Background. Doublecortin-like kinase 1 (DCLK1) has been universally identified as a cancer stem cell (CSC) marker and is found to be overexpressed in many types of cancers including breast cancer. However, there is little data regarding the functional role of DCLK1 in breast cancer metastasis. In the present study, we sought to investigate whether and how DCLK1 plays a metastatic-promoting role in human breast cancer cells. Methods. We used Crispr/Cas9 technology to knock out DCLK1 in breast cancer cell line BT474, which basically possesses DCLK1 at a higher level, and stably overexpressed DCLK1 in another breast cancer cell line, T47D, that basically expresses DCLK1 at a lower level. We further analyzed the alterations of metastatic characteristics and the underlying mechanisms in these cells. Results. It was shown that, compared with the corresponding control cells, DCLK1 overexpression led to an increase in metastatic behaviors including enhanced migration and invasion of T47D cells. By contrast, forced depletion of DCLK1 drastically inhibited these metastatic characteristics in BT474 cells. Mechanistically, the epithelial-mesenchymal transition (EMT) program, which is critical for cancer metastasis, was prominently activated in DCLK1-overexpressing cancer cells, evidenced by a decrease in an epithelial marker ZO-1 and an enhancement in several mesenchymal markers including ZEB1 and Vimentin. In addition, DCLK1 overexpression induced the ERK MAPK pathway, which resultantly enhanced the expression of MT1-MMP that is also involved in cancer metastasis. Knockout of DCLK1 could reverse these events, further supporting a metastatic-promoting role for DCLK1. Conclusions. Collectively, our data suggested that DCLK1 overexpression may be responsible for the increased metastatic features in breast cancer cells. Targeting DCLK1 may become a therapeutic option for breast cancer metastasis.

No abstract

Background: Cancer-associated fibroblasts (CAFs) are the most prominent cellular components in gastric cancer (GC) stroma that contribute to GC progression, treatment resistance, and immunosuppression. This study aimed at exploring stromal CAF-related factors and developing a CAF-related classifier for predicting prognosis and therapeutic effects in GC.Methods: We downloaded mRNA expression and clinical information of 431 GC samples from Gene Expression Omnibus (GEO) and 330 GC samples from The Cancer Genome Atlas (TCGA) databases. CAF infiltrations were quantified by the estimate the proportion of immune and cancer cells (EPIC) method, and stromal scores were calculated via the Estimation of STromal and Immune cells in MAlignant Tumors using Expression data (ESTIMATE) algorithm. Stromal CAF-related genes were identified by weighted gene co-expression network analysis (WGCNA). A CAF risk signature was then developed using the univariate and least absolute shrinkage and selection operator method (LASSO) Cox regression model. We applied the Spearman test to determine the correlation among CAF risk score, CAF markers, and CAF infiltrations (estimated via EPIC, xCell, microenvironment cell populations-counter (MCP-counter), and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms). The TIDE algorithm was further used to assess immunotherapy response. Gene set enrichment analysis (GSEA) was applied to clarify the molecular mechanisms.Results: The 4-gene (COL8A1, SPOCK1, AEBP1, and TIMP2) prognostic CAF model was constructed. GC patients were classified into high– and low–CAF-risk groups in accordance with their median CAF risk score, and patients in the high–CAF-risk group had significant worse prognosis. Spearman correlation analyses revealed the CAF risk score was strongly and positively correlated with stromal and CAF infiltrations, and the four model genes also exhibited positive correlations with CAF markers. Furthermore, TIDE analysis revealed high–CAF-risk patients were less likely to respond to immunotherapy. GSEA revealed that epithelial–mesenchymal transition (EMT), TGF-β signaling, hypoxia, and angiogenesis gene sets were significantly enriched in high–CAF-risk group patients.Conclusion: The present four-gene prognostic CAF signature was not only reliable for predicting prognosis but also competent to estimate clinical immunotherapy response for GC patients, which might provide significant clinical implications for guiding tailored anti-CAF therapy in combination with immunotherapy for GC patients.

