Development and validation of an immune-related gene signature for predicting the radiosensitivity of lower-grade gliomas.

Radiotherapy is an important treatment modality for lower-grade gliomas (LGGs) patients. This analysis was conducted to develop an immune-related radiosensitivity gene signature to predict the survival of LGGs patients who received radiotherapy. The clinical and RNA sequencing data of LGGs were obtained from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). Lasso regression analyses were used to construct a 21-gene signature to identify the LGGs patients who could benefit from radiotherapy. Based on this radiosensitivity signature, patients were classified into a radiosensitive (RS) group and a radioresistant (RR) group. According to the Kaplan-Meier analysis results of the TCGA dataset and the two CGGA validation datasets, the RS group had a higher overall survival rate than that of the RR group. This gene signature was RT-specific and an independent prognostic indicator. The nomogram model performed well in predicting 3-, and 5-year survival of LGGs patients after radiotherapy by this gene signature and other clinical factors (age, sex, grade, IDH mutations, 1p/19q codeletion). In summary, this signature is a powerful supplement to the prognostic factors of LGGs patients with radiotherapy and may provide an opportunity to incorporate individual tumor biology into clinical decision making in radiation oncology.

Integration of immune and hypoxia gene signatures improves the prediction of radiosensitivity in breast cancer.

Immunity and hypoxia are two important factors that affect the response of cancer patients to radiotherapy. At the same time, considering the limited predictive value of a single predictive model and the uncertainty of grouping patients near the cutoff value, we developed and validated a combined model based on immune- and hypoxia-related gene expression profiles to predict the radiosensitivity of breast cancer patients. This study was based on breast cancer data from The Cancer Genome Atlas (TCGA). Spike-and-slab Lasso regression analysis was performed to select three immune-related genes and develop a radiosensitivity model. Lasso Cox regression modeling selected 11 hypoxia-related genes for development of radiosensitivity model. Three independent datasets (Molecular Taxonomy of Breast Cancer International Consortium [METABRIC], E-TABM-158, GSE103746) were used to validate the predictive value of radiosensitivity signatures. In the TCGA dataset, the 10-year survival probabilities of the immune radioresistant (IRR) and hypoxia radioresistant (HRR) groups were 0.189 (0.037, 0.973) and 0.477 (0.293, 0.776), respectively. The 10-year survival probabilities of the immune radiosensitive (IRS) and hypoxia radiosensitive (HRS) groups were 0.778 (0.676, 0.895) and 0.824 (0.723, 0.939), respectively. Based on these two gene signatures, we further constructed a combined model and divided all patients into three groups (IRS/HRS, mixed, IRR/HRR). We identified the IRS/HRS patients most likely to benefit from radiotherapy; the 10-year survival probability was 0.886 (0.806, 0.976). The 10-year survival probability of the IRR/HRR group was 0. In conclusion, a combined model integrating immune- and hypoxia-related gene signatures could effectively predict the radiosensitivity of breast cancer and more accurately identify radiosensitive and radioresistant patients than a single model.

A Radiosensitivity Prediction Model Developed Based on Weighted Correlation Network Analysis of Hypoxia Genes for Lower-Grade Glioma.

Background and Purpose: Hypoxia is one of the basic characteristics of the physical microenvironment of solid tumors. The relationship between radiotherapy and hypoxia is complex. However, there is no radiosensitivity prediction model based on hypoxia genes. We attempted to construct a radiosensitivity prediction model developed based on hypoxia genes for lower-grade glioma (LGG) by using weighted correlation network analysis (WGCNA) and least absolute shrinkage and selection operator (Lasso). Methods: In this research, radiotherapy-related module genes were selected after WGCNA. Then, Lasso was performed to select genes in patients who received radiotherapy. Finally, 12 genes (AGK, ETV4, PARD6A, PTP4A2, RIOK3, SIGMAR1, SLC34A2, SMURF1, STK33, TCEAL1, TFPI, and UROS) were included in the model. A radiosensitivity-related risk score model was established based on the overall rate of The Cancer Genome Atlas (TCGA) dataset in patients who received radiotherapy. The model was validated in TCGA dataset and two Chinese Glioma Genome Atlas (CGGA) datasets. A novel nomogram was developed to predict the overall survival of LGG patients. Results: We developed and verified a radiosensitivity-related risk score model based on hypoxia genes. The radiosensitivity-related risk score served as an independent prognostic indicator. This radiosensitivity-related risk score model has prognostic prediction ability. Moreover, a nomogram integrating risk score with age and tumor grade was established to perform better for predicting 1-, 3-, and 5-year survival rates. Conclusions: We developed and validated a radiosensitivity prediction model that can be used by clinicians and researchers to predict patient survival rates and achieve personalized treatment of LGG.

