d) and 1(e)). Then, we investigated the biological processes and pathways by enrichment analysis. External encapsulating structure organization (GO: 0045229), extracellular matrix organization (GO: 0030198), and extracellular structure organization (GO: 0043062) are the main biological processes in DEGs (Table 1). Besides, ECM-receptor interaction (hsa04512), human papillomavirus infection (hsa05165), glycosaminoglycan biosynthesis-chondroitin sulfate dermatan sulfate (hsa00532), and focal adhesion (hsa04510) were the main pathways in DEGs (Table 2).

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3.2. Survival-Related EMT Gene Identification

Based on the survival analysis that was conducted on an R loop, among all 17468 genes, 939 genes were significantly related to survival. Among 939 survival-related genes, 118 were protective genes, and 821 were risky genes. The Venn map shows that 6 EMT genes (PIR, EID3, COX7B, CLEC18A, ALDH5A1, and DYNC1I1) are generally upregulated in resistant samples and are risky genes (Figure 2(a)). Besides, the Venn map shows that 4 EMT genes (CAPG, MST1R, KCNN4, and VDR) are generally downregulated in resistant samples and are protective genes (Figure 2(b)). These ten genes were defined as prognosis-related EMT genes (PREMTs).

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3.3. Construction of EMT Score

After that, we performed a lasso analysis on the TCGA-ESCC samples to analyze these ten PREMTs (Figure 2(c)). Via the process of cross-validation, it was shown that 7 PREMTs were capable of producing a superior effect in the model (Figure 2(d)). Then, the univariate Cox regression method was adopted to obtain the coefficient values of genes. An EMT model consisting of 7 genes (CLEC18A, PIR, KCNN4, MST1R, CAPG, ALDH5A1, and COX7B) was identified. The EMT score of individuals using coefficients and gene expression was .

Patients with ESCC who were included in the TCGA were classified as having either a high or low EMT score based on the median value. In the course of our research, we examined the rates of mortality in two different EMT groups. We made the startling discovery that the group at high EMT had a survivability that was much lower than the group at low EMT (Figure 3(a)). The expression values of CLEC18A, PIR, KCNN4, MST1R, CAPG, ALDH5A1, and COX7B between groups were illustrated in Figure 3(b). The expression values of CLEC18A, PIR, ALDH5A1, and COX7B were higher in the group at high EMT. The expression values of KCNN4, MST1R, and CAPG were lower in the group at high EMT. There is a substantial difference in OS between groups ( value < 0.001, Figure 3(c)). In addition, the AUC value was displayed to assess the EMT signature’s predictive abilities. AUC values of the EMT score for 1, 3, and 5 years of survival were 0.662, 0.729, and 0.760, respectively (Figure 3(d)).

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3.4. The Nomogram for OS Prediction

Typically, a nomogram is used to quantify the risk of people in a therapeutic environment by combining various variables. By combining the EMT score, age, and stage, we developed a nomogram to estimate the survival rates of 1-, 3-, and 5-year OS of ESCC (Figure 4(a)). Each component in the nomogram is assigned points according to its contribution. The majority of contributions came from the EMT score, and the C-index for the nomogram was 0.70. Calibration curves of 1-, 3-, and 5-years were used to evaluate the accuracy of the model predictions (Figures 4(b)–4(d)). And the findings suggested that actual and anticipated survival were highly concordant, particularly for three-year survival (Figure 4(b)).

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3.5. Estimation of the EMT Score with Immunity

Using ESTIMATE, the immune and stromal scores were calculated in order to examine the influence of EMT score on tumor immunity. According to the data, the immune score of those with a low EMT score was noticeably higher than those with a high EMT score ( value = 0.011, Supplementary Figure 1A). There was an inverse relationship between the EMT score and the tumor immunity (, , Supplementary Figure 1B). Patients with a high EMT score, on the other hand, tended to have tumor purity that was greater (Supplementary Figure 1A), but the difference was not significant ( value = 0.14).

In addition, the proportions of immune cells were compared across groups (Supplementary Figure 2). The fraction of immune cells such as CD8-T cells, dendritic cells, and natural-killer cells in the low EMT score subgroup was higher than those in the high EMT score subgroup.

