初稿

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Title

Multiple machine learning algorithms have pinpointed SLC6A8 as a diagnostic biomarker for the late stage of Hepatocellular carcinoma.

Abstract

Introduction

Materials and methods

Data collection and download

Data Integration

Differential Expression Genes

Functional enrichment analysis

Immune cell infiltration

Potential biomarkers selection

ROC of diagnostic biomarker

Single cell transcriptome data processing and analyzing

Statistical analysis

Results

Identification of DEGs in the HCC early stage and late stage

Figure 1

Figure 2A

Figure 2B

Functional analysis of DEGs by GO and KEGG enrichment analysis

图 1: Figure 3A
图 2: Figure 3C
图 3: Figure 3B
图 4: Figure 3D

Significant changes between two stages in immune cells by ImmuneCellAI

图 5: Figure 4A
图 6: Figure 4B
图 7: Figure 4C
图 8: Figure 4D

Potential gene biomarkers identified by multiple machine learning approaches

图 9: Figure 5A1
图 10: Figure 5A2
图 11: Figure 5B
图 12: Figure 5D
图 13: Figure 5C
图 14: Figure 5E

Correlation analysis between SLC6A8 and immune cells

图 15: Figure 6A
图 16: Figure 6B
图 17: Figure 6C
图 18: Figure 6D

Expression level of SLC6A8 in single-cell transcriptomic data

图 19: Figure 7A
图 20: Figure 7B
图 21: Figure 7C
图 22: Figure 7D

Discussion

Conclusion

Limitation of the study

Supplemental Figures