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An Independent Prognostic Model Based on Ten Autophagy-Related Long Noncoding RNAs in Pancreatic Cancer Patients

Published online by Cambridge University Press:  01 January 2024

Jiahui Tian
Affiliation:
Department of Laboratory, The First Affiliated Hospital of Hunan Normal University, Changsha, Hunan 410005, China Department of Medicine, Hunan Normal University, Changsha, Hunan 410005, China
Chunyan Fu
Affiliation:
Department of Medicine, Hunan Normal University, Changsha, Hunan 410005, China
Xuan Zeng
Affiliation:
Department of Medicine, Hunan Normal University, Changsha, Hunan 410005, China
Xiaoxiao Fan
Affiliation:
Department of Laboratory, The First Affiliated Hospital of Hunan Normal University, Changsha, Hunan 410005, China
Yi Wu*
Affiliation:
Department of Laboratory, The First Affiliated Hospital of Hunan Normal University, Changsha, Hunan 410005, China Department of Medicine, Hunan Normal University, Changsha, Hunan 410005, China
*
Correspondence should be addressed to Yi Wu; wuyi19701210@sina.com
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Abstract

Purpose. Pancreatic cancer (PC) is a common, highly lethal cancer with a low survival rate. Autophagy is involved in the occurrence and progression of PC. This study aims to explore the feasibility of using an autophagy-related long noncoding RNA (lncRNA) signature for assessing PC patient survival. Methods. We obtained RNA sequencing and clinical data of patients from the TCGA website. Autophagy genes were obtained from the Human Autophagy Database. The prognostic model, generated through univariate and multivariate Cox regression analyses, included 10 autophagy-related lncRNAs. Receiver operating characteristic (ROC) curves and forest plots were generated for univariate and multivariate Cox regression analyses, to examine the predictive feasibility of the risk model. Gene set enrichment analysis (GSEA) was used to screen enriched gene sets. Results. Twenty-eight autophagy-related lncRNAs were filtered out through univariate Cox regression analysis (P < 0.001). Ten autophagy-related lncRNAs, including 4 poor prognosis factors and 6 beneficial prognosis factors, were further screened via multivariate Cox regression analysis. The AUC value of the ROC curve was 0.815. GSEA results demonstrated that cancer-related gene sets were significantly enriched. Conclusion. A signature based on ten autophagy-related lncRNAs was identified. This signature could be potentially used for evaluating clinical prognosis and might be used for targeted therapy against PC.

Type
Research Article
Creative Commons
Creative Common License - CCCreative Common License - BY
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.
Copyright
Copyright © 2022 Jiahui Tian et al.

1. Introduction

Pancreatic cancer (PC) is a highly aggressive cancer that affects the digestive system. It has an incidence rate of 8% in 5 years and is the 11th most common malignant tumor [Reference Ilic and Ilic1, Reference Wu and Cai2]. It is difficult to diagnose PC at an early stage, owing to its insidious onset [Reference Zhang, Sanagapalli and Stoita3]. Currently, surgery is the only effective treatment available for patients with pancreatic cancer. However, pancreatic tumors are often characterized by a poor patient prognosis due to delayed diagnosis [Reference McGuigan, Kelly, Turkington, Jones, Coleman and McCain4, Reference Loveday, Lipton and Thomson5]. Although notable advances in the use of these technologies for PC screening and testing have been achieved, certain limitations are associated with the complete eradication of tumors. It is imperative to explore potent therapeutic targets and emerging prognostic biomarkers for the improved diagnosis and treatment of PC [Reference Wang, Lou and Ding6]. Autophagy is a cellular catabolic mechanism that facilitates the maintenance of cellular homeostasis through the degradation of cytoplasmic metabolites [Reference Kocaturk, Akkoc, Kig, Bayraktar, Gozuacik and Kutlu7, Reference Wang, Shao, Deng, Chen, Yue and Miao8]. Autophagy is involved in the progression of many cancers and may prevent or enhance tumor growth, depending on the tumor type and stage [Reference Amaravadi, Kimmelman and Debnath9, Reference Levy, Towers and Thorburn10]. Though autophagy may protect cells from harmful substances in the early stage of tumorigenesis, the adaptive metabolic response generated during autophagy results in nutritional conditions for tumor progression under an unfavorable environment [Reference Lim, Mohamad Hanif and Chin11]. Recent studies have focused on applications involving the use of autophagy genes for targeted anticancer therapy [Reference Chmurska, Matczak and Marczak12].

