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Prognostic significance of autophagy-related genes within esophageal carcinoma

BMC Cancer volume 20, Article number: 797 (2020) Cite this article

Abstract

Background

Several works suggest the importance of autophagy during esophageal carcinoma development. The aim of the study is to construct a scoring system according to the expression profiles of major autophagy-related genes (ARGs) among esophageal carcinoma cases.

Methods

The Cancer Genome Atlas was employed to obtain the esophageal carcinoma data. Thereafter, the online database Oncolnc (http://www.oncolnc.org/) was employed to verify the accuracy of our results. According to our results, the included ARGs were related to overall survival (OS).

Results

We detected the expression patterns of ARG within esophageal carcinoma and normal esophageal tissues. In addition, we identified the autophagy related gene set, including 14 genes displaying remarkable significance in predicting the esophageal carcinoma prognosis. The cox regression results showed that, 7 ARGs (including TBK1, ATG5, HSP90AB1, VAMP7, DNAJB1, GABARAPL2, and MAP2K7) were screened to calculate the ARGs scores. Typically, patients with higher ARGs scores were associated with poorer OS. Moreover, the receiver operating characteristic (ROC) curve analysis suggested that, ARGs accurately distinguished the healthy people from esophageal carcinoma patients, with the area under curve (AUC) value of > 0.6.

Conclusion

A scoring system is constructed in this study based on the main ARGs, which accurately predicts the outcomes for esophageal carcinoma.

Peer Review reports

Background

Nowadays, studies around the world show that, esophageal carcinoma ranks the 7th and 6th places in terms of its morbidity tumor-related mortality cause [1]. However, the area distribution is imbalanced between cases and deaths, and some areas are regarded as the “esophageal carcinoma belt” [2, 3]. Several factors, such as smoking, obesity, low vegetable consumption, have been proven to adversely predict esophageal carcinoma [4, 5]. Nowadays, an increasing obesity trend in western countries results in the further increased esophageal carcinoma morbidity [1], as proven in one meta-analysis [6].

So far, several prognostic studies have used various indicators (including PET-CT [7], HER2 [8], Microsatellite instability [9] and PD-L1 expression [10]) to predict the long-term outcomes, and other studies have evaluated the treatment efficacy, or complications among esophageal carcinoma patients [11, 12]. Autophagy, a crucial biological process, balances the homeostasis in cells through the degradation of injured or aged organelles and proteins within lysosomes [13, 14]. Previous studies have validated the role of autophagy in promoting tumor cell survival and suppressing oncogenesis [15,16,17]. On this account, both enhancing and inhibiting autophagy have been suggested as the treatment strategies [18,19,20,21], which suggest that patient assessment plays an important role in autophagy. A large number of studies demonstrate that autophagy activation shows marked correlation with the tumor dormancy, chemoresistance, as well as stem cell survival [22].

According to previous studies, autophagy is correlated with esophageal carcinoma diagnosis and treatment [23]. However, the role of autophagy in the prognosis for esophageal carcinoma has scarcely been assessed by large-scale expression data. Therefore, this study was designed to construct a novel scoring system based on the screened important ARGs, which might contribute to a perspective tool in evaluating patient prognosis.

Methods

Autophagy related gene set

We identified autophagy related genes in human autophagy database.

Patient samples

Both clinical data and gene expression patterns of esophageal carcinoma were retrieved based on The Cancer Genome Atlas (TCGA) database. Ultimately, a total of 171 specimens were collected based on TCGA microarray to be the cohort.

Processes

First, we carried out Principal component analysis (PCA) using R program to investigate those different expression profiles of genes in the enrolled specimens. Second, we plotted the receiver-operating characteristics (ROC) curves using the survival ROC of R package to assess the survival specificity and sensitivity. Then, we determined the values of area under the ROC curve (AUC) based on those plotted ROC curves. Last, we used the online database Oncolnc to prove the accuracy of our results.