Background Cancer-associated fibroblasts (CAFs) contribute notably to colorectal cancer (CRC) tumorigenesis, stiffness, angiogenesis, immunosuppression and metastasis, and could serve as a promising therapeutic target. Our purpose was to construct CAF-related prognostic signature for CRC. Methods We performed bioinformatics analysis on single-cell transcriptome data derived from Gene Expression Omnibus (GEO) and identified 208 differentially expressed cell markers from fibroblasts cluster. Bulk gene expression data of CRC was obtained from The Cancer Genome Atlas (TCGA) and GEO databases. Univariate Cox regression and least absolute shrinkage operator (LASSO) analyses were performed on TCGA training cohort (n = 308) for model construction, and was validated in TCGA validation (n = 133), TCGA total (n = 441), GSE39582 (n = 470) and GSE17536 (n = 177) datasets. Microenvironment Cell Populations-counter (MCP-counter) and Estimate the Proportion of Immune and Cancer cells (EPIC) methods were applied to evaluated CAFs infiltrations from bulk gene expression data. Real-time polymerase chain reaction (qPCR) was performed in tissue microarrays containing 80 colon cancer samples to further validate the prognostic value of the CAF model. pRRophetic and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms were utilized to predict chemosensitivity and immunotherapy response. Human Protein Atlas (HPA) databases and immunohistochemistry were used to evaluate the protein expressions. Results A nine-gene prognostic CAF-related signature was established in training cohort. Kaplan–Meier survival analyses revealed patients with higher CAF risk scores were correlated with adverse prognosis in each cohort. MCP-counter and EPIC results consistently revealed CAFs infiltrations were significantly higher in high CAF risk group. Patients with higher CAF risk scores were more prone to not respond to immunotherapy, but were more sensitive to several conventional chemotherapeutics, suggesting a potential strategy of combining chemotherapy with anti-CAF therapy to improve the efficacy of current T-cell based immunotherapies. Univariate and multivariate Cox regression analyses verified the CAF model was as an independent prognostic indicator in predicting overall survival, and a CAF-based nomogram was then built for clinical utility in predicting prognosis of CRC. Conclusion To conclude, the CAF-related signature could serve as a robust prognostic indicator in CRC, which provides novel genomics evidence for anti-CAF immunotherapeutic strategies.

BackgroundEsophageal squamous cell carcinoma (ESCC) is the most common type of esophageal cancer and the seventh most prevalent cause of cancer-related death worldwide. Tumor microenvironment (TME) has been confirmed to play an crucial role in ESCC progression, prognosis, and the response to immunotherapy. There is a need for predictive biomarkers of TME-related processes to better prognosticate ESCC outcomes.AimTo identify a novel gene signature linked with the TME to predict the prognosis of ESCC.MethodsWe calculated the immune/stromal scores of 95 ESCC samples from The Cancer Genome Atlas (TCGA) using the ESTIMATE algorithm, and identified differentially expressed genes (DEGs) between high and low immune/stromal score patients. The key prognostic genes were further analyzed by the intersection of protein–protein interaction (PPI) networks and univariate Cox regression analysis. Finally, a risk score model was constructed using multivariate Cox regression analysis. We evaluated the associations between the risk score model and immune infiltration via the CIBERSORT algorithm. Moreover, we validated the signature using the Gene Expression Omnibus (GEO) database. Within the ten gene signature, five rarely reported genes were further validated with quantitative real time polymerase chain reaction (qRT-PCR) using an ESCC tissue cDNA microarray.ResultsA total of 133 up-regulated genes were identified as DEGs. Ten prognostic genes were selected based on intersection analysis of univariate COX regression analysis and PPI, and consisted of C1QA, C1QB, C1QC, CD86, C3AR1, CSF1R, ITGB2, LCP2, SPI1, and TYROBP (HR>1, p<0.05). The expression of 9 of these genes in the tumor samples were significantly higher compared to matched adjacent normal tissue based on the GEO database (p<0.05). Next, we assessed the ability of the ten-gene signature to predict the overall survival of ESCC patients, and found that the high-risk group had significantly poorer outcomes compared to the low-risk group using univariate and multivariate analyses in the TCGA and GEO cohorts (HR=2.104, 95% confidence interval:1.343-3.295, p=0.001; HR=1.6915, 95% confidence interval:1.053-2.717, p=0.0297). Additionally, receiver operating characteristic (ROC) curve analysis demonstrated a relatively sensitive and specific profile for the signature (1-, 2-, 3-year AUC=0.672, 0.854, 0.81). To identify the basis for these differences in the TME, we performed correlation analyses and found a significant positive correlation with M1 and M2 macrophages and CD8+ T cells, as well as a strong correlation to M2 macrophage surface markers. A nomogram based on the risk score and select clinicopathologic characteristics was constructed to predict overall survival of ESCC patients. For validation, qRT-PCR of an ESCC patient cDNA microarray was performed, and demonstrated that C1QA, C3AR1, LCP2, SPI1, and TYROBP were up-regulated in tumor samples and predict poor prognosis.ConclusionThis study established and validated a novel 10-gene signature linked with M2 macrophages and poor prognosis in ESCC patients. Importantly, we identified C1QA, C3AR1, LCP2, SPI1, and TYROBP as novel M2 macrophage-correlated survival biomarkers. These findings may identify potential targets for therapy in ESCC patients.