Highly birefringent hollow-core anti-resonant terahertz fiber with a thin strut microstructure.

A novel highly birefringent and low transmission loss hollow-core anti-resonant (HC-AR) fiber with a central strut is proposed for terahertz waveguiding. To the best of our knowledge, it is the first time that a design of a highly birefringent terahertz fiber based on the hybrid guidance mechanism of the anti-resonant mechanism and the total internal reflection mechanism is provided. Several HC-AR fibers with both ultra-low transmission loss and ultra-low birefringence have been achieved in the near-infrared optical communication band. We propose a HC-AR fiber design in terahertz band by introducing a microstructure in the fiber core which leads to tremendous improvement in birefringence. Calculated results indicate that the proposed HC-AR fiber achieves a birefringence higher than 10-2 in a wide wavelength range. In addition, low relative absorption loss of 0.8% (8.6%) and negligible confinement loss of 1.69×10-4 dB/cm (9.14×10-3 dB/cm) for x-polarization (y-polarization) mode at 1THz are obtained. Furthermore, the main parameters of the fiber structure are evaluated and discussed, proving that the HC-AR fiber possesses great design and fabrication tolerance. Further investigation of the proposed HC-AR fiber also suggests a good balance between birefringence and transmission loss which can be achieved by changing strut thickness to cater numerous applications ideally.

FEN1 Status and Its Correlation with Clinicopathologic Characteristic in Colorectal Cancer.

OBJECTIVE: The goal of this study was to investigate the status of FEN1 in colorectal cancer (CRC) and determine the potential correlation between FEN1 expression level and clinicopathological parameters in CRC patients. METHODS: Expression of FEN1 in CRC tissue on tissue microarray was detected using immunohistochemistry (IHC). The relationship between FEN1 expression status and clinicopathologic characteristics of CRC was analyzed by the Chi-square test. The survival data of TCGA Colon Cancer (COAD) were obtained from ucsc xena browser (https://xenabrowser.net/). Patients were separated into higher and lower expression groups by median FEN1 expression. The association with prognosis of CRC patients was determined by Kaplan-Meier survival analysis with Log-rank test. RESULTS: FEN1expression level and cellular localization had wide variability among different individuals; we classified the staining results into four types: both positive in nucleus and cytoplasm, both negative in nucleus and cytoplasm, only positive in the nucleus, only positive in the cytoplasm. Moreover, FEN1 expression status only correlated with patient's metastasis status, and the patients in the NLCL group showed more risk of cancer cell metastasis. CONCLUSION: Our results indicate that FEN1 expression level and cellular localization had wide variability in CRC and is not a promising biomarker in CRC.

Explore association of genes in PDL1/PD1 pathway to radiotherapy survival benefit based on interaction model strategy.