Statistical analyses

Multivariate and univariate Cox regression analyses were carried out to assesse the correlation among ARGs, the risk score value (based on ARGs) and other clinical characteristics in prognosis prediction. Based on the risk score, patients with esophageal carcinoma were divided into low- and high-risk group according to the median score. The results of survival analyses were recognized as the key outcomes; and Kaplan-Meier analysis was used to analysis the patient prognosis (OS) among various ranges of score. The time-dependent ROC curves were plotted to calculate the risk model predictive ability. R software (version 3.6.1) was used to make Bioinformatic analyses. P < 0.05 was regarded as statistically significant. X2 test or Student’s t-test was used to analysis the differences of clinical baseline characteristics between low-risk and high-risk group.

Results

Differentially expressed ARGs (DEARGs) within esophageal carcinoma tissues

Wilcoxon signed-rank test was carried out to analyze the 232 ARGs expression within 11 normal esophageal and 160 esophageal carcinoma samples, and 28 DEARGs were found, including 24 up-regulated and 4 down-regulated ones (|log2FC| > 1, FDR < 0.05, Fig. 1). Figure 1a was box plot of 28 DEARGs (red boxes showed the genic expression of tumor sample, and green boxes indicated the genic expression of normal sample); and Fig. 1b was the heat map of 28 DEARGs (red indicated higher expression and green indicated lower expression).

Fig. 1
figure 1

DEARGs within esophageal carcinoma tissues

Full size image

Prognostic factor of DEARGs among esophageal carcinoma patients

Univariate Cox regression analysis was performed to examine ARGs expression within esophageal carcinoma cases, for the sake of identifying the significance of ARGs in prognosis prediction. According to our results, TBK1, ATG5, HSP90AB1, VAMP7, DNAJB1, GABARAPL2, and MAP2K7 expression was remarkably related to patient OS (P < 0.05). (Fig. 2a).

Fig. 2
figure 2

ARGs within patients with esophageal carcinoma were used to construct and analyze the risk models for OS. a ARGs expression among esophageal carcinoma cases was analyzed through univariate Cox regression analysis b OS for esophageal carcinoma cases with low (green line) and high (red line) risk was analyzed by Kaplan-Meier survival curve. c ROC curves showing the values of AUC for OS among esophageal carcinoma cases

Full size image

To identify the best signature to in prognosis prediction, multivariate Cox proportional hazards regression analysis was further carried out. As suggested by our results, TBK1 (HR 1.877, 95%CI 0.985–3.574), ATG5 (HR 2.913, 95%CI 1.464–5.795), HSP90AB1 (HR 1.449, 95%CI 1.005–2.087), VAMP7 (HR 2.712, 95%CI 1.310–5.614), DNAJB1 (HR 1.688, 95%CI 1.255–2.270), GABARAPL2 (HR 2.853, 95%CI 1.585–5.136), and MAP2K7 (HR 0.510, 95%CI 0.217–1.199) were identified as the independent adverse prognostic factors. The risk score calculating formula was (0.6295 × TBK1 expression) + (1.0691 × ATG5 expression) + (0.3706 × HSP90AB1 expression) + (0.9976 × VAMP7 expression) + (0.5236 × DNAJB1 expression) + (1.0485 × GABARAPL2 expression) - (0.6739 × MAP2K7 expression).

Higher ARGs score was related to the worse OS

Table 1 displays the baseline characteristics of all included patients. As suggested by our log-rank test results obtained using the Kaplan-Meier survival curve, compared with low-risk cases, the high-risk cases showed poor prognosis (Fig. 2b).

Table 1 Baseline characteristics of patients with esophageal carcinoma
Full size table

Then, the ROC curves were plotted to determine the effect of that as-constructed signature on predicting patient OS. As suggested by our results, the value of AUC was 0.801 for those prognostic models to predict OS, which was greater than that of gender (AUC = 0.494), stage (AUC = 0.634), T (AUC = 0.542), N (AUC = 0.665), and M (AUC = 0.547). Afterwards, the distributions of patient OS risk scores were ranked and examined (Fig. 3). To better predict the clinical outcomes for patients with esophageal carcinoma, baseline characteristics (including gender, tumor, node, and metastasis stage in every patient) were incorporated into ROC curve analysis (Fig. 2c).

