Highly robust model of transcription regulator activity predicts breast cancer overall survival

Background: While several multigene signatures are available for predicting breast cancer prognosis, particularly in early stage disease, effective molecular indicators are needed, especially for triple-negative carcinomas, to improve treatments and predict diagnostic outcomes. The objective of this study was to identify transcriptional regulatory networks to better understand mechanisms giving rise to breast cancer development and to incorporate this information into a model for predicting clinical outcomes. Methods: Gene expression profiles from 1097 breast cancer patients were retrieved from The Cancer Genome Atlas (TCGA). Breast cancer-specific transcription regulatory information was identified by considering the binding site information from ENCODE and the top co-expressed targets in TCGA using a nonlinear approach. We then used this information to predict breast cancer patient survival outcome. Result: We built a multiple regulator-based prediction model for breast cancer. This model was validated in more than 5000 breast cancer patients from the Gene Expression Omnibus (GEO) databases. We demonstrated our regulator model was significantly associated with clinical stage and that cell cycle and DNA replication related pathways were significantly enriched in high regulator risk patients. Conclusion: Our findings demonstrate that transcriptional regulator activities can predict patient survival. This finding provides additional biological insights into the mechanisms of breast cancer progression

Transcription factor expression as a predictor of colon cancer prognosis: a machine learning practice

Background Colon cancer is one of the leading causes of cancer deaths in the USA and around the world. Molecular level characters, such as gene expression levels and mutations, may provide profound information for precision treatment apart from pathological indicators. Transcription factors function as critical regulators in all aspects of cell life, but transcription factors-based biomarkers for colon cancer prognosis were still rare and necessary. Methods We implemented an innovative process to select the transcription factors variables and evaluate the prognostic prediction power by combining the Cox PH model with the random forest algorithm. We picked five top-ranked transcription factors and built a prediction model by using Cox PH regression. Using Kaplan-Meier analysis, we validated our predictive model on four independent publicly available datasets (GSE39582, GSE17536, GSE37892, and GSE17537) from the GEO database, consisting of 925 colon cancer patients. Results A five-transcription-factors based predictive model for colon cancer prognosis has been developed by using TCGA colon cancer patient data. Five transcription factors identified for the predictive model is HOXC9, ZNF556, HEYL, HOXC4 and HOXC6. The prediction power of the model is validated with four GEO datasets consisting of 1584 patient samples. Kaplan-Meier curve and log-rank tests were conducted on both training and validation datasets, the difference of overall survival time between predicted low and high-risk groups can be clearly observed. Gene set enrichment analysis was performed to further investigate the difference between low and high-risk groups in the gene pathway level. The biological meaning was interpreted. Overall, our results prove our prediction model has a strong prediction power on colon cancer prognosis. Conclusions Transcription factors can be used to construct colon cancer prognostic signatures with strong prediction power. The variable selection process used in this study has the potential to be implemented in the prognostic signature discovery of other cancer types. Our five TF-based predictive model would help with understanding the hidden relationship between colon cancer patient survival and transcription factor activities. It will also provide more insights into the precision treatment of colon cancer patients from a genomic information perspective

Identification of the shared gene signature and biological mechanism between type 2 diabetes and colorectal cancer

Background: The correlation of type 2 diabetes mellitus (T2DM) with colorectal cancer (CRC) has garnered considerable attention in the scientific community. Despite this, the molecular mechanisms underlying the interaction between these two diseases are yet to be elucidated. Hence, the present investigation aims to explore the shared gene signatures, immune profiles, and drug sensitivity patterns that exist between CRC and T2DM.Methods: RNA sequences and characteristics of patients with CRC and T2DM were retrieved from The Cancer Genome Atlas and Gene Expression Omnibus databases. These were investigated using weighted gene co-expression network analysis (WGCNA) to determine the co-expression networks linked to the conditions. Genes shared between CRC and T2DM were analyzed by univariate regression, followed by risk prognosis assessment using the LASSO regression model. Various parameters were assessed through different software such as the ESTIMATE, CIBERSORT, AND SSGSEA utilized for tumor immune infiltration assessment in the high- and low-risk groups. Additionally, pRRophetic was utilized to assess the sensitivity to chemotherapeutic agents in both groups. This was followed by diagnostic modeling using logistic modeling and clinical prediction modeling using the nomogram.Results: WGCNA recognized four and five modules that displayed a high correlation with T2DM and CRC, respectively. In total, 868 genes were shared between CRC and T2DM, with 14 key shared genes being identified in the follow-up analysis. The overall survival (OS) of patients in the low-risk group was better than that of patients in the high-risk group. In contrast, the high-risk group exhibited higher expression levels of immune checkpoints The Cox regression analyses established that the risk-score model possessed independent prognostic value in predicting OS. To facilitate the prediction of OS and cause-specific survival, the nomogram was established utilizing the Cox regression model.Conclusion: The T2DM + CRC risk-score model enabled independent prediction of OS in individuals with CRC. Moreover, these findings revealed novel genes that hold promise as therapeutic targets or biomarkers in clinical settings