Long noncoding RNAs (lncRNAs) are transcripts with a length of more than 200 nucleotides that lack protein-coding functions. LncRNAs are involved in important biological processes, such as proliferation, differentiation, apoptosis, invasion, and metastasis, in patients with cancer [Reference Bermúdez, Aguilar-Medina and Lizárraga-Verdugo13, Reference Xu, Cui, Zhong and Cai14]. Recently, increasing levels of evidence indicate that lncRNAs are primarily involved in the occurrence and development of cancer, and this might also be related to their potential role in regulating autophagy [Reference Li, Jin and Li15, Reference Yang, Wang, Shen, Feng and Jin16]. Sun et al. determined the prognostic signature based on autophagy-related lncRNAs (ATRlncRNAs); this signature might potentially be used for targeted therapy and evaluation of the prognosis of colon cancer patients [Reference Sun, Cao, Guo, Liu and Zhang17, Reference Wu, Yin and Ji18]. Liu et al. investigated the clinical role of ATRlncRNAs in cholangiocarcinoma and demonstrated its potential for the personalization of cholangiocarcinoma treatment [Reference Liu, Hounye, Wang, Liu, Yi and Qi19]. Deng et al. proposed a six-ATRlncRNA signature that might be helpful for individualized therapy and PC patient assessment. Furthermore, LINC01559 may play a critical role in alleviating the resistance to gemcitabine [Reference Deng, Li, Shi, Lu, Yao and Wang20]. Feng et al. demonstrated the correlation between ATRlncRNAs and many cancers, including cervical cancer, breast cancer, glioma, and lung adenocarcinoma [Reference Feng, Wang, Cui, Liu and Wang21Reference Luan, Chen and Chen24]. Thus, the use of ATRlncRNAs as prognostic biomarkers for PC patients may provide new indicators for the early diagnosis and prognostic judgment of PC patients.

We comprehensively analyzed the correlation between RNA sequencing (RNA-seq) data and autophagy genes. Subsequently, we developed a prognostic signature based on ten ATRlncRNAs that were found in PC patients. Numerous biographical analysis results demonstrated that the ATRlncRNA signature could be extensively used in prospective applications, for molecular diagnosis, prognostic evaluation, and targeted treatment of PC patients.

2. Materials and Methods

2.1. Collection of Datasets and Clinical Information of PC Patients

The RNA-seq and clinical data of PC patients were acquired from the TCGA website (https://portal.gdc.cancer.gov/). Details regarding futime, fustat, age, sex, grade, stage, and tumor-node-metastasis classification were included in the clinical information. Incomplete clinical information was removed.

2.2. Selection of Autophagy-Related Genes of PC Patients

The autophagy genes (ATGs) were obtained from the Human Autophagy Database (https://www.autophagy.lu/index.html). The lncRNA expression levels were normalized by log2 transformation, using the edgeR package. Pearson’s correlation test was used for the screening of lncRNAs that were related to autophagy genes. The criteria used to identify the ATRlncRNAs were as follows: correlation coefficient |R 2| > 0.4 and P < 0.001. Cytoscape software (version 3.8.2) was used to construct coexpression networks.

2.3. Construction of the Prognostic Model

Univariate Cox regression analysis was used to identify prognostic ATRlncRNAs (P < 0.001). Then, multivariate Cox regression analysis was performed to optimize the risk model, and ten ATRlncRNAs were included in the formula to assess PC patient prognosis.

(1) Risk score = β expression of IncRNAs ,

where “β” is the regression coefficient for each gene.

