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Integrative analysis reveals that SLC38A1 promotes hepatocellular carcinoma development via PI3K/AKT/mTOR signaling via glutamine mediated energy metabolism

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Abstract

Although hepatocellular carcinoma (HCC) is rather frequent, little is known about the molecular pathways underlying its development, progression, and prognosis. In the current study, we comprehensively analyzed the deferentially expressed metabolism-related genes (MRGs) in HCC based on TCGA datasets attempting to discover the potentially prognostic genes in HCC. The up-regulated MRGs were further subjected to analyze their prognostic values and protein expressions. Twenty-seven genes were identified because their high expressions were significant in OS, PFS, DFS, DSS, and HCC tumor samples. They were then used for GO, KEGG, methylation, genetics changes, immune infiltration analyses. Moreover, we established a prognostic model in HCC using univariate assays and LASSO regression based on these MRGs. Additionally, we also found that SLC38A1, an amino acid metabolism closely related transporter, was a potential prognostic gene in HCC, and its function in HCC was further studied using experiments. We found that the knockdown of SLC38A1 notably suppressed the growth and migration of HCC cells. Further studies revealed that SLC38A1 modulated the development of HCC cells by regulating PI3K/AKT/mTOR signaling via glutamine mediated energy metabolism. In conclusion, this study identified the potentially prognostic MRGs in HCC and uncovered that SLC38A1 regulated HCC development and progression by regulating PI3K/AKT/mTOR signaling via glutamine mediated energy metabolism, which might provide a novel marker and potential therapeutic target in HCC.

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Data availability statement

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

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Authors and Affiliations

  1. Department of Hepatobiliary Surgery, the Second Affiliated Hospital of Chongqing Medical University, Chongqing Medical University, No. 74 Linjiang Road, Chongqing, China

    Hua-guo Feng, Chuan-xin Wu, Guo-chao Zhong, Jian-ping Gong, Chun-mu Miao & Bin Xiong

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  1. Hua-guo Feng
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  2. Chuan-xin Wu
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  4. Jian-ping Gong
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Contributions

Hua-guo Feng and Bin Xiong conceived the project, conducted the experiments, and prepared the manuscript. Chuan-xin Wu and Guo-chao Zhong assisted in the experiments. Jian-ping Gong and Chun-mu Miao performed the data analysis. Hua-guo Feng and Chun-mu Miao prepared figures, Chuan-xin Wu, Jian-ping Gong, and Bin Xiong edited manuscript. All authors contributed to the article and approved the submitted version.

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Correspondence to Bin Xiong.

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This research did not involve Ethical consent. Sequencing data of HCC patients from public databases (TCGA and ICGC datasets).

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Supplementary Information

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432_2023_5360_MOESM1_ESM.tif

Supplementary file1 Supplementary Figure S1. Identification of HCC molecular subtypes based on TCGA database using DEGs. (A) The cumulative distribution function (CDF) Delta area curve of TCGA-LIHC samples. (B) The cumulative distribution function (CDF) curves, which is able to describe the probability distribution of a real random variable, and established using consensus clustering approach. (C) The 371 HCC samples were split into 2 clusters by the consensus clustering matrix (k = 2). (D) Color-coded heatmap related to the consensus matrix for k= 2 obtained by using consensus clustering. C1 has 104 HCC samples and C2 has 267 HCC samples. (E) The overall survival (OS) analysis of the two groups. G1 has 104 HCC samples and G2 has 267 HCC samples. (F) The progression-free survival (PFS) analysis of the two groups. G1 has 104 HCC samples and G2 has 267 HCC samples (TIF 4349 KB)

432_2023_5360_MOESM2_ESM.tif

Supplementary file2 Supplementary Figure S2. Identification of differentially expressed genes (DEGs) in the above-certified two HCC groups and functional enrichment analysis. (A) Heatmap of DEGs in the two HCC groups. G1 has 104 HCC samples and G2 has 267 HCC samples. (B) Volcano map of DEGs. (C and D) KEGG (Kyoto Encyclopedia of Genes and Genomes) analyses of up- and down-regulated DEGs. (E and F) GO (Gene Ontology) analyses of up- and down-regulated DEGs (TIF 5720 KB)

