Prognostic model and immune-infiltrating cell landscape based on differentially expressed autophagy-related genes in TP53-mutated multiple myeloma

Abstract

Introduction Autophagy functions as a prosurvival mechanism in multiple myeloma (MM).The objective of this research was to establish an autophagy-related gene (ARG) signature for predicting the survival outcomes of MM patients with TP53 mutations. Material and methods Information about MM patients with TP53 mutations was downloaded from Gene Expression Omnibus (GEO) database. Cox proportional hazard regression was employed to determine the independent prognostic ARG and construct a risk signature. Time-dependent receiver-operating characteristic (t ROC) curve was used to explore the predictive accuracy of the prognostic model. A nomogram was constructed to give a more precise prediction of the probability of 5-year, 8-year and 10-year overall survival (OS). In addition, we utilized the CIBERSORT algorithm to explore the distribution difference of 22 immune-infiltrating cells. Results Three differentially expressed ARGs (CASP8, MAPK8, RB1CC1) were finally incorporated to construct the risk model. Area under the curve (AUC) of corresponding tROC curve for 5-year,8-year and 10-year OS were 0.735, 0.686 and 0.662, respectively. MM patients were categorized into high and low-risk group in accordance with the median threshold value (-1.724549). ARG-based risk score model was an independent prognostic element correlated with OS, giving an hazard ratio (HR) of 3.29 (95%CI 2.35-4.60, P<0.001). 13 immune infiltrating cells were found to have distribution differences between the two groups. Conclusions We established a three-ARGs risk signature which manifested an independent prognostic factor. The nomogram was testified to perform well in forecasting the long-term survival of TP53-mutated MM patients. Powered by TCPDF (www.tcpdf.org) Pr rin t Response letter to editors and reviewers Dear distinguished editors, This is about our revised manuscript (AMS-13124-2021-01) entitled Prognostic model and immune-infiltrating cell landscape based on differentially expressed autophagy-related genes in TP53-mutated multiple myeloma to Archives of Medical Science. First, I would like to extend my sincere gratitude for the instructive comments from the conscientious editors and the reviewers. A revised version of the manuscript (with track changes marked in red) has also been reuploaded in the submission system. In accordance with your requirements and comments, we have made a point-bypoint response to all the reviewers and editors, and the modifications are summarized as follows. Review 1: In the current study, Zheng et al established a prognostic model and immuneinfiltrating cell landscape based on differently expressed autophagy-related genes in TP53-mutated multiple myeloma, and proposed a three autophagyrelated gene risk signature which was testified to perform well in forecasting the long-term survival of multiple myeloma patients with TP53-mutations. In addition, they found thirteen kinds of immune infiltrating cells were distributed differently between the high and low-risk group according to the prognostic model. Their findings are interesting and have clinical merit to MM management. There were still two minor points remained to be addressed or explained: 1.Table 3 listed the distribution differences of 22 immune-infiltrating cells. The increase or decrease of immune cells need to be marked in the manuscript. Author response to reviewer1: Thanks for your kind suggestions. We have specified and added the increase or decrease of immune cells in our manuscript as required. Pr pri nt The newly-added contents are: “The abundance of CD8+T cells, gamma delta T cells, activated NK cells, activated dendritic cells, monocytes, resting mast cells, and macrophagesM0,M1 were observed lower in the high-risk group than the low-risk group(all p-value<0.05), whereas the fraction of memory B cells, plasma cells, naïve CD4+T cells, resting NK cells, and activated mast cells were higher in the high-risk group(all p-value<0.05). The results indicated that the proportion of different immune-infiltrating cells was closely associated with the aggressiveness and risk stratification and DEARG-based risk signature might be correlated with the immune microenvironments of TP53-mutated MM.” 2.The ROC value of risk model is not so prominent. The authors are thus recommended to further compare or explain the difference of their model with traditional ISS staging system in multiple myeloma. Author response to reviewer1: Thanks for your comments. Just as you have mentioned, we also noticed that the ROC value of our established model is not so prominent as expected. But our study was only focused on TP53-mutated MM patients. Therefore, we consider it unsuitable to compare our established model with traditional ISS staging system or Durie-Salmon staging system in