PURPOSE: To explore the association of genes in "PD-L1 expression and PD-1 check point pathway in cancer" to radiotherapy survival benefit. METHODS AND MATERIALS: Gene expression data and clinical information of cancers were downloaded from TCGA. Radiotherapy survival benefit was defined based on interaction model. Fast backward multivariate Cox regression was performed using stacking multiple interpolation data to identify radio-sensitive (RS) genes. RESULTS: Among the 73 genes in PD-L1/PD-1 pathway, we identified 24 RS genes in BRCA data set, 25 RS genes in STAD data set and 20 RS genes in HNSC data set, with some crossover genes. Theoretically, there are two types of RS genes. The expression level of Type I RS genes did not affect patients' overall survival (OS), but when receiving radiotherapy, patients with different expression level of Type I RS genes had varied survival benefit. Oppositely, Type II RS genes affected patients' OS. And when receiving radiotherapy, those with lower OS could benefit a lot. Type II RS genes in BRCA had strong positive correlation and closely biological interactions. When performing cluster analysis using these related Type II RS genes, patients could be divided into RS group and non-RS group in BRCA and METABRIC data sets. CONCLUSIONS: Our study explored potential radio-sensitive biomarkers of several main cancer types in an important tumor immune checkpoint pathway and revealed a strong association between this pathway and radiotherapy survival benefit. New types of RS genes could be identified based on expanded definition to RS genes.

Developing ZNF Gene Signatures Predicting Radiosensitivity of Patients with Breast Cancer.

Adjuvant radiotherapy is one of the main treatment methods for breast cancer, but its clinical benefit depends largely on the characteristics of the patient. This study aimed to explore the relationship between the expression of zinc finger (ZNF) gene family proteins and the radiosensitivity of breast cancer patients. Clinical and gene expression data on a total of 976 breast cancer samples were obtained from The Cancer Genome Atlas (TCGA) database. ZNF gene expression was dichotomized into groups with a higher or lower level than the median level of expression. Univariate and multivariate Cox regression analyses were used to evaluate the relationship between ZNF gene expression levels and radiosensitivity. The Molecular Taxonomy Data of the International Federation of Breast Cancer (METABRIC) database was used for validation. The results revealed that 4 ZNF genes were possible radiosensitivity markers. High expression of ZNF644 and low expression levels of the other 3 genes (ZNF341, ZNF541, and ZNF653) were related to the radiosensitivity of breast cancer. Hierarchical cluster, Cox, and CoxBoost analysis based on these 4 ZNF genes indicated that patients with a favorable 4-gene signature had better overall survival on radiotherapy. Thus, this 4-gene signature may have value for selecting those patients most likely to benefit from radiotherapy. ZNF gene clusters could act as radiosensitivity signatures for breast cancer patients and may be involved in determining the radiosensitivity of cancer.

Development and Validation of a Radiosensitivity Prediction Model for Lower Grade Glioma Based on Spike-and-Slab Lasso.

BACKGROUND AND PURPOSE: Lower grade glioma (LGG) is one of the leading causes of death world worldwide. We attempted to develop and validate a radiosensitivity model for predicting the survival of lower grade glioma by using spike-and-slab lasso Cox model. METHODS: In this research, differentially expressed genes based on tumor microenvironment was obtained to further analysis. Log-rank test was used to identify genes in patients who received radiotherapy and patients who did not receive radiotherapy, respectively. Then, spike-and-slab lasso was performed to select genes in patients who received radiotherapy. Finally, three genes (INA, LEPREL1 and PTCRA) were included in the model. A radiosensitivity-related risk score model was established based on overall rate of TCGA dataset in patients who received radiotherapy. The model was validated in TCGA dataset that PFS as endpoint and two CGGA datasets that OS as endpoint. A novel nomogram integrated risk score with age and tumor grade was developed to predict the OS of LGG patients. RESULTS: We developed and verified a radiosensitivity-related risk score model. The radiosensitivity-related risk score is served as an independent prognostic indicator. This radiosensitivity-related risk score model has prognostic prediction ability. Moreover, the nomogram integrated risk score with age and tumor grade was established to perform better for predicting 1, 3, 5-year survival rate. CONCLUSIONS: This model can be used by clinicians and researchers to predict patient's survival rates and achieve personalized treatment of LGG.

Impact of NSLAB on Kazakh cheese flavor.