Identification of Land Use Conflicts in Shandong Province from an Ecological Security Perspective

Accurate identification of land use conflicts is an important prerequisite for the rational allocation of land resources and optimizing the production–living–ecological space pattern. Previous studies used suitability assessment and landscape pattern indices to identify land use conflicts. However, research on land use conflict identification from the perspective of ecological security is insufficient and not conducive to regional ecological, environmental protection, and sustainable development. Based on ecological security, this study takes Shandong Province as an example and comprehensively evaluates the importance of ecosystem service function and environmental sensitivity. It identifies the ecological source, and extracts ecological corridors with a minimum cumulative resistance model from which ecological security patterns are constructed. It identifies land use conflicts through spatial overlay analysis of arable land and construction land. The results show that: (1) Shandong Province has formed an ecological security pattern of “two ecological barriers, two belts, and eight cores” with an area of 15,987 km2. (2) The level of arable land–ecological space conflict is low, at 39.76%. The proportions of serious and moderate conflicts are 13.44% and 26.97%, respectively, distributed primarily on the Jiaodong Peninsula and the low hill areas of Ludong. (3) Construction land–ecological space conflict is reasonably stable and controllable, at 76.39%, occurring mainly around urban construction land, with serious and moderate conflict concentrated in the eastern coastal areas, mainly between rural settlements and ecologically safe space in the region. This study has important theoretical and practical reference values for identifying land use conflicts, protecting regional ecological security, and optimizing land use patterns

Land Use Conflict Identification Coupled with Ecological Protection Priority in Jinan City, China

Land use conflicts exacerbate soil erosion and reduce biodiversity, which is detrimental to sustainable development. Multiple methods such as multi-criteria evaluation and landscape pattern indexes can identify land use conflicts, but few studies conform to the concept of green development. The concept of green development gives priority to ecological protection and coordinates the relationship between production development, food production and ecological protection to achieve sustainable development. Taking Jinan City (China) as the study area, we identified the ecological source areas by evaluating the importance of ecosystem service functions and ecological sensitivity, then extracted and optimized the ecological corridor network (using the minimum cumulative resistance model and gravity model), and constructed the ecological security pattern. Spatial overlay analysis of cultivated land, construction land, and the ecological security pattern was performed to identify the types and intensity of land use conflicts. Spatially, we found that ecological land was in more serious conflict with cultivated land than construction land. Different types of land use conflicts have significant differences in spatial distribution. The key to land use conflict mediation in Jinan City is to balance food security with the improvements in the quality of the ecological environment. Hence, it is necessary to delineate the main functional zones and formulate tailored land use conflict mediation strategies in each zone. The method for land use conflict identification proposed here follows the principle of giving priority to ecological protection, providing a scientific reference for the utilization and protection of territorial space in other similar areas

Spatiotemporal Variation of Driving Forces for Settlement Expansion in Different Types of Counties

Understanding the process of settlement expansion and the spatiotemporal variation of driving forces is the foundation of rational and specific planning for sustainable development. However, little attention has been paid to the spatiotemporal differences of driving forces among different counties, especially when they are representatives of different development types. This study used Guanyun, Kunshan and Changshu as case studies, and binary logistic regression was employed. The results showed that the expansion rates of Kunshan and Changshu were 5.55 and 3.93 times higher than that of Guanyun. The combinations and relative importance of drivers varied with counties and periods. The change in the number of driving forces can be divided into three stages: increasing stage, decreasing stage, and stable stage. In the relatively developed counties, Kunshan and Changshu, the importance of population is decreased, while it remain an important factor in the less developed county, Guanyun. In addition, the effect of GDP stays the same in Kunshan while it becomes the most important factor in Changshu. The distance to the main road and the distance to town are increasingly important in Kunshan and Guanyun, and distance to town has been the only common factor in the last period, indicating the discrepancy is increased. The relative importance of distance to a lake in Kunshan and Changshu increased, reflecting the role of increasing tourism in accelerating settlement expansion

