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. 2023 May 19:10:1200335.
doi: 10.3389/fmolb.2023.1200335. eCollection 2023.

FAM family gene prediction model reveals heterogeneity, stemness and immune microenvironment of UCEC

Hao Chi  1 , Xinrui Gao  1 , Zhijia Xia  2 , Wanying Yu  1 , Xisheng Yin  1 , Yifan Pan  1 , Gaoge Peng  1 , Xinrui Mao  1 , Alexander Tobias Teichmann  3 , Jing Zhang  4 , Lisa Jia Tran  2 , Tianxiao Jiang  2 , Yunfei Liu  2 , Guanhu Yang  5 , Qin Wang  3
Affiliations

FAM family gene prediction model reveals heterogeneity, stemness and immune microenvironment of UCEC

Hao Chi et al. Front Mol Biosci. .

Abstract

Background: Endometrial cancer (UCEC) is a highly heterogeneous gynecologic malignancy that exhibits variable prognostic outcomes and responses to immunotherapy. The Familial sequence similarity (FAM) gene family is known to contribute to the pathogenesis of various malignancies, but the extent of their involvement in UCEC has not been systematically studied. This investigation aimed to develop a robust risk profile based on FAM family genes (FFGs) to predict the prognosis and suitability for immunotherapy in UCEC patients. Methods: Using the TCGA-UCEC cohort from The Cancer Genome Atlas (TCGA) database, we obtained expression profiles of FFGs from 552 UCEC and 35 normal samples, and analyzed the expression patterns and prognostic relevance of 363 FAM family genes. The UCEC samples were randomly divided into training and test sets (1:1), and univariate Cox regression analysis and Lasso Cox regression analysis were conducted to identify the differentially expressed genes (FAM13C, FAM110B, and FAM72A) that were significantly associated with prognosis. A prognostic risk scoring system was constructed based on these three gene characteristics using multivariate Cox proportional risk regression. The clinical potential and immune status of FFGs were analyzed using CiberSort, SSGSEA, and tumor immune dysfunction and rejection (TIDE) algorithms. qRT-PCR and IHC for detecting the expression levels of 3-FFGs. Results: Three FFGs, namely, FAM13C, FAM110B, and FAM72A, were identified as strongly associated with the prognosis of UCEC and effective predictors of UCEC prognosis. Multivariate analysis demonstrated that the developed model was an independent predictor of UCEC, and that patients in the low-risk group had better overall survival than those in the high-risk group. The nomogram constructed from clinical characteristics and risk scores exhibited good prognostic power. Patients in the low-risk group exhibited a higher tumor mutational load (TMB) and were more likely to benefit from immunotherapy. Conclusion: This study successfully developed and validated novel biomarkers based on FFGs for predicting the prognosis and immune status of UCEC patients. The identified FFGs can accurately assess the prognosis of UCEC patients and facilitate the identification of specific subgroups of patients who may benefit from personalized treatment with immunotherapy and chemotherapy.

