HGFK1 Enhances the Anti-Tumor Effects of Angiogenesis Inhibitors via Inhibition of CD90+ CSCs in Hepatocellular Carcinoma.

The combination of anti-angiogenesis agents with immune-checkpoint inhibitors is a promising treatment for patients with advanced hepatocellular carcinoma (HCC); however, therapeutic resistance caused by cancer stem cells present in tumor microenvironments remains to be overcome. In this study, we report for the first time that the Kringle 1 domain of human hepatocyte growth-factor α chain (HGFK1), a previously described anti-angiogenesis peptide, repressed the sub-population of CD90+ cancer stem cells (CSCs) and promoted their differentiation and chemotherapy sensitivity mainly through downregulation of pre-Met protein expression and inhibition of Wnt/ß-catenin and Notch pathways. Furthermore, we showed that the i.p. injection of PH1 (a tumor-targeted and biodegradable co-polymer), medicated plasmids encoding Endostatin (pEndo), HGFK1 genes (pEndo), and a combination of 50% pEndo + 50% pHGFK1 all significantly suppressed tumor growth and prolonged the survival of the HCC-bearing mice. Importantly, the combined treatment produced a potent synergistic effect, with 25% of the mice showing the complete clearance of the tumor via a reduction in the microvessel density (MVD) and the number of CD90+ CSCs in the tumor tissues. These results suggest for the first time that HGFK1 inhibits the CSCs of HCC. Furthermore, the combination of two broad-spectrum anti-angiogenic factors, Endo and HGFK1, is the optimal strategy for the development of effective anti-HCC drugs.

Development of a novel hypoxia-immune-related LncRNA risk signature for predicting the prognosis and immunotherapy response of colorectal cancer.

Background: Colorectal cancer (CRC) is one of the most common digestive system tumors worldwide. Hypoxia and immunity are closely related in CRC; however, the role of hypoxia-immune-related lncRNAs in CRC prognosis is unknown. Methods: Data used in the current study were sourced from the Gene Expression Omnibus and The Cancer Genome Atlas (TCGA) databases. CRC patients were divided into low- and high-hypoxia groups using the single-sample gene set enrichment analysis (ssGSEA) algorithm and into low- and high-immune groups using the Estimation of STromal and Immune cells in MAlignant Tumours using Expression data (ESTIMATE) algorithm. Differentially expressed lncRNAs (DElncRNAs) between low- and high-hypoxia groups, low- and high-immune groups, and tumor and control samples were identified using the limma package. Hypoxia-immune-related lncRNAs were obtained by intersecting these DElncRNAs. A hypoxia-immune-related lncRNA risk signature was developed using univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analyses. The tumor microenvironments in the low- and high-risk groups were evaluated using ssGSEA, ESTIMATE, and the expression of immune checkpoints. The therapeutic response in the two groups was assessed using TIDE, IPS, and IC50. A ceRNA network based on signature lncRNAs was constructed. Finally, we used RT-qPCR to verify the expression of hypoxia-immune-related lncRNA signatures in normal and cancer tissues. Results: Using differential expression analysis, and univariate Cox and LASSO regression analyses, ZNF667-AS1, LINC01354, LINC00996, DANCR, CECR7, and LINC01116 were selected to construct a hypoxia-immune-related lncRNA signature. The performance of the risk signature in predicting CRC prognosis was validated in internal and external datasets, as evidenced by receiver operating characteristic curves. In addition, we observed significant differences in the tumor microenvironment and immunotherapy response between low- and high-risk groups and constructed a CECR7-miRNA-mRNA regulatory network in CRC. Furthermore, RT-qPCR results confirmed that the expression patterns of the six lncRNA signatures were consistent with those in TCGA-CRC cohort. Conclusion: Our study identified six hypoxia-immune-related lncRNAs for predicting CRC survival and sensitivity to immunotherapy. These findings may enrich our understanding of CRC and help improve CRC treatment. However, large-scale long-term follow-up studies are required for verification.

High-resolution metabolomic biomarkers for lung cancer diagnosis and prognosis.

Lung cancer is the leading cause of human cancer mortality due to the lack of early diagnosis technology. The low-dose computed tomography scan (LDCT) is one of the main techniques to screen cancers. However, LDCT still has a risk of radiation exposure and it is not suitable for the general public. In this study, plasma metabolic profiles of lung cancer were performed using a comprehensive metabolomic method with different liquid chromatography methods coupled with a Q-Exactive high-resolution mass spectrometer. Metabolites with different polarities (amino acids, fatty acids, and acylcarnitines) can be detected and identified as differential metabolites of lung cancer in small volumes of plasma. Logistic regression models were further developed to identify cancer stages and types using those significant biomarkers. Using the Variable Importance in Projection (VIP) and the area under the curve (AUC) scores, we have successfully identified the top 5, 10, and 20 metabolites that can be used to differentiate lung cancer stages and types. The discrimination accuracy and AUC score can be as high as 0.829 and 0.869 using the five most significant metabolites. This study demonstrated that using 5 + metabolites (Palmitic acid, Heptadecanoic acid, 4-Oxoproline, Tridecanoic acid, Ornithine, and etc.) has the potential for early lung cancer screening. This finding is useful for transferring the diagnostic technology onto a point-of-care device for lung cancer diagnosis and prognosis.