JAK/STAT/SOCS-signaling pathway and colon and rectal cancer.
The Janus kinase (JAK)/signal transducer and activator of transcription (STAT) signaling pathway is involved in immune function and cell growth. We evaluated the association between genetic variation in JAK1 (10 SNPs), JAK2 (9 SNPs), TYK2 (5 SNPs), suppressors of cytokine signaling (SOCS)1 (2 SNPs), SOCS2 (2 SNPs), STAT1 (16 SNPs), STAT2 (2 SNPs), STAT3 (6 SNPs), STAT4 (21 SNPs), STAT5A (2 SNPs), STAT5B (3 SNPs), STAT6 (4 SNPs) with risk of colorectal cancer. We used data from population-based case-control studies (colon cancer n = 1555 cases, 1,956 controls; rectal cancer n = 754 cases, 959 controls). JAK2, SOCS2, STAT1, STAT3, STAT5A, STAT5B, and STAT6 were associated with colon cancer; STAT3, STAT4, STAT6, and TYK2 were associated with rectal cancer. Given the biological role of the JAK/STAT-signaling pathway and cytokines, we evaluated interaction with IFNG, TNF, and IL6; numerous statistically significant associations after adjustment for multiple comparisons were observed. The following statistically significant interactions were observed: TYK2 with aspirin/NSAID use; STAT1, STAT4, and TYK2 with estrogen status; and JAK2, STAT2, STAT4, STAT5A, STAT5B, and STAT6 with smoking status and colon cancer risk; JAK2, STAT6, and TYK2 with aspirin/NSAID use; JAK1 with estrogen status; STAT2 with cigarette smoking and rectal cancer. JAK2, SOCS1, STAT3, STAT5, and TYK2 were associated with colon cancer survival (hazard rate ratio (HRR) of 3.3 95% CI 2.01,5.42 for high mutational load). JAK2, SOCS1, STAT1, STAT4, and TYK2 were associated with rectal cancer survival (HRR 2.80 95% CI 1.63,4.80). These data support the importance of the JAK/STAT-signaling pathway in colorectal cancer and suggest targets for intervention.
Slattery ML
，Lundgreen A
，Kadlubar SA
，Bondurant KL
，Wolff RK
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The JAK-STAT signaling-related signature serves as a prognostic and predictive biomarker for renal cell carcinoma immunotherapy.
Renal cell carcinoma (RCC) represents a significant portion of genitourinary cancers, marked by challenging prognosis and high metastasis rates. Immunotherapy has been applied in managing advanced renal cell carcinoma, but the therapeutic outcomes are unsatisfactory. In this study, we order to construct a Janus kinase/signal transduction and activator transcriptional (JAK/STAT)-related signature linked to kidney patient outcomes for better predicting the efficacy to immune checkpoint inhibitors (ICIs) and to provide guidance for effective combination therapy. We screened 25 differentially expressed genes (DEGs) that exhibited high expression in RCC samples and were enriched in the JAK-STAT signaling pathway. Among these genes, 11 key genes were identified and correlated with the expectation of Kidney Clear Cell Carcinoma (KIRC) patients and all these genes was significantly elevated in RCC tumor tissues and cancer cells compared to para-cancer tissues and normal renal cells. Utilizing these 11 genes, we divided RCC patients into high-risk and low-risk groups. We found a clear correlation between the clinicopathologic factors of KIRC patients and the JAK-STAT-related risk score. And the IHC results shown that the JAK3 and STAT4 expression of tumor was significantly higher than normal tissue in RCC patients, the level of JAK3 and STAT4 was positively related to the T stage of RCC patients. In addition, high-risk patients had a poorer prognosis and greater protumor immune cell infiltration, and benefitted less from immunotherapy than did low-risk patients. Furthermore, the JAK-STAT-related risk score can predict disease-free survival (DFS) in RCC patients according to the nomogram, which constructed in combination with other clinical features such as age, TNM-staging and stage. Our study demonstrated the JAK-STAT signaling pathway's important regulatory function in RCC tumor immunity. This insight not only enhances our ability to accurately predict the survival rate of RCC patients, but also underscores a potential therapeutic alternative for RCC, involving the combined targeting of the JAK-STAT pathway and immune checkpoints.
Chan S
，Liu Z
，Chen Y
，Chen S
，Liang Y
，Yang Z
，Zhang Z
，Li M
，Zhang X
，Liu X
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Construction of a risk score prognosis model based on hepatocellular carcinoma microenvironment.
Hepatocellular carcinoma (HCC) is a common cancer with a poor prognosis. Previous studies revealed that the tumor microenvironment (TME) plays an important role in HCC progression, recurrence, and metastasis, leading to poor prognosis. However, the effects of genes involved in TME on the prognosis of HCC patients remain unclear. Here, we investigated the HCC microenvironment to identify prognostic genes for HCC.
To identify a robust gene signature associated with the HCC microenvironment to improve prognosis prediction of HCC.
We computed the immune/stromal scores of HCC patients obtained from The Cancer Genome Atlas based on the ESTIMATE algorithm. Additionally, a risk score model was established based on Differentially Expressed Genes (DEGs) between high- and low-immune/stromal score patients.
The risk score model consisting of eight genes was constructed and validated in the HCC patients. The patients were divided into high- or low-risk groups. The genes (Disabled homolog 2, Musculin, C-X-C motif chemokine ligand 8, Galectin 3, B-cell-activating transcription factor, Killer cell lectin like receptor B1, Endoglin and adenomatosis polyposis coli tumor suppressor) involved in our risk score model were considered to be potential immunotherapy targets, and they may provide better performance in combination. Functional enrichment analysis showed that the immune response and T cell receptor signaling pathway represented the major function and pathway, respectively, related to the immune-related genes in the DEGs between high- and low-risk groups. The receiver operating characteristic (ROC) curve analysis confirmed the good potency of the risk score prognostic model. Moreover, we validated the risk score model using the International Cancer Genome Consortium and the Gene Expression Omnibus database. A nomogram was established to predict the overall survival of HCC patients.
The risk score model and the nomogram will benefit HCC patients through personalized immunotherapy.
Zhang FP
，Huang YP
，Luo WX
，Deng WY
，Liu CQ
，Xu LB
，Liu C
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