Patients were split into two (low-risk and high-risk) subgroups, based on the median risk score. Kaplan-Meier survival curve analysis was applied to compare the differences in survival between these subgroups.

2.4. Feasibility of the Risk Model for Clinical Evaluation

First, to examine the independent predictive ability of the risk score and clinical factors, we constructed forest plots for univariate and multivariate Cox regression analyses. Subsequently, the receiver operating characteristic (ROC) curve was applied, to detect the validity of the risk assessment system, via a comparison with the area under the curve (AUC).

2.5. Functional Analysis

Gene set enrichment analysis (GSEA, https://www.broadinstitute.org/gsea/index.jsp) was used to screen deferentially expressed genes. It was used to analyze whether gene sets between the high and low expression groups differed during the process of autophagy.

3. Results

3.1. Construction of a Coexpression Network for ATRlncRNAs

A total of 182 lncRNAs were downloaded from TCGA, and a total of 232 autophagy-related genes were downloaded from the Human Autophagy Database (HADb, https://www.autophagy.lu/). Consequently, 176 PC patients and their complete clinicopathological information were sifted out for subsequent analysis.

3.2. Establishment of a Prognostic Model on ATRlncRNAs

The workflow revealed the detailed work of this study (Figure 1). The 28 prognostic ATRlncRNAs used for assessing survival were filtered using univariate Cox regression analysis (P < 0.001, Table 1). Besides, we further screened 10 prognostic ATRlncRNAs on the basis of the above 28 autophagy lncRNAs via multivariate Cox analysis. Of these, 4 lncRNAs were poor prognosis factors (AC245041.2, AC036176.1, LINC01089, and LINC02257) and 6 lncRNAs were beneficial prognosis factors (FLVCR1-DT, AC006504.7, AC125494.2, AC012306.2, ST20-AS1, and AC005696.1) (Table 2).

FIGURE 1: The workflow of this study.

TABLE 1: Univariate Cox analysis showed that 28 autophagy-related lncRNAs were significantly related to the survival of PC patients.

TABLE 2: Multivariate Cox regression analysis of 10 lncRNAs.

Accordingly, we established a coexpression network for the 10 ATRlncRNAs, to determine the interactions between the autophagy genes and prognosis-related lncRNAs (Figure 2). Based on the results shown in the Sankey diagram, the association between autophagy-related genes, prognosis-related lncRNAs, and related risk types has been derived, as shown in Figure 3. The Kaplan–Meier survival curve further indicated that the 10 ATRlncRNAs were closely related to a prognosis of PC (P < 0.001, Figures 4(a)4(j)).

FIGURE 2: Coexpression network for autophagy-related genes and ten independently diagnosed lncRNAs. The red rectangular nodes represent independently diagnosed lncRNAs, and the blue and purple round nodes represent autophagy-related genes.

FIGURE 3: The relationship between autophagy-related genes, independently diagnosed lncRNAs, and risk types was demonstrated in the Sankey diagram.

FIGURE 4: Kaplan–Meier survival curve of nine independently diagnosed lncRNAs in PC. Four lncRNAs were poor prognosis factors (AC245041.2 (B), AC036176.1 (G), LINC01089 (H), and LINC02257 (J)). Six lncRNAs were beneficial prognosis factors (FLVCR1-DT (A), AC006504.7 (C), AC125494.2 (D), AC012306.2 (E), ST20-AS1 (F), and AC005696.1 (I)).

3.3. Evaluation of the Prognostic ATRlncRNA Model

A risk model of prognostic ATRlncRNAs was established based on the risk score. Patients were divided into two groups, including the high-risk and low-risk groups. Patients with a higher overall survival (OS) in the low-risk group were better illustrated through the risk curve and scatter plot (P < 0.001) (Figures 5(a) and 5(b)). The construction of a heat map enabled the visualization of 10 differentially expressed prognostic lncRNAs, as shown in Figure 5(c). Furthermore, KM (Kaplan–Meier) survival analysis showed that the low-risk group had a better prognostic impact than the high-risk group (P=2.527e−11, Figure 6(a)). The ROC curve demonstrated in Figure 6(b) was used to evaluate the diagnostic value of the risk model. The AUC value for ATRlncRNAs was 0.815, which showed that the risk model exhibited potential for the evaluation of the prognosis of PC.

