Comprehensive analysis of the clinical significance and prospective molecular mechanisms of differentially expressed autophagy-related genes in thyroid cancer

The role of DAPK2 as a key regulatory element in various human cancers: a systematic review

Cancer is considered the uncontrolled growth and spread of cells into neighboring tissues, a process governed at the molecular level by many different factors, including abnormalities in the protein family's death-associated kinase (DAPK). DAPK2 is a member of the DAPK protein family, which plays essential roles in several cellular processes. DAPK2 acts as a tumor suppressor, interacting with several proteins, such as TNF, IFN, etc. during apoptosis and autophagy. Expression of DAPK2 causes changes in the structure of the cell, ultimately leading to cell death by apoptosis. In this essay, studies are obtained from Scopus, PubMed, and the Web of Science. According to these investigations, DAPK2 activates autophagy by interacting with AMPK, mTORC1, and p73. Furthermore, DAPK2 induces apoptosis pathway via interacting with the p73 family and JNK. In general, due to the vital role of DAPK2 in cell physiology and its effect on various factors and signaling pathways, it can be a potent target in the treatment of various cancers, including gastric, ovarian, breast, and other prominent cancers.

Identification and Validation of a Prognostic Signature for Thyroid Cancer Based on Ferroptosis-Related Genes

Background: Thyroid cancer is the most common endocrine malignancy. Most PTC patients have a good prognosis; however, there are 5–20% of PTC patients with extra-thyroidal invasion, vascular invasion, or distant metastasis who have relatively poor prognoses. The aim of this study is to find new and feasible molecular pathological markers and therapeutic targets for early identification and appropriate management. Methods: The GEO and TCGA databases were used to gather gene expression data and clinical outcomes. Based on gene expression and clinical parameters, we developed a ferroptosis-related gene-based prognostic model and a nomogram. CCK-8, wound-healing, and transwell assays were conducted to explore the proliferation, migration, and invasion abilities of thyroid cancer cells. Results: We found 75 genes associated with ferroptosis that were differentially expressed between normal thyroid tissue and thyroid cancer tissues. The prognostic values of the 75 ferroptosis-related gene expressions were evaluated using the TCGA-THCA dataset, and five (AKR1C3, BID, FBXW7, GPX4, and MAP3K5) of them were of significance. Following that, we chose AKR1C3 as the subject for further investigation. By combining gene expression and clinical parameters, we developed a ferroptosis-related gene-based prognostic model with an area under the curve (AUC) of 0.816, and the nomogram also achieved good predictive efficacy for the three-year survival rate of thyroid cancer patients. Knocking down AKR1C3 enhances thyroid cancer cell proliferation, invasion, and migration abilities. Conclusions: A ferroptosis-related gene-based prognostic model was constructed that provided unique insights into THCA prognosis prediction. In addition, AKR1C3 was found to be a progression promoter in thyroid cancer cell lines.

A Novel Four Genes of Prognostic Signature for Uveal Melanoma

Autophagy and immunity play critical roles in various cancers, but the prognostic impact of autophagy and immunity for uveal melanoma (UM) remains lacking. Therefore, the RNA sequencing of data in the TCGA-UVM dataset was downloaded from UCSC Xena database. The prognostic autophagy- and immunity-related genes (AIRGs) were selected via univariate Cox regression. Next, we applied LASSO method to construct four genes of signature in the TCGA-UVM and verified in another two GEO datasets (GSE84976 and GSE22138). This signature intimately associated with overall survival (OS) time and metastasis-free survival (MFS) time of UM, which could be considered as a prognostic indicator. Besides, by applying risk assessment, the patients of UM can be divided into two subgroups (high/low risk) with different survival time, distinct clinical outcomes, and immune microenvironments. Gene set enrichment analysis (GSEA) manifested that cancer hallmark epithelial-mesenchymal transition and KRAS pathways were positively activated in the high-risk group. Moreover, the high-risk group could be more sensitive to chemotherapies than the low-risk group. Thus, our finding suggested that the four genes of signature closely linked with UM risk and survival can afford more accurate survival prediction and potential therapeutic targets for clinical application.

Identification of a Five Autophagy Subtype-Related Gene Expression Pattern for Improving the Prognosis of Lung Adenocarcinoma

Background: Autophagy plays an important role in lung adenocarcinoma (LUAD). In this study, we aimed to explore the autophagy-related gene (ARG) expression pattern and to identify promising autophagy-related biomarkers to improve the prognosis of LUAD.

