Comparison between retroperitoneal leiomyosarcoma and dedifferentiated liposarcoma

Deep learning radiomics-based prediction model of metachronous distant metastasis following curative resection for retroperitoneal leiomyosarcoma: a bicentric study

BACKGROUND

Combining conventional radiomics models with deep learning features can result in superior performance in predicting the prognosis of patients with tumors; however, this approach has never been evaluated for the prediction of metachronous distant metastasis (MDM) among patients with retroperitoneal leiomyosarcoma (RLS). Thus, the purpose of this study was to develop and validate a preoperative contrast-enhanced computed tomography (CECT)-based deep learning radiomics model for predicting the occurrence of MDM in patients with RLS undergoing complete surgical resection.

METHODS

A total of 179 patients who had undergone surgery for the treatment of histologically confirmed RLS were retrospectively recruited from two tertiary sarcoma centers. Semantic segmentation features derived from a convolutional neural network deep learning model as well as conventional hand-crafted radiomics features were extracted from preoperative three-phase CECT images to quantify the sarcoma phenotypes. A conventional radiomics signature (RS) and a deep learning radiomics signature (DLRS) that incorporated hand-crafted radiomics and deep learning features were developed to predict the risk of MDM. Additionally, a deep learning radiomics nomogram (DLRN) was established to evaluate the incremental prognostic significance of the DLRS in combination with clinico-radiological predictors.

RESULTS

The comparison of the area under the curve (AUC) values in the external validation set, as determined by the DeLong test, demonstrated that the integrated DLRN, DLRS, and RS models all exhibited superior predictive performance compared with that of the clinical model (AUC 0.786 [95% confidence interval 0.649-0.923] vs. 0.822 [0.692-0.952] vs. 0.733 [0.573-0.892] vs. 0.511 [0.359-0.662]; both P < 0.05). The decision curve analyses graphically indicated that utilizing the DLRN for risk stratification provided greater net benefits than those achieved using the DLRS, RS and clinical models. Good alignment with the calibration curve indicated that the DLRN also exhibited good performance.

CONCLUSIONS

The novel CECT-based DLRN developed in this study demonstrated promising performance in the preoperative prediction of the risk of MDM following curative resection in patients with RLS. The DLRN, which outperformed the other three models, could provide valuable information for predicting surgical efficacy and tailoring individualized treatment plans in this patient population.

TRIAL REGISTRATION

Not applicable.

Association of PD-L1 and IDO1 expression with JAK-STAT pathway activation in soft-tissue leiomyosarcoma

PURPOSE

Therapies targeting the immune checkpoint molecules programmed death ligand 1 (PD-L1) and indoleamine 2,3-dioxygenase 1 (IDO1) have been explored in various malignant tumours. In this study, we examined the relationship between PDL-1, IDO1 and JAK2 expression and the roles of these signal pathways in soft tissue leiomyosarcoma (LMS).

METHODS

The next-generation sequencing data of 53 patients with LMS were obtained from an online public database and were used to assess PD-L1, IDO1 and JAK2 gene amplification and mRNA expression. Then, we determined the relationship between JAK-STAT pathway activation and PD-L1 and IDO1 expression in a LMS cell line. In addition, immunohistochemical staining of 69 cases of LMS was performed for PD-L1, IDO1, TDO2 and phosphorylated JAK2 (pJAK2).

RESULTS

Comprehensive gene expression analysis using microarray and RNA-Seq data revealed that PD-L1 and IDO1 mRNA expression positively correlated with JAK2 and STAT1 mRNA expression. Two of the 53 cases exhibited PD-L1 and JAK2 gene amplification; however, they were not related to their gene expression. LMS cell line analysis revealed that IFN-γ supplementation induced IDO1 and PD-L1 expression; these effects were suppressed by JAK inhibition. Immunohistochemical analysis of the resected specimens revealed that TDO2 expression positively correlated with pJAK2 (P = 0.0490) and IDO1 expression (P < 0.0001). PD-L1-positive specimens tended to express pJAK2; however, the relationship did not reach statistical significance (P = 0.1477).

CONCLUSION

The results suggest the possible feasibility of the combined inhibition of PD-1/PD-L1 or IDO1 with IFN-γ-JAK-STAT pathway inhibition to treat soft tissue LMS.

Linc00423 as a tumor suppressor in retroperitoneal liposarcoma via activing MAPK signaling pathway through destabilizing of NFATC3

Unraveling the noncoding RNA expression networks governing cancer initiation and development is essential while remains largely uncompleted in retroperitoneal liposarcoma (RLS). Through RNA-seq technologies and computational biology, deregulated long noncoding RNAs (lncRNAs) are being identified and reveal that lncRNAs are implicated in serial steps of RLS development. High-throughput sequencing with computational methods for assembling the transcriptome of five paired RLS patient’s tissues. We found that long intergenic noncoding RNA 423 (linc00423) was downregulated in RLS tissues. Gain-of-function assays revealed that overexpressed linc00423 obviously inhibited RLS cell growth in vitro and in vivo. Additionally, RNA sequence, RNA-pulldown and RIP assays evidenced that linc00423 involved in MAPK signaling pathway via destabilizing of nuclear factor of activated T-cells 3 (NFATC3). Summing up, our findings demonstrated that linc00423 acted as the tumor suppressor in RLS cells through regulating the protein level of NFATC3 at a post-transcriptional level and negatively regulated the MAPK signaling pathway at a transcriptional level. Linc00423 might serve as a candidate prognostic biomarker and a target for novel therapies of RLS patients.

Comprehensive Bioinformatic Analysis Genes Associated to the Prognosis of Liposarcoma

BACKGROUND Liposarcoma is the most common type of soft tissue sarcoma, but its molecular mechanism is poorly defined. This study aimed to identify genes crucial to the pathogenesis of liposarcoma and to explore their functions, related pathways, and prognostic value. MATERIAL AND METHODS Differentially expressed genes (DEGs) in the GSE59568 dataset were screened. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted to investigate the DEGs at the functional level. Protein-protein interaction (PPI) networks and module analysis were applied to identify hub genes from among the DEGs. The GSE30929 dataset was used to validate the relationship between hub genes and the distant recurrence-free survival (DRFS) of liposarcoma patients using Cox model analysis. RESULTS A total of 1111 DEGs were identified. GO and KEGG pathway analysis indicated that the DEGs were mainly associated with lipopolysaccharides and pathways in cancer. The PPI network and module analysis identified 10 hub genes from the DEG network. The Cox model identified 3 genes (NIP7, RPL10L, and MCM2) significantly associated with DRFS. The risk score calculated by the Cox model of the NIP7-RPL10L-MCM2 signature could largely predict the 1-, 3-, and 5-year DRFS of liposarcoma patients, and the prognostic value was even higher for subtypes of liposarcoma. CONCLUSIONS This study identified genes that might play critical roles in liposarcoma pathogenesis as well as a 3-gene-based signature that could be used as a candidate prognostic biomarker for patients with liposarcoma.