Fig. 3
figure 3

Prognosis for cases with low and high risk esophageal carcinoma. a Distribution of risk scores for esophageal carcinoma cases in OS model with low (green) and high (red) risks. b Scatter plot showing esophageal carcinoma case survival status within that OS model. Green and red dots represent the survival and death of patients, respectively. c Risk gene expression in low (pink) and high (blue) risk esophageal carcinoma cases within that OS model

Full size image

Gene Ontology (GO) functional enrichment analysis

The above-mentioned genes with prognostic significance were extracted for functional enrichment analysis. According to the top 10 most significant GO terms with regard to MF, CC and BP categories, the above-mentioned ARGs were potentially related with ARGs regulation. (Fig. 4).

Fig. 4
figure 4

GO enrichment analysis

Full size image

Multivariate cox regression analysis on risk score together with baseline clinical features

As suggested by the findings, only risk score (HR 1.271 95%CI 1.176–1.372) was the independent risk factor for the worse OS. (Fig. 5) Fig. 5a indicated the results of univariate cox regression analysis, and Fig. 5b showed the results of multivariate cox regression analysis.

Fig. 5
figure 5

Multivariate and univariate Cox regression analyses for OS among esophageal carcinoma cases. a-b univariate and multivariate cox regression analyses, respectively

Full size image

Associations among the risk scores, ARGs, and clinical status (including survival, gender, age, stage, T, N, and M stage)

The results indicated that HSP90AB1, VAMP7, and risk score were related to the survival status and gender. In addition, GABARAPL2 was markedly associated with the survival status and stage. (Fig. 6).

Fig. 6
figure 6

Clinical correlations among ARGs included in the risk score, risk score, and baseline clinical characteristics

Full size image

The online database Oncolnc (http://www.oncolnc.org/) was used to test the included ARGs

The high-risk ARGs were correlated with poor prognosis; however, the low-risk DEARGs showed significant association with favorable patient prognosis. (Fig. 7).

Fig. 7
figure 7

Verification of ARGs included in the risk score by Oncolnc (http://www.oncolnc.org/)

Full size image

Discussion

Histologically, esophageal carcinoma can be classified as adenocarcinoma and squamous cell carcinoma [24]. This study included both esophageal cancer subtypes to develop a more helpful tool for predicting the prognosis for esophageal carcinoma. Obviously, the risk score calculated by ARGs demonstrated superiority to other classical clinical indicators, and it was also the independent risk factor for patient survival.

In physiological situation, autophagy, which serves as a crucial catabolic process, works as an intracellular quality control system to maintain internal environment homeostasis through removing the damaged proteins [25]. However, autophagy has been proven to play an important role in various disorders, including cancer, degradation, autoimmune disease and inflammation [23]. During cancer development, autophagy promotes cancer cell survival within various environments [26, 27]. As the new therapeutic approach, the mechanism of autophagy in tumor has long been proposed. Even, Sui et al. [28] pointed that autophagy was accessary for responses to chemoradiotherapy.

Firstly, our study verified the different expression of ARGs between esophageal carcinoma and normal tissues. Therefore, it may be of significant to exploit a useful autophagy-related risk score for patients with esophageal carcinoma. According to our rsult, the risk score calculated by the DEARGs score was superior to other classical clinical indicators.

Among the studies related to esophageal carcinoma, Langer et al. [23] showed that autophagy was correlated with esophageal carcinoma treatment and diagnosis. To treat esophageal carcinoma using targeting ARGs, several studies try to develop the useful treatments for esophageal carcinoma through enhancing or inhibiting autophagy. In the study conducted by Huang, et al. [29], the results showed that Pristimerin reduced the growth of esophageal carcinoma. Moreover, previous studies also indicate that ginsenoside Rk3 [30] and Sinoporphyrin sodium (DVDMs)-Photodynamic therapy (PDT) [31], which exert their functions by targeting autophagy, inhibit the survival of esophageal carcinoma cells. Furthermore, other studies also illustrate that the treatment targeting autophagy contributes to enhancing the anti-tumor effect [32,33,34], which functions based on the chemotherapy agents (cisplatin [33], 5-fluorouracil (5-FU) [34]).