FIGURE 5: Risk score evaluation of a prognostic, autophagy-related lncRNA model of PC. (a) Risk score of the risk model. (b) The scatter plot reflects the survival period of PC patients. (c) Heat map demonstrating 10 differentially expressed prognostic lncRNAs is exhibited.

FIGURE 6: Prognostic impact of the risk signature. (a) Kaplan–Meier survival curve of the risk model. (b) Risk score of the ROC curve and other clinical factors based on the AUC. AUC, acute area under the curve.

3.4. Correlation Analysis of Clinical Characteristics and Risk Models of PC

To determine whether the risk model was an independent prognostic factor for PC survival analysis, univariate and multivariate Cox regression analyses were performed, as shown in Figures 7(a) and 7(b). The results of both these analyses revealed that the risk score could act as an effective prognostic factor (univariate regression: HR = 1.406, 95% CI = 1.295–1.526, P < 0.001; multivariate regression: HR = 1.422, 95% CI = 1.298–1.558, P < 0.001). Details regarding clinical factors, including age, sex, stage, and tumor-node-metastasis status, have been shown in Table 3. A significant difference was observed in the risk score.

FIGURE 7: Predictive performance evaluation of the risk model based on the risk score and clinical factors using the forest plots for univariate (a) and multivariate (b) Cox regression.

TABLE 3: Correlation analysis of clinical characteristics of PC patients and risk models.

3.5. Gene Set Enrichment Analysis

Based on GSEA results, deferentially expressed genes were screened out. Seven lncRNAs were upregulated in high-risk groups at an FDR <0.05 and a nominal P value <0.01. Several sets, including those for cell migration, ZEB1 targets, EGFR signaling, lin genes silenced by the tumor microenvironment, and CDH1 targets (Figure 8), were all closely linked to cancer. These results give rise to the possibility that the diagnosis and treatment of PC could be achieved using this method.

FIGURE 8: GSEA analysis of deferentially expressed genes.

4. Discussion

Pancreatic cancer (PC) remains one of the most aggressive cancers worldwide. The mortality rate of PC tends to be similar to its morbidity rate despite the development of advanced treatment techniques [Reference Armstrong, Beal and Chakedis25, Reference Hcabc, Qzabc, Zeng, Xxabc and Sjabc26]. Recent studies have mainly focused on the use of biomarkers, such as lncRNAs, for targeted therapies in PC; this may drive the ability to provide customized treatment and optimize the therapeutic effects [Reference Wei, Liang and Li27Reference Giulietti, Righetti, Principato and Piva29]. Autophagy has positive and negative regulatory effects on PC, based on the setting and stage [Reference Görgülü, Diakopoulos and Kaya-Aksoy30]. Emerging evidence has proven that lncRNAs may serve as prognostic and diagnostic biomarkers during the initiation and development of PC [Reference Giulietti, Righetti, Principato and Piva29, Reference Newhook, Blais and Lindberg31]. Lou et al. discovered that lncRNA HULC participates in the Wnt/β-catenin signaling pathway in pancreatic cancer cells and could be considered as an effective biomarker for the diagnosis of PC [Reference Ou, Luo and Lu32]. However, there have been few reports on the selection of autophagy-related lncRNAs for the assessment of PC prognosis. Consequently, we developed a risk model for evaluating the survival of PC patients through the screening of autophagy-related lncRNAs.

Based on the formed coexpression network, we selected autophagy-related lncRNAs for assessing survival in PC patients. Second, 10 autophagy-related lncRNAs, including AC245041.2, AC036176.1, LINC01089, LINC02257, FLVCR1-DT, AC006504.7, AC125494.2, AC012306.2, ST20-AS1, and AC005696.1 were obtained through Cox regression analysis. Future studies need to assess whether these 10 risk factors can be used for prognostic assessment of PC.