Xinjiang is a multiethnic region of China. Traditionally, most ethnic minorities are known to produce and consume cheese. Nomadic people have been reported to use lactic acid bacteria (LAB) for decades to produce fermented dairy products as part of a balanced diet. Non-starter LAB (NSLAB) contribute to different degrees of ripening, depending on the cheese variety. In the present study, we screened three types of NSLAB with good proteolysis and autolytic abilities from traditional Kazakh cheese: Pediococcus acidilactici R3-5, Staphylococcus epidermidis R4-2, and Lactobacillus rhamnosus R9-6. A control (no NSLAB) was also included, resulting in four distinct types of cheese samples. We used gas chromatography-mass spectrometry and the electronic nose system to identify volatile compounds and analyze the effect of NSLAB on cheese flavor at the ripening stage. The physicochemical indicators changed significantly during the ripening of Kazakh cheese. Compared with the control, the protein content, free fatty acid content, pH, flavor compounds, and odor profiles of the test cheeses were significantly different. The major chemical differences among cheeses were the synthesis of some key volatile components (ethyl caprylate, ethyl caprate, myristyl carbonate, capric acid, caprylic acid, nonanal, and benzyl alcohol). NSLAB can be used as an adjunct starter to make Kazakh cheese and the use of NSLAB affected the cheese flavor quality positively.

More evidence for prediction model of radiosensitivity.

With the development of precision medicine, searching for potential biomarkers plays a major role in personalized medicine. Therefore, how to predict radiosensitivity to improve radiotherapy is a burning question. The definition of radiosensitivity is complex. Radiosensitive gene/biomarker can be useful for predicting which patients would benefit from radiotherapy. The discovery of radiosensitivity biomarkers require multiple pieces of evidence. A prediction model of breast cancer radiosensitivity based on six genes was established. We had put forward some supplements on the basis of the present study. We found that there were no differences between high- and low-risk scores in the non-radiotherapy group. Patients who received radiotherapy had a significantly better overall survival than non-radiotherapy patients in the predicted low-risk score patients. Furthermore, there was no difference between radiotherapy group and non-radiotherapy group in the high-risk score group. Those results firmly supported the prediction model of radiosensitivity. In addition, building a radiosensitivity prediction model was systematically discussed. Genes of model could be screened by different methods, such as Cox regression analysis, Lasso Cox regression method, random forest algorithm and other methods. In the future, precision radiotherapy might depend on the combination of multi-omics data and high dimensional image data.

Genes Involved in the PD-L1 Pathway Might Associate with Radiosensitivity of Patients with Gastric Cancer.

The PD-1/PD-L1 pathway plays an important role in the treatment of cancers as immune checkpoint. However, the association of genes involved in the PD-L1 pathway and radiosensitivity of gastric cancer has not been fully characterized. This study aims to explore the relationship between the expression levels of genes involved in the PD-L1 pathway and radiosensitivity for gastric cancer patients. A total of 367 patients with clinical survival information and radiotherapy information were obtained in The Cancer Genome Atlas (TCGA). Genes involved in the PD-L1 pathway were categorized into high and low expression level groups according to the median value. The Cox proportional hazards model was used to find the association between gene expression level and radiosensitivity. The results show that high expression levels of CD274, EGFR, RAF1, RPS6KB1, PIK3CA, MTOR, CHUK, NFKB1, TRAF6, FOS, NFATC1, and HIF1A were associated with radiosensitivity of gastric cancer. While low expression level of HRAS was also associated with radiosensitivity in gastric cancer. The rates of a new tumor event and disease progression were lower for radiosensitivity patients than other patients. The relationship between the expression level of CD274 and other genes involved in the PD-L1 pathway is significant. GO (Gene Ontology) analysis shows that the biological process of 13 genes was mainly related to innate immune response activating the cell surface receptor signaling pathway. KEGG analysis demonstrated that 13 genes in gastric cancer are mainly related to the PD-L1 expression and PD-1 checkpoint pathway in cancer. The correlation between the expression level of CD274 and other genes involved in the PD-L1 pathway is significant. The present study offered more evidence for using PD-L1 and genes involved in the PD-L1 pathway as potential biomarkers to predict radiosensitive patients with gastric cancer.