3.6. Evaluate the Power of Signatures for Distinguishing Resistant and Responsive Patients

The expression values of genes were compared between resistant and responsive patients (Figure 5(a)). To evaluate the power to distinguish resistant and responsive patients, we measured the AUC of genes and EMT score (Figures 5(b)–5(i)). For diagnosis (resistant and responsive), the EMT score showed the highest AUC value () than genes.

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An independent dataset (GSE86099) contains the expression profiles of the cells associated with paclitaxel resistance. For diagnosis (resistant and responsive), all genes and EMT score showed perfect AUC values () (Supplementary Figure 3A–3H).

3.7. Evaluate the Power of Signatures for Distinguishing ESCC and Normal Samples

The gene expression levels of CAPG, CLEC18A, and MST1R were higher in the tumor samples (Figure 6(a)). We drew the ROC curve of survival-related ECM genes to clarify the diagnostic value for distinguishing ESCC and normal samples (Figures 6(b)–6(i)). The results showed MST1R (), CAPG (AUC 0.743), CLEC18A (), and EMT score () had significant diagnostic values.

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3.8. Validates Prognostic Feature Genes

Then, the correlation between EMT genes expression and patient survival was confirmed (Supplementary Figure 4A–4J). The findings demonstrated that patients with elevated levels of ALDH5A1, PIR, and COX7B had a significantly lower OS.

4. Discussion

ESCC is a kind of cancer that is aggressive and poses a significant threat to human health as a result of its high incidence rate as well as its low survival rate after 5 years [31]. Currently, there are few effective biomarkers that can be used to diagnose, prognosis, and treatment of ESCC. Expression data was utilized to discover EMT genes linked with chemoradiotherapy resistance, as well as their connection with ESCC prognosis. Finally, 6 risky genes (PIR, EID3, COX7B, CLEC18A, ALDH5A1, and DYNC1I1) and 4 protective genes (CAPG, MST1R, KCNN4, and VDR) were identified. Based on lasso analysis, an EMT score model was constructed by the expression values of 7 genes (CLEC18A, PIR, KCNN4, MST1R, CAPG, ALDH5A1, and COX7B). Patients with an elevated EMT score for ESCC had a worse prognosis.

Earlier research has analyzed the difference in gene expression between nCRT responder and nonresponder samples in order to predict nCRT response [32]. Among the identified genes, five genes could accurately predict the response to nCRT. In our study, among the 7 identified genes, ALDH5A1, CLEC18A, COX7B, and PIR were upregulated in resistant patients. CAPG, KCNN4, and MST1R were upregulated in responsive patients. In the predictive models, all seven genes and EMT score could achieve a high accuracy (>80%) in predicting the response to therapy of patients. Besides, MST1R (), CAPG (), CLEC18A (), and EMT score () also had significant diagnostic accuracy in distinguishing tumor and normal samples.

By analyzing expression profiles, we predicted the immune score and the values of immune subpopulations. According to the findings, the group with the high EMT score had a considerable reduction in the number of immune cells. It is possible that this is the reason why people with high EMT scores have a poorer prognosis. EMT may interact with immunosuppression either directly or indirectly, as shown by the results of a prior study [33]. Since immune cells are important biomarkers for immunotherapy, the influence of EMT on immunity is important and needs more studies.

MST1R was related to cellular motility and matrix invasion that are the predictive indications of a tumor phenotype with the ability to metastasize [34]. MST1R was significantly highly expressed in 74% of gastroesophageal samples, and overexpression predicted poor survival [34]. For other genes, their roles in ESCC need more studies.

There were some limitations to our study. These seven key EMT genes have the potential to be used not only in ESCC resistance prediction but also as possible prognostic biomarkers. However, the association between seven important EMT genes and ESCC prognosis may not be robust. Therefore, in order to discover the precise biological behaviors of these seven genes (CLEC18A, PIR, KCNN4, MST1R, CAPG, ALDH5A1, and COX7B) that are involved in the formation of ESCC, experimental validation has to be carried out. Meanwhile, there were just a few ESCC specimens available. In order to evaluate the potential predictive utility of these genes for illness, more validation in more samples is required.