Eight autophagy-related lncRNAs, including AC245041.2, AC036176.1, LINC01089, LINC02257, FLVCR1-DT, AC125494.2, AC012306.2, and ST20-AS1, were found to be involved in cancer prognosis. There have been no detailed reports on the role of AC006504.7 and AC005696 in cancer risk assessment to date. (1) The lncRNA AC245041.2 and mRNA LAMA3, which are strongly correlated with each other, are relevant to the detection of KRAS mutations, which may be indicative of a poor prognosis of PC [Reference Tian, Xiyao and Chunlin33]. Cao et al. screened out multiple autophagy-associated lncRNAs, including AC245041.2, based on bioinformatic analyses, to develop a risk model for the prognostic analysis of PC [Reference Deng, Li, Shi, Lu, Yao and Wang20]. ApoL1 is one of the interacting autophagy-related genes that express AC245041.2. When ApoL1 was overexpressed intracellularly, it could induce autophagy and autophagy-associated cell death in all cell types. In contrast, when the ratio of ApoL6 : ApoL1 was elevated, it could promote apoptosis via the inhibition of autophagic signals [Reference Hu, Klopfer and Ray34]. (2) AC036176.1, a ferroptosis-related lncRNA, may act as a prognostic biomarker for PC [Reference Chen, Gao and Zang35]. ATG16L1 is one of the interacting ATGs of AC036176.1; the stimulation of Mir223 could downregulate the levels of ATG16L1 below the threshold level, therefore resulting in the inhibition of autophagic activity. This establishes ATG16L1 as an important target of Mir223, which is closely involved in the autophagy process [Reference Li, Zhou and Ren36]. (3) LINC01089 was proven to serve as an influential factor that affected cancer development [Reference Zhu, Zhao, Dong, Liu, Tai and Xu37]. (4) LINC02257 was expressed at high levels in colorectal cancer patients and seemed to act as a hazard factor [Reference Huang, Cai, Yuan and Peng38]. Xu et al. noted that LINC02257 and FLVCR1-DT were utilized as an adverse lncRNA and favorable lncRNA, respectively, for monitoring the model-based prognosis prediction of PC patients [Reference Xu, Wang and Huang39]. RPTOR is one of the ATGs interacting with LINC02257; a study found that the expression of miR-377-3p could target RPTOR and induce autophagy in vivo and in vitro [Reference Wei, Yi and Lv40]. (5) AC125494.2 proteins were also reported to act as factors that were beneficial for assessing PC patient survival [Reference Deng, Li, Shi, Lu, Yao and Wang20]. (6) Shen et al. predicted that AC012306.2 is positively correlated with the occurrence of cervical cancer [Reference Shen, Yu and Liu41]. (7) The immune-related lncRNA ST20-AS1 model was established in patients with anaplastic gliomas [Reference Meng, Quan and Liu42].

Ten autophagy-related lncRNAs were filtered to develop a risk signature for prognostic identification in PC patients. The results of univariate and multivariate Cox analyses further illustrated their reliability and demonstrated that the ten autophagy-related lncRNAs acted as prognostic factors that could predict overall survival in the model. Subsequently, on the basis of the coexpression network and Sankey diagram, the relationship between the mRNAs and lncRNAs was visualized. The AUC value was 0.815, which illustrated the feasibility of using the model for predicting the prognosis of PC patients.

GSEA analysis suggested that the gene set was primarily centered on pathways associated with tumor progression, metastasis, and cell migration. For example, in the two crucial gene sets Zeb1 and EGFR, Zeb1 is identified as one of the key EMT genes, and its overexpression is linked to tumor metastasis [Reference Bhagwat, Hamm and Hsu43]. EGFR has received extensive attention from researchers focused on the field of targeted anticancer therapy. The downregulation of EGFR signaling via CDF compounds upregulating miR-146a might provide a new therapeutic option against PC [Reference Ali, Ahmad and Aboukameel44]. EGFR is associated with resistance to conventional cancer therapy; resistance to EGFR-targeted therapy can be attenuated via autophagy inhibition and thus represent a new mode of tumor treatment [Reference Jutten and Rouschop45].