MM. Review 2: 1.Results, section 3.5: differences of cell populations should be explained clearly in the text, for example ..... monocytes infiltration is higher in low risk cases... Author response to reviewer2: Thanks for your instructions. Just as reviewer 1 have pointed out the same question, we have specified and added the increase or decrease of immune cells in our manuscript. Pr pri t The newly-added contents are: “The abundance of CD8+T cells, gamma delta T cells, activated NK cells, activated dendritic cells, monocytes, resting mast cells, and macrophagesM0,M1 were observed lower in the high-risk group than the low-risk group(all p-value<0.05), whereas the fraction of memory B cells, plasma cells, naïve CD4+T cells, resting NK cells, and activated mast cells were higher in the high-risk group(all p-value<0.05). The results indicated that the proportion of different immune-infiltrating cells was closely associated with the aggressiveness and risk stratification and DEARG-based risk signature might be correlated with the immune microenvironments of TP53-mutated MM.” 2. Actually, I must say and share with you that, a constant finding (as my observation) in bone marrow microscopy of myeloma patients is this: when monocytes are present, it is good, patients do well, progression is slow. But when monocytes are dissappeared, things are not good, disease is (or will be) highly aggressive. So, I am highly impressed by your results. Author response to reviewer2: Thanks very much for your comments. We are also happy to hear that our study results are consistent with your observations. And to some extent, your observation corroborated our results. Review 3: I think that the Survival analysis and establishment of prognostic model should be expainded better and the CIBERSORT estimation of immune infiltration cells should be validated in vitro, for exemple. It is difficult understand, biologically, how with the 3 genes (DEARGs ) you arrive in the risk score and in the prognostic model. Author response to reviewer3: Thank you for your comments. As I have mentioned in the Materials and Methods Section, the process of identifying and incorporating the DEARG in our prognostic Pr ep r n model has already been elucidated in the section “3.Survival analysis and establishment of prognostic model”. Here we will give a detailed description of the process: “Log-rank test was used to evaluate DEARG which might be associated with the prognosis, with pvalue<0.05 deemed statistically significant. Then DEARGs with potential prognostic value were initially selected by means of univariate Cox regression model. Least absolute shrinkage and selection operator (LASSO) regression was then utilized to eliminate false positive DEARGs because of over-fitting. DEARGs with p-value<0.05 in the univariate results were integrated into the multivariate analysis to determine the independent prognostic factors associated with OS and then construct a risk signature.” “The risk score for each individual patient was quantified by the following formula: risk scores= ∑ β(DEARGi) × Exp(i) i=1,2...n , where β represents the regression coefficient for each DEARG derived from the multivariate Cox regression and Exp indicates the relative expression levels of each DEARG standardized by Z-score.Survival difference between the above two groups were also evaluated by Kaplan-Meier curve and then the log-rank test. Univariate and multivariate Cox regression were further carried out to determine whether the DEARG-based risk score could be an independent prognostic factor in TP53-mutated MM patients.” Section Editor recommendation Accept without changes Author response to Section editor: Thanks very much for your suggestions. Furthermore, I have cited the following relevant articles published in last 2-3 years in order to build the impact factor. The newly-cited references in our manuscript which were published in Archives of Medical Science are as follows. Reference 29: Azhati B, Maolakuerban N, Ma T, Li X, Rexiati M. Up-regulation of DRAM2 promotes tolerance of bladder transitional cell carcinoma to gemcitabine. Arch Med Sci. 2020. 16(5): 1207-1217. Reference 52: Zgodzinski W, Grywalska E, Zinkiewicz K, et al. Peripheral blood T lymphocytes are downregulated by the PD-1/PD-L1 axis in advanced gastric cancer. Arch Med Sci. 2019. Pr ep ri t 15(3): 774-783. The corresponding newly-added contents are as follows. Also, induction of autophagy forcefully strengthened chemoresistance to gemcitabine in bladder carcinoma(Reference 29). Our study revealed that immune cells with tumor-killing effect in high-risk group constituted smaller proportion than their counterparts in low-risk group, which is consistent with a previous research on gastric cancer(Reference 52). Thanks again for your generous assistance. We hope that the revised version can meet the requirements. I am looking forward to hearing from you. Cordially Yours Yan-Hua Zheng