5. Conclusions

Using different datasets, 7 genes that play essential roles in ESCC chemotherapy resistance, namely, CLEC18A, PIR, KCNN4, MST1R, CAPG, ALDH5A1, and COX7B, were selected. The findings of this research may help to clarify the molecular processes of chemotherapy resistance in ESCC and assist us in identifying prospective targets for predicting chemotherapy resistance.

Data Availability

The following information was supplied regarding data availability: data is available at the TCGA (https://portal.gdc.cancer.gov/) and GEO (https://www.ncbi.nlm.nih.gov/geo/).

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Authors’ Contributions

Kewei Song and Hao Chen conceived and designed the study. Kewei Song, Baohong Gu, and Chenhui Ma analyzed the data and plotted the figures. Kewei Song and Bofang Wang drafted the article. Na wang, Rong Yu, and Hao Chen revised the article critically. All authors had final approval of the submitted versions.

Acknowledgments

This study was supported by National Natural Science Foundation of China (no. 82160129), Key Talents Project of Gansu Province (no. 2019RCXM020), Key Project of Science and Technology in Gansu Province (19ZD2WA001), COVID-19 Prevention and Control Technology Research Project of Lanzhou City (2020-XG-54), Science and Technology Project of Chengguan District of Lanzhou City (2020SHFZ0039), Science and Technology Project of Chengguan District of Lanzhou City (2020JSCX0073), and Cuiying Scientific and Technological Innovation Program of Lanzhou University Second Hospital (no. CY2017-ZD01).

Supplementary Materials

Supplementary 1. Supplementary Figure 1: estimation of the EMT score with tumor immunity. (a) Distribution of stromal scores, immune scores, and tumor purity in the low and high EMT score subgroups of the TCGA data cohort. (b) Association between the EMT score and immune score.

Supplementary 2. Supplementary Figure 2: association of the EMT score with the immune subpopulations.

Supplementary 3. Supplementary Figure 3: diagnosis values of (a) CLEC18A, (b) ALDH5A1, (c) PIR, (d) COX7B, (e) CAPG, (f) KCNN4, (g) MST1R, and (h) EMT score for paclitaxel resistance.

Supplementary 4. Supplementary Figure 4: Kaplan–Meier survival curves for genes for ALDH5A1 (a), CAPG (b), CLEC18A (c), COX7B (d), DYNC1I1 (e), EID3 (f), KCNN4 (g), MST1R (h), PIR (i), and VDR (j).

Supplementary 5. Supplementary Table 1: the detailed information of samples from the GSE45670 dataset. Supplementary Table 2: the detailed information of samples from the TCGA-ESCC dataset.