Methods: The gene expression profiles and clinical information of LUAD patients were downloaded from the Cancer Genome Atlas (TCGA), and validation cohort information was extracted from the Gene Expression Omnibus database. The Human Autophagy Database (HADb) was used to extract ARGs. Gene expression data were analyzed using the limma package and visualized using the ggplot2 package as well as the pheatmap package in R software. Functional enrichment analysis was also performed for the differentially expressed ARGs (DEARGs). Then, consensus clustering revealed autophagy-related tumor subtypes, and differentially expressed genes (DEGs) were screened according to the subtypes. Next, the univariate Cox and multivariate Cox regression analyses were used to identify independent prognostic ARGs. After overlapping DEGs and the independent prognostic ARGs, the predictive risk model was established and validated. Correlation analyses between ARGs and clinicopathological variables were also explored. Finally, the TIMER and TISIDB databases were used to further explore the correlation analysis between immune cell infiltration levels and the risk score as well as clinicopathological variables in the predictive risk model.

Results: A total of 222 genes from the HADb were identified as ARGs, and 28 of the 222 genes were pooled as DEARGs. The most significant GO term was autophagy (p = 3.05E-07), and KEGG analysis results indicated that 28 DEARGs were significantly enriched in the ErbB signaling pathway (p < 0.001). Then, consensus clustering analysis divided the LUAD into two clusters, and a total of 168 DEGs were identified according to cluster subtypes. Then univariate and multivariate Cox regression analyses were used to identify 12 genes that could serve as independent prognostic indicators. After overlapping 168 DEGs and 12 genes, 10 genes (ATG4A, BAK1, CAPNS1, CCR2, CTSD, EIF2AK3, ITGB1, MBTPS2, SPHK1, ST13) were selected for the further exploration of the prognostic pattern. Survival analysis results indicated that this risk model identified the prognosis (p = 4.379E-10). Combined with the correlation analysis results between ARGs and clinicopathological variables, five ARGs were screened as prognostic genes. Among them, SPHK1 expression levels were positively correlated with CD4⁺ T cells and dendritic cell infiltration levels.

Conclusions: In this study, we constructed a predictive risk model and identified a five autophagy subtype-related gene expression pattern to improve the prognosis of LUAD. Understanding the subtypes of LUAD is helpful to accurately characterize the LUAD and develop personalized treatment.

An autophagy-related lncRNA prognostic risk model for thyroid cancer

PURPOSE

Thyroid cancer (TC) is the most common malignancy of the endocrine system and its incidence is gradually rising. Research has demonstrated a close link between autophagy and thyroid cancer. We constructed a prognostic model of autophagy-related long non-coding RNA (lncRNA) in thyroid cancer and explored its prognostic value.

METHODS

The data used in this study were all obtained from The Cancer Genome Atlas (TCGA) database and the Human Autophagy Database (HADb). We construct a co-expression network by autophagy-related genes and lncRNA to obtain autophagy-related lncRNAs. After univariate Cox regression analysis and multivariate Cox regression analysis, autophagy-related lncRNAs significantly associated with prognosis were identified. Based on the risk score of lncRNA, thyroid cancer patients are divided into high-risk group and low-risk group.

RESULTS

A total of 14,142 lncRNAs and 212 autophagy-related genes (ATGs) were obtained from the TCGA database and the HADb, respectively. We performed lncRNA-ATGs correlation analysis and finally obtained 1,166 autophagy-associated lncRNAs. Subsequently, we conducted univariate Cox regression analysis and multivariate Cox regression analysis, nine autophagy-related lncRNAs (AC092279.1, AC096677.1, DOCK9-DT, LINC02454, AL136366.1, AC008063.1, AC004918.3, LINC02471 and AL162231.2) significantly associated with prognosis were identified. Based on these autophagy-related lncRNAs, a risk model was constructed. The area under the curve (AUC) of the risk score was 0.905, proving that the accuracy of risk signature was superior. In addition, multiple regression analysis showed that risk score was a significant independent prognostic risk factor for thyroid cancer.

CONCLUSION

In this study, nine autophagy-related lncRNAs in thyroid cancer were established to predict the prognosis of thyroid cancer patients.

Machine learning identifies two autophagy-related genes as markers of recurrence in colorectal cancer

OBJECTIVE

Colorectal cancer (CRC) is the most common cancer worldwide. Patient outcomes following recurrence of CRC are very poor. Therefore, identifying the risk of CRC recurrence at an early stage would improve patient care. Accumulating evidence shows that autophagy plays an active role in tumorigenesis, recurrence, and metastasis.