Certain limitations are associated with this study. (1) The database is a single-source database and the study data are limited to 182 cases; the documentation of the clinical features is also incomplete. (2) Additional validation studies, including those involving an independent cohort, need to be conducted, to verify the potential of the prognostic model for the purpose of making assessments. (3) In this study, only data analysis was performed; the explicit function of autophagy-related lncRNAs in disease prognosis was not validated by performing relevant experiments.

In conclusion, ten autophagy-related lncRNAs were used to determine a prognostic signature for PC patients. The prognostic factors in the signature may facilitate the development of novel methods for targeted therapy and clinical evaluation of PC.

Data Availability

The RNA-seq and clinical data can be obtained from the TCGA website; the autophagy genes (ATGs) are available at the Human Autophagy Database. The standardized data used for bioinformatics analysis are included within the supplementary information files.

Conflicts of Interest

The authors state that there are no conflicts of interest to declare.

Authors’ Contributions

TJ provided the experimental design, processed the data, and prepared the manuscript. FC analyzed the data and did revision. FX and ZX did format modification. WY did article revision and research design.

Acknowledgments

The authors express their sincere gratitude to all the contributors who provided data to them, and the authors appreciate the fact that the TCGA database is available for free download and use. A preprint has been published previously at https://www.researchsquare.com/article/rs-634359/v1 [Reference Tian, Wu, Zeng, Fang and Fu46]. The funding was provided by Hunan Provincial Health Commission (No. 202202082769) and Science and Technology Program of Changsha City (Nos. KQ 1907130).

Supplementary Materials

Table S1. The RNA-seq data from the TCGA website. Table S2. Clinical data from the TCGA website. Table S3. The core results showed a significant correlation between autophagy-related genes and lncRNAs. Table S4. The expression of 28 autophagy-related lncRNAs. Table S5. The risk data used for the plotting of the heat map, risk curve, and scatter plot for KM survival analysis.

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Figure 0

FIGURE 1: The workflow of this study.

Figure 1

TABLE 1: Univariate Cox analysis showed that 28 autophagy-related lncRNAs were significantly related to the survival of PC patients.

Figure 2

TABLE 2: Multivariate Cox regression analysis of 10 lncRNAs.

Figure 3

FIGURE 2: Coexpression network for autophagy-related genes and ten independently diagnosed lncRNAs. The red rectangular nodes represent independently diagnosed lncRNAs, and the blue and purple round nodes represent autophagy-related genes.

Figure 4

FIGURE 3: The relationship between autophagy-related genes, independently diagnosed lncRNAs, and risk types was demonstrated in the Sankey diagram.

Figure 5

FIGURE 4: Kaplan–Meier survival curve of nine independently diagnosed lncRNAs in PC. Four lncRNAs were poor prognosis factors (AC245041.2 (B), AC036176.1 (G), LINC01089 (H), and LINC02257 (J)). Six lncRNAs were beneficial prognosis factors (FLVCR1-DT (A), AC006504.7 (C), AC125494.2 (D), AC012306.2 (E), ST20-AS1 (F), and AC005696.1 (I)).

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FIGURE 5: Risk score evaluation of a prognostic, autophagy-related lncRNA model of PC. (a) Risk score of the risk model. (b) The scatter plot reflects the survival period of PC patients. (c) Heat map demonstrating 10 differentially expressed prognostic lncRNAs is exhibited.

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FIGURE 6: Prognostic impact of the risk signature. (a) Kaplan–Meier survival curve of the risk model. (b) Risk score of the ROC curve and other clinical factors based on the AUC. AUC, acute area under the curve.

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FIGURE 7: Predictive performance evaluation of the risk model based on the risk score and clinical factors using the forest plots for univariate (a) and multivariate (b) Cox regression.

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TABLE 3: Correlation analysis of clinical characteristics of PC patients and risk models.

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FIGURE 8: GSEA analysis of deferentially expressed genes.

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