References

R. L. Siegel, K. D. Miller, H. E. Fuchs, and A. Jemal, “Cancer statistics,” CA: a Cancer Journal for Clinicians, vol. 72, no. 1, pp. 7–33, 2022.
View at: Publisher Site | Google Scholar
W. Chen, R. Zheng, P. D. Baade et al., “Cancer statistics in China, 2015,” CA: a Cancer Journal for Clinicians, vol. 66, no. 2, pp. 115–132, 2016.
View at: Publisher Site | Google Scholar
M. Arnold, I. Soerjomataram, J. Ferlay, and D. Forman, “Global incidence of oesophageal cancer by histological subtype in 2012,” Gut, vol. 64, no. 3, pp. 381–387, 2015.
View at: Publisher Site | Google Scholar
C. C. Abnet, M. Arnold, and W. Q. Wei, “Epidemiology of esophageal squamous cell carcinoma,” Gastroenterology, vol. 154, no. 2, pp. 360–373, 2018.
View at: Publisher Site | Google Scholar
F. Islami, H. Poustchi, A. Pourshams et al., “A prospective study of tea drinking temperature and risk of esophageal squamous cell carcinoma,” International Journal of Cancer, vol. 146, no. 1, pp. 18–25, 2020.
View at: Publisher Site | Google Scholar
F. Z. Li and W. Q. Zang, “Knockdown of lncRNAXLOC_001659 inhibits proliferation and invasion of esophageal squamous cell carcinoma cells,” World Journal of Gastroenterology, vol. 25, no. 42, pp. 6299–6310, 2019.
View at: Publisher Site | Google Scholar
A. Pennathur, M. K. Gibson, B. A. Jobe, and J. D. Luketich, “Oesophageal Carcinoma,” Lancet, vol. 381, no. 9864, pp. 400–412, 2013.
View at: Publisher Site | Google Scholar
A. Herskovic, W. Russell, M. Liptay, M. J. Fidler, and M. al-Sarraf, “Esophageal carcinoma advances in treatment results for locally advanced disease: review,” Annals of Oncology, vol. 23, no. 5, pp. 1095–1103, 2012.
View at: Publisher Site | Google Scholar
W. H. Allum, J. M. Blazeby, S. M. Griffin et al., “Guidelines for the management of oesophageal and gastric cancer,” Gut, vol. 60, no. 11, pp. 1449–1472, 2011.
View at: Publisher Site | Google Scholar
T. N. Walsh, N. Noonan, D. Hollywood, A. Kelly, N. Keeling, and T. P. J. Hennessy, “A comparison of multimodal therapy and surgery for esophageal adenocarcinoma,” The New England Journal of Medicine, vol. 335, no. 7, pp. 462–467, 1996.
View at: Publisher Site | Google Scholar
A. C. Berger, J. Farma, W. J. Scott et al., “Complete response to neoadjuvant chemoradiotherapy in esophageal carcinoma is associated with significantly improved survival,” Journal of Clinical Oncology, vol. 23, no. 19, pp. 4330–4337, 2005.
View at: Publisher Site | Google Scholar
W. Yang, X. Xing, S. J. Yeung et al., “Neoadjuvant programmed cell death 1 blockade combined with chemotherapy for resectable esophageal squamous cell carcinoma,” Journal for Immunotherapy of Cancer, vol. 10, no. 1, p. e003497, 2022.
View at: Publisher Site | Google Scholar
C. A. Martz, K. A. Ottina, K. R. Singleton et al., “Systematic identification of signaling pathways with potential to confer anticancer drug resistance,” Science Signaling, vol. 7, article a121, 2014.
View at: Google Scholar
D. Basu, T. T. Nguyen, K. T. Montone et al., “Evidence for mesenchymal-like sub-populations within squamous cell carcinomas possessing chemoresistance and phenotypic plasticity,” Oncogene, vol. 29, no. 29, pp. 4170–4182, 2010.
View at: Publisher Site | Google Scholar
K. A. Whelan, P. M. Chandramouleeswaran, K. Tanaka et al., “Autophagy supports generation of cells with high CD44 expression via modulation of oxidative stress and Parkin-mediated mitochondrial clearance,” Oncogene, vol. 36, no. 34, pp. 4843–4858, 2017.
View at: Publisher Site | Google Scholar
J. Liu, H. Fan, Y. Ma et al., “Notch1 is a 5-fluorouracil resistant and poor survival marker in human esophagus squamous cell carcinomas,” PLoS One, vol. 8, no. 2, article e56141, 2013.
View at: Publisher Site | Google Scholar
I. Pastushenko, A. Brisebarre, A. Sifrim et al., “Identification of the tumour transition states occurring during EMT,” Nature, vol. 556, no. 7702, pp. 463–468, 2018.
View at: Publisher Site | Google Scholar
N. Matsuura, K. Tanaka, M. Yamasaki et al., “NOTCH3 limits the epithelial–mesenchymal transition and predicts a favorable clinical outcome in esophageal cancer,” Cancer Medicine, vol. 10, no. 12, pp. 3986–3996, 2021.