Our results in this study showed that several genes served as the risk factors for patient prognosis. Of them, ATG5 and TBK1 had attracted our great interests. For ATG5, Cheng et al. [33] demonstrated in the esophageal carcinoma study that ATG5 was involved in autophagy activation. Additionally, Zheng et al. [35] suggested that ATG5 inhibition contributed to treatment for esophageal carcinoma patients. Furthermore, autophagy abolition through the ATG5/7 re-sensitized EC109/CDDP knockdown or the use of pharmacological inhibitors is greatly significant [36] not only in the esophageal, but also in gastric [37], colorectal [38, 39], bladder [40], ovarian [41], and prostate cancers [42]. With regard to TBK1, it has been proven that TBK1 takes part in modulating cell growth and autophagy [43]. Moreover, Sarraf et al. [44] also indicated that TBK1 exerted an important role in mitophagy.

Conclusions

In conclusion, the major ARGs are taken to develop a novel scoring system in this study, which accurately predict the clinical outcomes for esophageal carcinoma patients. The included ARGs are confirmed using the online dataset oncolnc. Therefore, our results show that ARGs can be recognized as the therapeutic targets and prognostic biomarkers for esophageal cancer. Nonetheless, our findings should be further investigated in the future.

Availability of data and materials

Each dataset used in this study was searched based on the published literature, freely accessible from related authors upon request.

Abbreviations

ARGs:

Autophagy-related genes

OS:

Overall survival

ROC:

Receiver operating characteristic

AUC:

Area under curve

TBK1:

TANK-binding kinase 1

ATG5:

Autophagy protein 5

HSP90AB1:

Heat shock protein 90 kDa alpha, class B member 1

VAMP7:

Vesicle-Associated Membrane Protein 7

DNAJB1:

DnaJ homolog subfamily B member 1

GABARAPL2:

Golgi-associated ATPase enhancer of 16 kDa

MAP2K7:

Mitogen-activated protein kinase kinase 7

PET-CT:

Positron Emission Tomography-Computed Tomography

HER2:

Human Epidermal Growth Factor Receptor 2

PD-L1:

Programmed Death 1

TCGA:

The Cancer Genome Atla

PCA:

Principal component analysi

GO:

Gene Ontology

References

  1. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68(6):394–424.

    PubMed  Google Scholar 

  2. Torre LA, Siegel RL, Ward EM, Jemal A. Global Cancer incidence and mortality rates and trends--an update. Cancer Epidemiol Biomark Prev. 2016;25(1):16–27.

    Google Scholar 

  3. Zhao JK, Wu M, Kim CH, Jin ZY, Zhou JY, Han RQ, Yang J, Zhang XF, Wang XS, Liu AM, et al. Jiangsu four cancers study: a large case-control study of lung, liver, stomach, and esophageal cancers in Jiangsu Province, China. Eur J Cancer Prev. 2017;26(4):357–64.

    PubMed  PubMed Central  Google Scholar 

  4. Engel LS, Chow WH, Vaughan TL, Gammon MD, Risch HA, Stanford JL, Schoenberg JB, Mayne ST, Dubrow R, Rotterdam H, et al. Population attributable risks of esophageal and gastric cancers. J Natl Cancer Inst. 2003;95(18):1404–13.

    PubMed  Google Scholar 

  5. Chen CX, Bai L, Liu ZG, Zhou K, Lu WW, Zhao QY. Finding esophageal cancer: could initial blood cell counts tell us? Int J Clin Exp Med. 2019;12(6):7820–6.

    CAS  Google Scholar 

  6. Turati F, Tramacere I, La Vecchia C, Negri E. A meta-analysis of body mass index and esophageal and gastric cardia adenocarcinoma. Ann Oncol. 2013;24(3):609–17.

    CAS  PubMed  Google Scholar 

  7. Smith JW, Moreira J, Abood G, Aranha GV, Nagda S, Wagner RH, Shoup M. The influence of (18) flourodeoxyglucose positron emission tomography on the management of gastroesophageal junction carcinoma. Am J Surg. 2009;197(3):308–12.