The oncogene ATPase family AAA domain-containing protein 2 (ATAD2) has been demonstrated to promote malignancy in a number of different types of tumor; however, its expression and role in ovarian cancer (OC) remain unknown. In the present study, it was demonstrated that ATAD2 acts as both a marker and a driver of cell proliferation in OC. Immunohistochemistry (IHC) and bioinformatics analyses were used to evaluate ATAD2 expression in OC, and multi-omics integrated analyses were used to dissect which factor resulted in its upregulation. Multiplex IHC assay was used to reveal the specific expression of ATAD2 in proliferating OC cells. CRISPR-Cas9-mediated gene editing was performed to investigate the effect of ATAD2 deletion on OC proliferation. The results demonstrated that ATAD2 is elevated in primary OC tissues compared with the adjacent normal tissue and metastases from the stomach. Genetic copy number amplification is a primary cause resulting in upregulation of ATAD2, and this was most frequently observed in OC. High ATAD2 expression was associated with advanced progression and predicted an unfavorable prognosis. ATAD2 could be used to identify cases of OC with a high proliferation signature and could label proliferating cells in OC. CRISPR-Cas9-mediated ATAD2 deletion resulted in a significant decrease in both cell proliferation and colony formation ability. Mechanistically, ATAD2-knockdown resulted in deactivation of the mitogen-activated protein kinase (MAPK) pathways, particularly the JNK-MAPK pathway, resulting in suppression of proliferation. Collectively, the data from the present study demonstrated that the ATD2 gene was frequently amplified and protein expression levels were upregulated in OC. Therefore, ATAD2 may serve as an attractive diagnostic and prognostic OC marker, which may be used to identify patients with primary OC, whom are most likely to benefit from ATAD2 gene-targeted proliferation intervention therapies.

Background It is generally accepted that colorectal cancer (CRC) originates from cancer stem cells (CSCs), which are responsible for CRC progression, metastasis and therapy resistance. The high heterogeneity of CSCs has precluded clinical application of CSC-targeting therapy. Here, we aimed to characterize the stemness landscapes and screen for certain patients more responsive to immunotherapy. Methods Twenty-six stem cell gene sets were acquired from StemChecker database. Consensus clustering algorithm was applied for stemness subtypes identification on 1,467 CRC samples from TCGA and GEO databases. The differences in prognosis, tumor microenvironment (TME) components, therapy responses were evaluated among subtypes. Then, the stemness-risk model was constructed by weighted gene correlation network analysis (WGCNA), Cox regression and random survival forest analyses, and the most important marker was experimentally verified. Results Based on single-sample gene set enrichment analysis (ssGSEA) enrichments scores, CRC patients were classified into three subtypes (C1, C2 and C3). C3 subtype exhibited the worst prognosis, highest macrophages M0 and M2 infiltrations, immune and stromal scores, and minimum sensitivity to immunotherapies, but was more sensitive to drugs like Bosutinib, Docetaxel, Elesclomol, Gefitinib, Lenalidomide, Methotrexate and Sunitinib. The turquoise module was identified by WGCNA that it was most positively correlated with C3 but most negatively with C2, and five hub genes in turquoise module were identified for stemness model construction. CRC patients with higher stemness scores exhibited worse prognosis, more immunosuppressive components in TME and lower immunotherapeutic responses. Additionally, the model’s immunotherapeutic prediction efficacy was further confirmed from two immunotherapy cohorts (anti-PD-L1 in IMvigor210 cohort and anti-PD-1 in GSE78220 cohort). Mechanistically, Gene Set Enrichment Analysis (GSEA) results revealed high stemness score group was enriched in interferon gamma response, interferon alpha response, P53 pathway, coagulation, apoptosis, KRAS signaling upregulation, complement, epithelial–mesenchymal transition (EMT) and IL6-mediated JAK-STAT signaling gene sets. Conclusions Our study characterized three stemness-related subtypes with distinct prognosis and TME patterns in CRC patients, and a 5-gene stemness-risk model was constructed by comprehensive bioinformatic analyses. We suggest our stemness model has prospective clinical implications for prognosis evaluation and might facilitate physicians selecting prospective responders for preferential use of current immune checkpoint inhibitors.

No abstract