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Case Reports
. 2023 Aug 18:13:1206043.
doi: 10.3389/fonc.2023.1206043. eCollection 2023.

Dermatomyositis with intrahepatic cholangiocarcinoma: a case report and data mining based on machine learning

Xusheng Zhang  1 , Bendong Chen  2
Affiliations
Case Reports

Dermatomyositis with intrahepatic cholangiocarcinoma: a case report and data mining based on machine learning

Xusheng Zhang et al. Front Oncol. .

Abstract

Cancer secondary to dermatomyositis (DM) is defined as paraneoplastic dermatomyositis, which is one of the major subtypes of DM. However, cases of DM with intrahepatic cholangiocarcinoma (ICC) are rarely reported. In the course of our clinical work, we encountered a case of a middle-aged female patient who was diagnosed with DM for 7 years and then diagnosed with ICC, and we would like to share this case. In addition, in order to further investigate the deeper mechanism of ICC associated with DM, we also analyzed the dataset related to DM and ICC in the Gene Expression Omnibus (GEO) database based on the machine learning methods and found that poly(ADP-ribose) polymerase family member 12 (PARP12) and metallothionein 1M (MT1M) were closely associated with ICC secondary to DM. They are potentially important biomarkers for predicting the occurrence of ICC in patients with DM.

Keywords: MT1M; PARP12; dermatomyositis; intrahepatic cholangiocarcinoma; paraneoplastic dermatomyositis.

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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
Patient’s preoperative abdominal CT axial scan and enhancement. (A) Plain phase. (B) Arterial phase. (C) Venous phase. (D) Delayed phase.
Figure 2
Figure 2
Abdominal MRI scan. (A) MRI coronal T2WI sequence. (B) T1WI sequence shows a low signal. (C) T2WI sequence shows a high signal. (D) DWI sequence shows a high signal. The red arrow points to the cancer foci. T1WI, T1-weighted imaging; T2WI, T2-weighted imaging; DWI, diffusion-weighted imaging.|.
Figure 3
Figure 3
Results of surgically resected pathological tissue sent for examination. (A) Intraoperative frozen pathological sections. (B) Surgical resection of the whole specimen; the volume of cancer foci was approximately 6 cm × 5 cm × 4 cm. (C) Postoperative pathological specimens examined.
Figure 4
Figure 4
(A) Heat map of differentially expressed genes in ICC. (B) Heat map of differential gene expression in DM. (C) Volcano map of differentially expressed genes in ICC. (D) Volcano map of differentially expressed genes in DM. (E) Circle diagram of GO/KEGG enrichment analysis of differentially expressed genes in DM. (F) Circle diagram of GO/KEGG enrichment analysis of differentially expressed genes in ICC. (G) Network diagram of enrichment analysis of differentially expressed genes in DM. (H) Network diagram of enrichment analysis of differentially expressed genes in ICC. (I–N) Random forest tree-based screening for signature genes. (I) Intersection Wayne plot of differentially expressed genes. (G, K) Random forest tree. (G) DM. (K) ICC. Green: error of cross-validation in the normal group. Red: error of cross-validation in the disease group. Black: error of cross-validation in the overall sample. (L, M) Circle plot of gene importance scores based on random forest model. (L) DM. (M) ICC. (N) Venn diagram of the intersection of genes with top three importance scores in DM and ICC. (O–V) Expression and diagnostic efficacy analysis of characteristic genes. (O, P) Expression analysis of PARP12 and MT1M in DM. (Q, I) Expression analysis of PARP12 and MT1M in ICC. (S, T) Expression level validation of PARP12 and MT1M in ICC. (U, V) Diagnostic efficacy analysis of PARP12 and MT1M in ICC. Con, normal; Treat, DM or ICC. (W, X) Nomogram risk model construction. (W) Columnar graph model. (X) Calibration curve. Con, normal; Treat, ICC. ICC, intrahepatic cholangiocarcinoma; DM, dermatomyositis; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes.
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