    PubMed  Google Scholar 

  8. Dreilich M, Wanders A, Brattstrom D, Bergstrom S, Hesselius P, Wagenius G, Bergqvist M. HER-2 overexpression (3+) in patients with squamous cell esophageal carcinoma correlates with poorer survival. Dis Esophagus. 2006;19(4):224–31.

    CAS  PubMed  Google Scholar 

  9. Le DT, Durham JN, Smith KN, Wang H, Bartlett BR, Aulakh LK, Lu S, Kemberling H, Wilt C, Luber BS, et al. Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade. Science (New York, NY). 2017;357(6349):409–13.

    CAS  Google Scholar 

  10. Shitara K, Ozguroglu M, Bang YJ, Di Bartolomeo M, Mandala M, Ryu MH, Fornaro L, Olesinski T, Caglevic C, Chung HC, et al. Pembrolizumab versus paclitaxel for previously treated, advanced gastric or gastro-oesophageal junction cancer (KEYNOTE-061): a randomised, open-label, controlled, phase 3 trial. Lancet (London, England). 2018;392(10142):123–33.

    CAS  Google Scholar 

  11. Chen CX, Ke HX, Shen XT, Yin M, Zhao QY. Risk factors of postoperative infection after McKeown esophagogastrectomy. Int J Clin Exp Med. 2018;11(5):5019–25.

    Google Scholar 

  12. Chen C, Wen T, Zhao Q. The change of laboratory tests could be predictive factors for infection after McKeown Esophagogastrectomy. Biomed Res Int. 2019;2019:9718705.

    PubMed  PubMed Central  Google Scholar 

  13. Dikic I, Johansen T, Kirkin V. Selective autophagy in cancer development and therapy. Cancer Res. 2010;70(9):3431–4.

    CAS  PubMed  Google Scholar 

  14. Macintosh RL, Ryan KM. Autophagy in tumour cell death. Semin Cancer Biol. 2013;23(5):344–51.

    CAS  PubMed  Google Scholar 

  15. Karsli-Uzunbas G, Guo JY, Price S, Teng X, Laddha SV, Khor S, Kalaany NY, Jacks T, Chan CS, Rabinowitz JD, et al. Autophagy is required for glucose homeostasis and lung tumor maintenance. Cancer Discov. 2014;4(8):914–27.

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Qu X, Yu J, Bhagat G, Furuya N, Hibshoosh H, Troxel A, Rosen J, Eskelinen EL, Mizushima N, Ohsumi Y, et al. Promotion of tumorigenesis by heterozygous disruption of the beclin 1 autophagy gene. J Clin Invest. 2003;112(12):1809–20.

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Rao S, Tortola L, Perlot T, Wirnsberger G, Novatchkova M, Nitsch R, Sykacek P, Frank L, Schramek D, Komnenovic V, et al. A dual role for autophagy in a murine model of lung cancer. Nat Commun. 2014;5:3056.

    PubMed  Google Scholar 

  18. Amaravadi R, Kimmelman AC, White E. Recent insights into the function of autophagy in cancer. Genes Dev. 2016;30(17):1913–30.

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Levy JM, Thorburn A. Targeting autophagy during cancer therapy to improve clinical outcomes. Pharmacol Ther. 2011;131(1):130–41.

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Levy JMM, Towers CG, Thorburn A. Targeting autophagy in cancer. Nat Rev Cancer. 2017;17(9):528–42.

    CAS  PubMed  Google Scholar 

  21. Towers CG, Thorburn A. Therapeutic targeting of autophagy. EBioMedicine. 2016;14:15–23.

    PubMed  PubMed Central  Google Scholar 

  22. Galluzzi L, Pietrocola F, Levine B, Kroemer G. Metabolic control of autophagy. Cell. 2014;159(6):1263–76.

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Langer R, Streutker CJ, Swanson PE. Autophagy and its current relevance to the diagnosis and clinical management of esophageal diseases. Ann N Y Acad Sci. 2016;1381(1):113–21.

    PubMed  Google Scholar 

  24. Siewert JR, Ott K. Are squamous and adenocarcinomas of the esophagus the same disease? Semin Radiat Oncol. 2007;17(1):38–44.