METHODS

We used machine learning algorithms and two regression models, univariable Cox proportion and least absolute shrinkage and selection operator (LASSO), to identify 26 autophagy-related genes (ARGs) related to CRC recurrence.

RESULTS

By functional annotation, these ARGs were shown to be enriched in necroptosis and apoptosis pathways. Protein-protein interactions identified SQSTM1, CASP8, HSP80AB1, FADD, and MAPK9 as core genes in CRC autophagy. Of 26 ARGs, BAX and PARP1 were regarded as having the most significant predictive ability of CRC recurrence, with prediction accuracy of 71.1%.

CONCLUSION

These results shed light on prediction of CRC recurrence by ARGs. Stratification of patients into recurrence risk groups by testing ARGs would be a valuable tool for early detection of CRC recurrence.

Identification and Validation of a Prognostic Model Based on Three Autophagy-Related Genes in Hepatocellular Carcinoma

Background

Accumulating studies have demonstrated that autophagy plays an important role in hepatocellular carcinoma (HCC). We aimed to construct a prognostic model based on autophagy-related genes (ARGs) to predict the survival of HCC patients.

Methods

Differentially expressed ARGs were identified based on the expression data from The Cancer Genome Atlas and ARGs of the Human Autophagy Database. Univariate Cox regression analysis was used to identify the prognosis-related ARGs. Multivariate Cox regression analysis was performed to construct the prognostic model. Receiver operating characteristic (ROC), Kaplan-Meier curve, and multivariate Cox regression analyses were performed to test the prognostic value of the model. The prognostic value of the model was further confirmed by an independent data cohort obtained from the International Cancer Genome Consortium (ICGC) database.

Results

A total of 34 prognosis-related ARGs were selected from 62 differentially expressed ARGs identified in HCC compared with noncancer tissues. After analysis, a novel prognostic model based on ARGs (PRKCD, BIRC5, and ATIC) was constructed. The risk score divided patients into high- or low-risk groups, which had significantly different survival rates. Multivariate Cox analysis indicated that the risk score was an independent risk factor for survival of HCC after adjusting for other conventional clinical parameters. ROC analysis showed that the predictive value of this model was better than that of other conventional clinical parameters. Moreover, the prognostic value of the model was further confirmed in an independent cohort from ICGC patients.

Conclusion

The prognosis-related ARGs could provide new perspectives on HCC, and the model should be helpful for predicting the prognosis of HCC patients.

An autophagic gene-based signature to predict the survival of patients with low-grade gliomas

Background

Since autophagy remains an important topic of investigation, the RNA‐sequence profiles of autophagy‐related genes (ARGs) can provide insights into predicting low‐grade gliomas (LGG) prognosis.

Methods

The RNA‐seq profiles of autophagic genes and prognosis data of LGG were integrated from the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA). Univariate Cox analysis and the least absolute shrinkage and selection operator (LASSO) regression model were carried out to identify the differentially expressed prognostic autophagy‐related genes. Then, the autophagic‐gene signature was formed and verified in TCGA test set and external CGGA cohorts. Time‐dependent receiver operating characteristic (ROC) was examined to test the accuracy of this signature feature. A nomogram was conducted to meet the needs of clinicians. Sankey diagrams were performed to visualize the relationship between the multigene signatures and clinic‐pathological features.

Results

Twenty‐four ARGs were finally identified most relevant to LGG prognosis. According to the specific prediction index formula, the patients were classified into low‐risk or high‐risk groups. Prognostic accuracy was proved by time‐dependent ROC analysis, with AUC 0.9, 0.93, and 0.876 at the survival time of 2‐, 3‐, and 5‐year, respectively, which was superior to the AUC of the isocitrate dehydrogenase (IDH) mutation. The result was confirmed while validated in the TCGA test set and external validation CGGA cohort. A nomogram was constructed to meet individual needs. With a visualization approach, Sankey diagrams show the relationship of the histological type, IDH status, and predict index. In TCGA and CGGA cohorts, both low‐risk groups displayed better survival rate in LGG while histological type and IDH status did not show consistency results.

Conclusions

24‐ARGs may play crucial roles in the progression of LGG, and LGG patients were effectively divided into low‐risk and high‐risk groups according to prognostic prediction. Overall, our study will provide novel strategies for clinical applications.