View at: Publisher Site | Google Scholar
D. Stefania and D. Vergara, “The many-faced program of epithelial-mesenchymal transition: a system biology-based view,” Frontiers in Oncology, vol. 7, p. 274, 2017.
View at: Google Scholar
M. M. Forghanifard, O. Moaven, M. Farshchian et al., “Expression analysis elucidates the roles of MAML1 and Twist1 in esophageal squamous cell carcinoma aggressiveness and metastasis,” Annals of Surgical Oncology, vol. 19, no. 3, pp. 743–749, 2012.
View at: Publisher Site | Google Scholar
J. Wen, H. Yang, M. Z. Liu et al., “Gene expression analysis of pretreatment biopsies predicts the pathological response of esophageal squamous cell carcinomas to neo-chemoradiotherapy,” Annals of Oncology, vol. 25, no. 9, pp. 1769–1774, 2014.
View at: Publisher Site | Google Scholar
S. Davis and P. S. Meltzer, “GEOquery: a bridge between the gene expression omnibus (GEO) and BioConductor,” Bioinformatics, vol. 23, no. 14, pp. 1846-1847, 2007.
View at: Publisher Site | Google Scholar
A. Colaprico, T. C. Silva, C. Olsen et al., “TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data,” Nucleic Acids Research, vol. 44, no. 8, article e71, 2016.
View at: Publisher Site | Google Scholar
R. Wang, A. Sumarpo, Y. Saiki, N. Chen, M. Sunamura, and A. Horii, “ABCB1 is upregulated in acquisition of taxane resistance: lessons from esophageal squamous cell carcinoma cell lines,” The Tohoku Journal of Experimental Medicine, vol. 240, no. 4, pp. 295–301, 2016.
View at: Publisher Site | Google Scholar
M. D. Robinson, D. J. McCarthy, and G. K. Smyth, “Edger: a bioconductor package for differential expression analysis of digital gene expression data,” Bioinformatics, vol. 26, no. 1, pp. 139-140, 2010.
View at: Publisher Site | Google Scholar
G. Yu, L. G. Wang, Y. Han, and Q. Y. He, “clusterProfiler: an R package for comparing biological themes among gene clusters,” OMICS, vol. 16, no. 5, pp. 284–287, 2012.
View at: Publisher Site | Google Scholar
S. V. Vasaikar, A. P. Deshmukh, P. den Hollander et al., “EMTome: a resource for pan-cancer analysis of epithelial-mesenchymal transition genes and signatures,” British Journal of Cancer, vol. 124, no. 1, pp. 259–269, 2021.
View at: Publisher Site | Google Scholar
J. Friedman, T. Hastie, and R. Tibshirani, “Regularization paths for generalized linear models via coordinate descent,” Journal of Statistical Software, vol. 33, no. 1, pp. 1–22, 2010.
View at: Publisher Site | Google Scholar
K. Yoshihara, M. Shahmoradgoli, E. Martínez 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
S. Hanzelmann, R. Castelo, and J. Guinney, “GSVA: gene set variation analysis for microarray and RNA-seq data,” BMC Bioinformatics, vol. 14, no. 1, p. 7, 2013.
View at: Publisher Site | Google Scholar
Y. Song, X. Wang, F. Wang et al., “Identification of four genes and biological characteristics of esophageal squamous cell carcinoma by integrated bioinformatics analysis,” Cancer Cell International, vol. 21, no. 1, p. 123, 2021.
View at: Publisher Site | Google Scholar
S. G. Maher, C. M. Gillham, S. P. Duggan et al., “Gene expression analysis of diagnostic biopsies predicts pathological response to neoadjuvant chemoradiotherapy of esophageal cancer,” Annals of Surgery, vol. 250, no. 5, pp. 729–737, 2009.
View at: Publisher Site | Google Scholar
N. Kang, X. Xie, X. Zhou et al., “Identification and validation of EMT-immune-related prognostic biomarkers CDKN2A, CMTM8 and ILK in colon cancer,” BMC Gastroenterology, vol. 22, no. 1, p. 190, 2022.
View at: Publisher Site | Google Scholar
D. V. Catenacci, G. Cervantes, S. Yala et al., “RON (MST1R) is a novel prognostic marker and therapeutic target for gastroesophageal adenocarcinoma,” Cancer Biology & Therapy, vol. 12, no. 1, pp. 9–46, 2011.
View at: Publisher Site | Google Scholar

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Copyright © 2022 Kewei Song 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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