432_2023_5360_MOESM3_ESM.tif

Supplementary file3 Supplementary Figure S3. The expression analyses of SLC38A1 in pan-cancers. (A). SLC38A1 mRNA expression in pan-cancers which is analyzed by using TIMER 2.0 databse. (B) The protein expression of SLC38A1 in multiple cancer types which is analyzed by using HPA database. (C) SLC38A1 methylation analysis across TCGA cancer types which is analyzed by using GSCA database. (D) SLC38A1 mutation landscape in pan-cancers which is analyzed by using TIMER 2.0 databse (TIF 4132 KB)

432_2023_5360_MOESM4_ESM.tif

Supplementary file4 Supplementary Figure S4. Immune cell infiltration analyses of SLC38A1 in TCGA cancer types. The correlation between SLC38A1 expression and the levels of infiltration of CD4+ T cells, CAF, HSC, γδ T cells, MDSC, NKT, regulatory T cells (Tregs), B cells, neutrophils, monocytes, macrophages, dendritic cells (DC), NK cells, Mast cells, and CD8+ T cells in TCGA cancers (TIF 5213 KB)

432_2023_5360_MOESM5_ESM.tif

Supplementary file5 Supplementary Figure S5. Expression and prognosis analyses of SLC38A1 in HCC. (A) mRNA expression of SLC38A1 in HCC based on TCGA database. (B) Protein expression of SLC38A1 in HCC using UALCAN database. (C) The immunohistochemistry analysis of SLC38A1 expression in HCC tumor samples using HPA database. (D) SLC38A1 expression and survival status of HCC patients from TCGA datasets. (E) The overall survival analysis of SLC38A1 high and low expression groups. (F) The ROC curves with AUC values at 1-year, 3-year and 5-year (TIF 3609 KB)

432_2023_5360_MOESM6_ESM.tif

Supplementary file6 Supplementary Figure S6. Sankey diagrams presents the SLC38A1 expression and clinicopathological characteristics, and immune interacting network construction. (A-B) The correlation between SLC38A1 high and low expression and clinicopathological characteristics (age, gender, grade, pT stage, pN stage, pM stage, survival status). (C) The immune interacting network between SLC38A1 and kinds of immune cells in HCC (TIF 1423 KB)

432_2023_5360_MOESM7_ESM.tif

Supplementary file7 Supplementary Figure S7. The SLC38A1-gene interaction network and functional enrichment analyses of SLC38A1 co-expression genes. (A) Gene-gene interaction of SLC38A1 with other genes was generated by GeneMANIA. (B-D) GO analyses of SLC38A1 co-expression genes. BP, biological process, CC, cellular component, MF, molecular function. (E) KEGG analyses of SLC38A1 co-expression genes. KEGG, Kyoto Encyclopedia of Genes and Genomes (TIF 4164 KB)

432_2023_5360_MOESM8_ESM.tif

Supplementary file8 Supplementary Figure S8. SLC38A1 expression in HCC cancer cell lines. (A) SLC38A1 expression in 946 cell lines of various cancer types using CCLE database. (B) The SLC38A1 expression in 25 HCC cell lines based on CCLE dataset. (C) The qRT-PCR detected the SLC38A1 mRNA levels in SNU-449, SNU-423, SNU-398, JHH-2, SMMC-7721, Huh-7, HepG2, Hep3B HCC cell lines (TIF 1752 KB)

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Feng, Hg., Wu, Cx., Zhong, Gc. et al. Integrative analysis reveals that SLC38A1 promotes hepatocellular carcinoma development via PI3K/AKT/mTOR signaling via glutamine mediated energy metabolism. J Cancer Res Clin Oncol 149, 15879–15898 (2023). https://doi.org/10.1007/s00432-023-05360-3

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