Keywords: FAM family genes; UCEC; cancer treatment; chemotherapy; stemness; tumor heterogeneity; tumor microenvironment.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
The diagram provides an overview of the primary design of the current investigation.
FIGURE 2
FIGURE 2
Identification of candidate FFGs and construction of prognostic signature. (A) Volcano map of 363 differentially expressed FAM family genes. (B) Venn diagram of the intersection of DE-FFGs and prognosis-related FFGs. (C) Prognosis of 15 FFGs in the entire cohort of UCEC was analyzed by univariate Cox regression model. (D) Ten‐time cross‐validation for tuning parameter selection in the LASSO model. (E) LASSO coefficient profiles. (F) Cox proportional risk regression model identified FAM13C, FAM110B and FAM72A as survival predictor signature. (G) Heatmap of risk factor in the test cohort. (H) PCA plot in the entire cohort. (I) K-M survival curve of endometrial cancer patients in the entire group. (J) Time-dependent ROC curves analysis. (K) Multi-index ROC analysis in the entire cohort. (L) Decision curve analysis.
FIGURE 3
FIGURE 3
Validation of the prognosis signature for FFGs. (A) Heat map of risk factors in the train cohort. (B) K-M survival curve of UCEC patients in the train cohort. (C) PCA plot in the train cohort. (D) Time-dependent ROC curve of UCEC patients in the train cohort. (E) Multi-index ROC analysis in the train cohort. (F) Heatmap of risk factor in the test cohort. (G) K-M survival curve of UCEC patients in the test cohort. (H) PCA plot in the test cohort. (I) Time-dependent ROC curve of UCEC patients in the test cohort. (J) Multi-index ROC analysis in the test cohort.
FIGURE 4
FIGURE 4
Independent prognostic analysis of risk scores and clinical parameters. Univariate (A) and multivariate (B) COX regression analysis of the signature and different clinical feature. (C) Heatmap for the 3 FFGs-based signature with clinicopathological manifestations. (D) Nomogram for predicting 1-year, 3-year, and 5-year OS of patients with UCEC. The calibration curve of the constructed nomogram of 1- year (E), 3- year (F), and 5-year (G) survival.
FIGURE 5
FIGURE 5
Comparison of the FFGs risk model with other models (A) KM curves and ROCs for FFGs signature. (B–F) KM curves and ROCs for risk models constructed by others. (G) C-indexes for six risk models.
FIGURE 6
FIGURE 6
TME and immune cell infiltration in the two risk score groups. (A) Immune cell bubble plots of the risk groups. (B) Heat map showing the correlation between the 22 TICs. Correlation tests were performed with Pearson’s coefficient. (C) The ratio of immune cells between high-risk and low-risk groups. (D) Immune function and immune cell ssGSEA scores between high-risk and low-risk groups. (E) Immune score, (F) stromal score, (G) ESTIMATE score, (H) immune checkpoint between high-risk and low-risk groups *p < 0.05; **p < 0.01; ***p < 0.001.
FIGURE 7
FIGURE 7
FFGs risk score predicts treatment response assessment. (A) Heat map of differences in the individual steps of the cancer-immune cycle between high and low risk groups. (B) Correlation of different risk scores with steps of the cancer-immune cycle. (C) Correlation of different risk scores with enrichment scores of immunotherapy prediction pathways. (D) Correlation between different risk scores and clinical response to immunotherapy. (E) Box-line graphs of TIDE scores in the high-risk versus low-risk groups in the TCGA UCEC cohort. (F) Correlation between risk scores and clinical response to cancer immunotherapy. Differences in IC50 of immunotherapy drugs by risk score (G) A.443654, (H) A.770041, (I) ABT.263, (J) AG.014699, (K) AICAR, (L) AKT. inhibitor, (M) AP.24534, (N) AZD.0530. PD, disease progression; SD, disease Stable; PR, partial response; CR, complete response. TIDE, Tumor Immune Dysfunction and Exclusion. *p < 0.05, **p < 0.01, ***p < 0.001.
FIGURE 8
FIGURE 8
Landscape of mutation profiles in UCEC samples. (A,B) The hub-mutated markers in both groups. (C) The TMB between high-risk and low-risk patients. (D) Correlation between the TMB and risk score. (E) Kaplan-Meier analysis showing the relationship between the level of TMB and clinical outcome (p = 0.00014). (F) Effects of distinct TMB in different Risks on the survival probability (p < 0.001). (G) The proportion of different MSI in the high-risk and low-risk score groups. (H) The relationship between the MS and risk score. (I) The correlation relationship between FFGs expression and CSC index.
FIGURE 9
FIGURE 9
Results of RT-qPCR and IHC experiments on 3-FFGs. RT-qPCR result of (A) FAM13C, (B) FAM72A, (C) FAM110B. IHC result of (D) FAM13C, (E) FAM72A, (F) FAM110B.*p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001.
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Grants and funding

This study was supported by grants from the Luzhou Science and Technology Department Applied Basic Research program (No: 2022-WYC-196), and the Sichuan Province Science and Technology Department of foreign (border) high-end talent introduction project (No: 2023ZHYZ0009).

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