    PubMed  Google Scholar 

  25. Marino G, Niso-Santano M, Baehrecke EH, Kroemer G. Self-consumption: the interplay of autophagy and apoptosis. Nat Rev Mol Cell Biol. 2014;15(2):81–94.

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Mathew R, Karp CM, Beaudoin B, Vuong N, Chen G, Chen HY, Bray K, Reddy A, Bhanot G, Gelinas C, et al. Autophagy suppresses tumorigenesis through elimination of p62. Cell. 2009;137(6):1062–75.

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Lorin S, Hamai A, Mehrpour M, Codogno P. Autophagy regulation and its role in cancer. Semin Cancer Biol. 2013;23(5):361–79.

    CAS  PubMed  Google Scholar 

  28. Sui X, Chen R, Wang Z, Huang Z, Kong N, Zhang M, Han W, Lou F, Yang J, Zhang Q, et al. Autophagy and chemotherapy resistance: a promising therapeutic target for cancer treatment. Cell Death Dis. 2013;4:e838.

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Huang P, Sun LY, Zhang YQ. A hopeful natural product, Pristimerin, induces apoptosis, cell cycle arrest, and autophagy in esophageal Cancer cells. Anal Cell Pathol (Amst). 2019;2019:6127169.

    Google Scholar 

  30. Liu H, Zhao J, Fu R, Zhu C, Fan D. The ginsenoside Rk3 exerts anti-esophageal cancer activity in vitro and in vivo by mediating apoptosis and autophagy through regulation of the PI3K/Akt/mTOR pathway. PLoS One. 2019;14(5):e0216759.

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Shi Y, Zhang B, Feng X, Qu F, Wang S, Wu L, Wang X, Liu Q, Wang P, Zhang K. Apoptosis and autophagy induced by DVDMs-PDT on human esophageal cancer Eca-109 cells. Photodiagn Photodyn Ther. 2018;24:198–205.

    CAS  Google Scholar 

  32. Falvey CM, O'Donovan TR, El-Mashed S, Nyhan MJ, O'Reilly S, McKenna SL. UBE2L6/UBCH8 and ISG15 attenuate autophagy in esophageal cancer cells. Oncotarget. 2017;8(14):23479–91.

    PubMed  PubMed Central  Google Scholar 

  33. Cheng CY, Liu JC, Wang JJ, Li YH, Pan J, Zhang YR. Autophagy inhibition increased the anti-tumor effect of cisplatin on drug-resistant esophageal cancer cells. J Biol Regul Homeost Agents. 2017;31(3):645–52.

    CAS  PubMed  Google Scholar 

  34. Feng Y, Gao Y, Wang D, Xu Z, Sun W, Ren P. Autophagy inhibitor (LY294002) and 5-fluorouracil (5-FU) combination-based Nanoliposome for enhanced efficacy against esophageal squamous cell carcinoma. Nanoscale Res Lett. 2018;13(1):325.

    PubMed  PubMed Central  Google Scholar 

  35. Zheng K, Li Y, Wang S, Wang X, Liao C, Hu X, Fan L, Kang Q, Zeng Y, Wu X, et al. Inhibition of autophagosome-lysosome fusion by ginsenoside Ro via the ESR2-NCF1-ROS pathway sensitizes esophageal cancer cells to 5-fluorouracil-induced cell death via the CHEK1-mediated DNA damage checkpoint. Autophagy. 2016;12(9):1593–613.

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Yu L, Gu C, Zhong D, Shi L, Kong Y, Zhou Z, Liu S. Induction of autophagy counteracts the anticancer effect of cisplatin in human esophageal cancer cells with acquired drug resistance. Cancer Lett. 2014;355(1):34–45.

    CAS  PubMed  Google Scholar 

  37. Pei G, Luo M, Ni X, Wu J, Wang S, Ma Y, Yu J. Autophagy facilitates Metadherin-induced chemotherapy resistance through the AMPK/ATG5 pathway in gastric Cancer. Cell Physiol Biochem. 2018;46(2):847–59.