Multi-Dimensional Feature Recognition and Policy Implications of Rural Human–Land Relationships in China

Rural decline has become an indisputable fact and a global issue. As a developing country, China is simultaneously facing unprecedented rapid urbanization and severe rural decline. The coordinated development of its rural human–land relationship is therefore of great significance for ensuring the country’s food security and achieving both rural revitalization and sustainable development. Yet, the related research on this complex subject has mostly focused on a single element: rural settlements. Since studies of the rural human–land relationship tend to only discuss the coordinated change in rural populations vis-à-vis rural settlement area, their degree of spatial matching and intensive utilization level of rural settlements has been largely overlooked. To rectify this imbalance, using data on rural populations and rural settlement area in counties of Shandong Province in 2009 and 2018, this paper applied the methods of per capita rural settlement area, the Theil index, and Tapio’s decoupling model to quantitatively identify the rural human–land relationship along three dimensions: intensive utilization level, spatial matching degree, and change coordination degree. The results revealed that the per capita rural settlement area in Shandong Province was as high as 212.18 m2/person in 2018, which exceeded the standard to varying degrees in all cities, having an overall geographical pattern of being high in the north and low in the south. The Theil index for all cities was small, which indicates that the spatial matching between rural population and rural settlements is high. To sum up, there are small differences in the utilization of rural settlements among cities, and their extensive utilization of rural settlements is a common phenomenon. In addition, the relationship between the changes in the rural population size and rural settlement area corresponded to a discordant state, in the form of strong negative decoupling, expansive negative decoupling, and expansive coupling; however, among them, the strong negative decoupling type was the dominant type. It is worth noting that all of these three types will exacerbate the extensive utilization of rural settlements. Accordingly, this paper proposes policies and measures, such as the paid withdrawal of rural homesteads, an expanded scope of homestead transfer, cross-regional “increasing versus decreasing balance”, classified promotion of rural revitalization, and improved village planning

The complete chloroplast genome of Crataegus hupehensis Sarg. (Rosaceae), a medicinal and edible plant in China

Crataegus hupehensis Sarg. is well-known for its medicinal and nutritive value. In this study, the complete chloroplast genome sequence of C. hupehensis was determined by using Illumina high-throughput sequencing approach. The complete chloroplast genome is 159,766 bp with 36.6% GC content. It contained a pair of inverted repeat regions of 26,385 bp, a large single-copy region of 87,852 bp, and a small single-copy region of 19,144 bp. It contained 112 distinct genes, including 78 protein-coding genes, 4 ribosomal RNA genes, and 30 transfer RNA genes. Phylogenetic analysis based on chloroplast genomes indicated that C. hupehensisis was closely related to C. kansuensis and C. marshallii in the subfamily Maloideae. This complete chloroplast genome will provide valuable insight into evolution, molecular breeding, and phylogenetic analysis of Crataegus species

Identification of Land Use Conflicts in Shandong Province from an Ecological Security Perspective

Accurate identification of land use conflicts is an important prerequisite for the rational allocation of land resources and optimizing the production–living–ecological space pattern. Previous studies used suitability assessment and landscape pattern indices to identify land use conflicts. However, research on land use conflict identification from the perspective of ecological security is insufficient and not conducive to regional ecological, environmental protection, and sustainable development. Based on ecological security, this study takes Shandong Province as an example and comprehensively evaluates the importance of ecosystem service function and environmental sensitivity. It identifies the ecological source, and extracts ecological corridors with a minimum cumulative resistance model from which ecological security patterns are constructed. It identifies land use conflicts through spatial overlay analysis of arable land and construction land. The results show that: (1) Shandong Province has formed an ecological security pattern of “two ecological barriers, two belts, and eight cores” with an area of 15,987 km2. (2) The level of arable land–ecological space conflict is low, at 39.76%. The proportions of serious and moderate conflicts are 13.44% and 26.97%, respectively, distributed primarily on the Jiaodong Peninsula and the low hill areas of Ludong. (3) Construction land–ecological space conflict is reasonably stable and controllable, at 76.39%, occurring mainly around urban construction land, with serious and moderate conflict concentrated in the eastern coastal areas, mainly between rural settlements and ecologically safe space in the region. This study has important theoretical and practical reference values for identifying land use conflicts, protecting regional ecological security, and optimizing land use patterns