Identification of Three Autophagy-Related Long Non-Coding RNAs as a Novel Head and Neck Squamous Cell Carcinoma Prognostic Signature

Head and neck squamous cell carcinoma (HNSCC) has a poor prognosis. Considerable evidence indicates that autophagy and non-coding RNA play essential roles in the biological processes involved in cancers, but associations between autophagy-related long non-coding RNAs (lncRNAs) and HNSCC remain unclear. In the present study, HNSCC RNA sequences and autophagy-related gene data were extracted from The Cancer Genome Atlas database and the Human Autophagy Database. A total of 1,153 autophagy-related lncRNAs were selected via calculating Pearson’s correlation coefficient. Three prognosis-related autophagy lncRNAs were identified via univariate Cox regression, least absolute shrinkage and selection operator analysis, and multivariate Cox regression analysis. We also constructed a prognostic model based on these autophagy-related lncRNAs and evaluated its ability to accurately and independently predict the prognosis of HNSCC patients. The area under the curve (AUC) was 0.864 (3-year) and 0.836 (5-year), and our model can independently predict the prognosis of HNSCC. The prognostic value of the three autophagy lncRNAs was confirmed via analysis of samples from five databases. To further identify the functions of the three lncRNAs, a co-expression network was constructed and pathway analysis was performed. In that analysis the lncRNAs were correlated with 189 related genes and 20 autophagy-related genes, and these lncRNAs mainly involved homologous recombination, the Fanconi anemia pathway, the autophagy-related pathway, and immune-related pathways. In addition, we validated the expression levels of three lncRNAs and autophagy markers (ATG12, BECN1, and MAP1LC3B) based on TIMER, Oncomine, and HPA database analysis. Our results indicated that TTTY15 was increased in HPV positive and HPV negative HNSCC patients, and three autophagy markers were up-regulated in all HNSCCC patients. Lastly, association between three lncRNAs and autophagy markers was performed, and our results showed that TTTY15 and MIF-AS1 were associated with autophagy markers. Collectively, these results suggested that three autophagy-related lncRNAs have prognostic value in HNSCC patients.

MicroRNA‑15b‑5p exerts its tumor repressive role via targeting GDI2: A novel insight into the pathogenesis of thyroid carcinoma

Thyroid carcinoma (THCA) is a malignant tumor of the endocrine system. Previous studies have revealed the vital roles of microRNAs (miRNAs/miRs) in THCA procession. The present study aimed to explore the effects of miR-15b-5p on the progression of THCA and its targeting mechanism. The data of THCA and healthy samples were firstly collected from starbase2.0 and used to analyze the relationship of miR-15b-5p with THCA. Dual-luciferase assay was performed to detect the direct interaction between miR-15b-5p and the predicted target gene GDP dissociation inhibitor 2 (GDI2). The effects of miR-15b-5p and GDI2 on the overall survival of patients with THCA were analyzed using Kaplan-Meier analysis with log rank test. Cell Counting Kit-8 and Transwell assays were conducted to assess the impacts of miR-15b-5p and GDI2 on the proliferation and invasion of THCA cells. Reverse transcription-quantitative PCR and western blot analyses were performed to analyze the expression levels of the related miRNAs and proteins, respectively. miR-15b-5p was found to be downregulated both in THCA tissues and cells, and the low expression of miR-15b-5p was associated with the short overall survival time of patients. Moreover, the upregulation or downregulation of miR-15b-5p could inhibit or enhance the proliferation and invasion of THCA cells, respectively. miR-15b-5p reduced the protein expression levels of matrix metalloproteinase (MMP)2 and MMP9, which were related to cell invasion. Furthermore, GDI2, which was enhanced in THCA and related to the poor prognosis of patients with THCA, was identified as the target gene of miR-15b-5p and negatively regulated by miR-15b-5p. Additional experiments demonstrated that GDI2 overexpression could significantly reduce the antitumor effect of miR-15b-5p and its inhibitory action on the expression levels of MMP2 and MMP9. Thus, the results indicated a potential tumor suppressive role of miR-15b-5p in THCA, which was mainly exerted by targeting GDI2 and modulating MMP2 and MMP9. These findings will increase the understanding on the pathogenesis of THCA and provide novel candidates for THCA therapy.