    CAS  PubMed  Google Scholar 

  38. Chen MC, Lin YC, Liao YH, Liou JP, Chen CH. MPT0G612, a Novel HDAC6 Inhibitor, Induces Apoptosis and Suppresses IFN-gamma-Induced Programmed Death-Ligand 1 in Human Colorectal Carcinoma Cells. Cancers. 2019;11(10). https://0-www-ncbi-nlm-nih-gov.brum.beds.ac.uk/pmc/articles/PMC6826904/.

  39. Che J, Wang W, Huang Y, Zhang L, Zhao J, Zhang P, Yuan X. miR-20a inhibits hypoxia-induced autophagy by targeting ATG5/FIP200 in colorectal cancer. Mol Carcinog. 2019;58(7):1234–47.

    CAS  PubMed  Google Scholar 

  40. Quan Y, Lei H, Wahafu W, Liu Y, Ping H, Zhang X. Inhibition of autophagy enhances the anticancer effect of enzalutamide on bladder cancer. Biomed Pharmacother. 2019;120:109490.

    CAS  PubMed  Google Scholar 

  41. Luan W, Pang Y, Li R, Wei X, Jiao X, Shi J, Yu J, Mao H, Liu P. Akt/mTOR-mediated autophagy confers resistance to BET inhibitor JQ1 in ovarian Cancer. Onco Targets Ther. 2019;12:8063–74.

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Zhang N, Li Z, Bai F, Zhang S. PAX5-induced upregulation of IDH1-AS1 promotes tumor growth in prostate cancer by regulating ATG5-mediated autophagy. Cell Death Dis. 2019;10(10):734.

    PubMed  PubMed Central  Google Scholar 

  43. Antonia RJ, Castillo J, Herring LE, Serafin DS, Liu P, Graves LM, Baldwin AS, Hagan RS. TBK1 limits mTORC1 by promoting phosphorylation of raptor Ser877. Sci Rep. 2019;9(1):13470.

    PubMed  PubMed Central  Google Scholar 

  44. Sarraf SA, Sideris DP, Giagtzoglou N, Ni L, Kankel MW, Sen A, Bochicchio LE, Huang CH, Nussenzweig SC, Worley SH, et al. PINK1/Parkin Influences Cell Cycle by Sequestering TBK1 at Damaged Mitochondria, Inhibiting Mitosis. Cell Rep. 2019;29(1):225–35 e225.

    CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

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Author notes

  1. Chongxiang Chen, Siliang Chen and Huijiao Cao contributed equally to this work.

Authors and Affiliations

  1. Department of Intensive Care Unit, State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, 651 Dongfeng Dong Road, Guangzhou, 510060, People’s Republic of China

    Chongxiang Chen & Huan Li

  2. Guangzhou Institute of Respiratory Diseases, State Key Laboratory of Respiratory Disease, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China

    Chongxiang Chen

  3. Department of hematology, State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China

    Siliang Chen & Xiaochun Hu

  4. Department of VIP Inpatient, State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China

    Huijiao Cao

  5. Department of Tuberculosis, Fuzhou Pulmonary Hospital of Fujian, Fuzhou, China

    Jiaojiao Wang

  6. School of Public Health, Sun Yat-sen University, Guangzhou, China

    Tianmeng Wen

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Contributions

HL and SLC designed the research protocol, analyzed data and revised the manuscript. CXC and SLC searched and analyzed the data. HJC and XCH wrote the manuscript and participated in analyzing data. JJW and TMW participated in searching the data. All authors have read and approved the manuscript.

Corresponding authors

Correspondence to Xiaochun Hu or Huan Li.

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All data were searched freely from Public Dataset based on the published literature. No permission was required. The following are the links of the related Dataset: Human autophagy database (http://www.autophagy.lu/), The Cancer Genome Atlas (https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga), Oncolnc (http://www.oncolnc.org/), Gene Ontology (http://geneontology.org/).

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Chen, C., Chen, S., Cao, H. et al. Prognostic significance of autophagy-related genes within esophageal carcinoma. BMC Cancer 20, 797 (2020). https://0-doi-org.brum.beds.ac.uk/10.1186/s12885-020-07303-4

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BMC Cancer

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