Downregulation of miR‑193a‑3p via targeting cyclin D1 in thyroid cancer

Thyroid cancer (TC) is a frequently occurring malignant tumor with a rising steadily incidence. microRNA (miRNA/miR)‑193a‑3p is an miRNA that is associated with tumors, playing a crucial role in the genesis and progression of various cancers. However, the expression levels of miR‑193a‑3p and its molecular mechanisms in TC remain to be elucidated. The present study aimed to probe the expression of miR‑193a‑3p and its clinical significance in TC, including its underlying molecular mechanisms. Microarray and RNA sequencing data gathered from three major databases, specifically Gene Expression Omnibus (GEO), ArrayExpress and The Cancer Genome Atlas (TCGA) databases, and the relevant data from the literature were used to examine miR‑193a‑3p expression. Meta‑analysis was also conducted to evaluate the association between clinicopathological parameters and miR‑193a‑3p in 510 TC and 59 normal samples from the TCGA database. miRWalk 3.0, and the TCGA and GEO databases were used to predict the candidate target genes of miR‑193a‑3p. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes and protein‑protein interaction network enrichment analyses were conducted by using the predicted candidate target genes to investigate the underlying carcinogenic mechanisms. A dual luciferase assay was performed to validate the targeting regulatory association between the most important hub gene cyclin D1 (CCND1) and miR‑193a‑3p. miR‑193a‑3p expression was considerably downregulated in TC compared with in the non‑cancer controls (P<0.001). The area under the curve of the summary receiver operating characteristic was 0.80. Downregulation of miR‑193a‑3p was also significantly associated with age, sex and metastasis (P=0.020, 0.044 and 0.048, respectively). Bioinformatics analysis indicated that a low miR‑193a‑3p expression may augment CCND1 expression to affect the biological processes of TC. In addition, CCND1, as a straightforward target, was validated through a dual luciferase assay. miR‑193a‑3p and CCND1 may serve as prognostic biomarkers of TC. Finally, miR‑193a‑3p may possess a crucial role in the genesis and progression of TC by altering the CCND1 expression.

Clinical Significance and Potential Regulatory Mechanisms of Serum Response Factor in 1118 Cases of Thyroid Cancer Based on Gene Chip and RNA-Sequencing Data

Background

Thyroid cancer (TC) is one of the most prevalent endocrine malignancies and there may be many unclarified molecular events and gene types involved in TC. The objective of this study was to assess the clinical implications and potential mechanisms of serum response factor (SRF) in TC.

Material/Methods

RNA-sequencing and gene chip data with TC expression were collected from The Cancer Genome Atlas/Genotype-Tissue Expression, Gene Expression Omnibus, ArrayExpress, Sequence Read Archive, and Oncomine. SRF expression of all TC and adjacent non-cancerous tissue were calculated using the t test, STATA, and Meta-DiSc. The related pathways of the potential SRF target genes and target miRNAs were explored. Dual-luciferase reporter assay was performed to validate the association between SRF and its putative miRNA.

Results

One RNA-sequencing and 15 gene chips were collected, and the pooled standardized mean difference of SRF was −1.00. Furthermore, the area under the curve of sROC of SRF in TC was 0.8251, indicating a dramatic decreased expression of SRF in TC tissues based on 1118 cases. The intersection of differentially expressed genes in TC, SRF co-expressed genes, and SRF potential target genes achieved from Cistrome Cancer led to 169 overlapped genes. miR-330-5p was predicted to target SRF, which was further confirmed by dual-luciferase reporter assay.

Conclusions

The reduction of SRF appears to play a crucial role in the origin of TC. These properties are accomplished by the target genes of SRF, as a transcription factor, or by the axes with the associated miRNAs.

Clinical roles of miR-136-5p and its target metadherin in thyroid carcinoma

BACKGROUND

Thyroid carcinoma (TC) is a common malignancy of the endocrine system. This research aimed to examine the expression levels of miR-136-5p and metadherin (MTDH) in TC and unveil their potential targeting relationship.

METHODS

TC microRNA (miRNA) microarray and miRNA-sequencing data were collected to evaluated miR-136-5p expression. We assessed the comprehensive expression of miR-136-5p by calculating the standard mean difference (SMD) and summary receiver operating characteristic curves (sROC). Subsequently, the miR-136-5p mimic and inhibitor were transfected into the TC B-CPAP cell, Thiazolyl Blue Tetrazolium Bromide (MTT) assay and cell apoptosis assay by FACS with Annexin V-/7-AAD double staining were performed to explore the biological role of miR-136-5p in the B-CPAP cell line. Prediction of target genes and potential biological function analysis of miR-136-5p were made using miRWalk2.0 and DAVID, respectively. Through target gene prediction, MTDH may be the candidate target gene of miR-136-5p. Subsequently, gene microarrays and RNA-sequencing data were also leveraged for MTDH expression. The meta-analysis method was conducted to evaluate the comprehensive expression level of MTDH. In addition, MTDH protein expression was identified using immunohistochemistry. The MTDH protein levels post-miR-136-5p transfection were verified by western blot, and the dual luciferase reporter assay was adapted to confirm the direct targeting relation between miR-136-5p and MTDH.

RESULTS

The miR-136-5p level was remarkably downregulated in TC, the pooled SMD was -0.47 (95% CI: -0.70 to -0.23, I²=36.6%, P=0.192) and the area under the curve (AUC) of the sROC was 0.67 based on 543 cases of TC. MTT indicated that the overexpression of miR-136-5p dramatically inhibited the proliferation of B-CPAP cells. The cell apoptosis increased in the miR-136-5p mimic group compared to the negative control group. In addition, both MTDH mRNA and protein levels were markedly overexpressed, with the pooled SMD being 0.94 (95% CI: -0.35 to 2.24, I²=98.8%, P<0.001), and the AUC of the sROC being 0.85 with 1054 cases of TC. The MTDH protein level was significantly up-regulated in TC than in the non-carcinomic tissues by immunohistochemistry (8.292±1.717 vs. 2.618±2.570, P<0.001). Western blot indicated that MTDH protein expression was suppressed by miR-136-5p mimic in the B-CPAP cell line, which was further supported by the dual luciferase reporter assay.

CONCLUSION

The miR-136-5p/MTDH axis may play a vital role in modulating TC tumorigenesis, providing new insight into possible molecular mechanisms of TC oncogenesis.

Differentially expressed autophagy-related genes are potential prognostic and diagnostic biomarkers in clear-cell renal cell carcinoma

We examined the role of differentially expressed autophagy-related genes (DEARGs) in clear cell Renal Cell Carcinoma (ccRCC) using high-throughput RNA-seq data from The Cancer Genome Atlas (TCGA). Cox regression analyses showed that 5 DEARGs (PRKCQ, BID, BAG1, BIRC5, and ATG16L2) correlated with overall survival (OS) and 4 DEARGs (EIF4EBP1, BAG1, ATG9B, and BIRC5) correlated with disease-free survival (DFS) in ccRCC patients. Multivariate Cox regression analysis using the OS and DFS prognostic risk models showed that expression of the nine DEARGs accurately and independently predicted the risk of disease recurrence or progression in ccRCC patients (area under curve or AUC values > 0.70; all p < 0.05). Moreover, the DEARGs accurately distinguished healthy individuals from ccRCC patients based on receiver operated characteristic (ROC) analyses (area under curve or AUC values > 0.60), suggesting their potential as diagnostic biomarkers for ccRCC. The expression of DEARGs also correlated with the drug sensitivity of ccRCC cell lines. The ccRCC cell lines were significantly sensitive to Sepantronium bromide, a drug that targets BIRC5. This makes BIRC5 a potential therapeutic target for ccRCC. Our study thus demonstrates that DEARGs are potential diagnostic and prognostic biomarkers and therapeutic targets in ccRCC.

A predicted risk score based on the expression of 16 autophagy-related genes for multiple myeloma survival

Autophagy has an important role in the pathogenesis of plasma cell development and multiple myeloma (MM); however, the prognostic role of autophagy-related genes (ARGs) in MM remains undefined. In the present study, the expression profiles of 234 ARGs were obtained from a Gene Expression Omnibus dataset (accession GSE24080), which contains 559 samples of patients with MM analyzed with 54,675 probes. Univariate Cox regression analysis identified 55 ARGs that were significantly associated with event-free survival of MM. Furthermore, a risk score with 16 survival-associated ARGs was developed using multivariate Cox regression analysis, including ATIC, BNIP3L, CALCOCO2, DNAJB1, DNAJB9, EIF4EBP1, EVA1A, FKBP1B, FOXO1, FOXO3, GABARAP, HIF1A, NCKAP1, PRKAR1A and SUPT20H, was constructed. Using this prognostic signature, patients with MM could be separated into high- and low-risk groups with distinct clinical outcomes. The area under the curve values for the receiver operating characteristic curves were 0.740, 0.741 and 0.712 for 3, 5 and 10 years prognosis predictions, respectively. Notably, the prognostic role of this risk score could be validated with another four independent cohorts (accessions: GSE57317, GSE4581, GSE4452 and GSE4204). In conclusion, ARGs may serve vital roles in the progression of MM, and the ARGs-based prognostic model may provide novel ideas for clinical applications in MM.