Transcriptome and small RNA analysis unveils novel insights into the C4 gene regulation in sugarcane.

MAIN CONCLUSION: This study reveals miRNA indirect regulation of C4 genes in sugarcane through transcription factors, highlighting potential key regulators like SsHAM3a. C4 photosynthesis is crucial for the high productivity and biomass of sugarcane, however, the miRNA regulation of C4 genes in sugarcane remains elusive. We have identified 384 miRNAs along the leaf gradients, including 293 known miRNAs and 91 novel miRNAs. Among these, 86 unique miRNAs exhibited differential expression patterns, and we identified 3511 potential expressed targets of these differentially expressed miRNAs (DEmiRNAs). Analyses using Pearson correlation coefficient (PCC) and Gene Ontology (GO) enrichment revealed that targets of miRNAs with positive correlations are integral to chlorophyll-related photosynthetic processes. In contrast, negatively correlated pairs are primarily associated with metabolic functions. It is worth noting that no C4 genes were predicted as targets of DEmiRNAs. Our application of weighted gene co-expression network analysis (WGCNA) led to a gene regulatory network (GRN) suggesting miRNAs might indirectly regulate C4 genes via transcription factors (TFs). The GRAS TF SsHAM3a emerged as a potential regulator of C4 genes, targeted by miR171y and miR171am, and exhibiting a negative correlation with miRNA expression along the leaf gradient. This study sheds light on the complex involvement of miRNAs in regulating C4 genes, offering a foundation for future research into enhancing sugarcane's photosynthetic efficiency.

Molecular mechanisms of pancreatic cancer liver metastasis: the role of PAK2.

Background: Pancreatic cancer remains an extremely malignant digestive tract tumor, posing a significant global public health burden. Patients with pancreatic cancer, once metastasis occurs, lose all hope of cure, and prognosis is extremely poor. It is important to investigate liver metastasis of Pancreatic cancer in depth, not just because it is the most common form of metastasis in pancreatic cancer, but also because it is crucial for treatment planning and prognosis assessment. This study aims to delve into the mechanisms of pancreatic cancer liver metastasis, with the goal of providing crucial scientific groundwork for the development of future treatment methods and drugs. Methods: We explored the mechanisms of pancreatic cancer liver metastasis using single-cell sequencing data (GSE155698 and GSE154778) and bulk data (GSE71729, GSE19279, TCGA-PAAD). Initially, Seurat package was employed for single-cell data processing to obtain expression matrices for primary pancreatic cancer lesions and liver metastatic lesions. Subsequently, high-dimensional weighted gene co-expression network analysis (hdWGCNA) was used to identify genes associated with liver metastasis. Machine learning algorithms and COX regression models were employed to further screen genes related to patient prognosis. Informed by both biological understanding and the outcomes of algorithms, we meticulously identified the ultimate set of liver metastasis-related gene (LRG). In the study of LRG genes, various databases were utilized to validate their association with pancreatic cancer liver metastasis. In order to analyze the effects of these agents on tumor microenvironment, we conducted an in-depth analysis, including changes in signaling pathways (GSVA), cell differentiation (pseudo-temporal analysis), cell communication networks (cell communication analysis), and downstream transcription factors (transcription factor activity prediction). Additionally, drug sensitivity analysis and metabolic analysis were performed to reveal the effects of LRG on gemcitabine resistance and metabolic pathways. Finally, functional experiments were conducted by silencing the expression of LRG in PANC-1 and Bx-PC-3 cells to validate its influence to proliferation and invasiveness on PANC-1 and Bx-PC-3 cells. Results: Through a series of algorithmic filters, we identified PAK2 as a key gene promoting pancreatic cancer liver metastasis. GSVA analysis elucidated the activation of the TGF-beta signaling pathway by PAK2 to promote the occurrence of liver metastasis. Pseudo-temporal analysis revealed a significant correlation between PAK2 expression and the lower differentiation status of pancreatic cancer cells. Cell communication analysis revealed that overexpression of PAK2 promotes communication between cancer cells and the tumor microenvironment. Transcription factor activity prediction displayed the transcription factor network regulated by PAK2. Drug sensitivity analysis and metabolic analysis revealed the impact of PAK2 on gemcitabine resistance and metabolic pathways. CCK8 experiments showed that silencing PAK2 led to a decrease in the proliferative capacity of pancreatic cancer cells and scratch experiments demonstrated that low expression of PAK2 decreased invasion capability in pancreatic cancer cells. Flow cytometry reveals that PAK2 significantly inhibited apoptosis in pancreatic cancer cell lines. Molecules related to the TGF-beta pathway decreased with the inhibition of PAK2, and there were corresponding significant changes in molecules associated with EMT. Conclusion: PAK2 facilitated the angiogenic potential of cancer cells and promotes the epithelial-mesenchymal transition process by activating the TGF-beta signaling pathway. Simultaneously, it decreased the differentiation level of cancer cells, consequently enhancing their malignancy. Additionally, PAK2 fostered communication between cancer cells and the tumor microenvironment, augments cancer cell chemoresistance, and modulates energy metabolism pathways. In summary, PAK2 emerged as a pivotal gene orchestrating pancreatic cancer liver metastasis. Intervening in the expression of PAK2 may offer a promising therapeutic strategy for preventing liver metastasis of pancreatic cancer and improving its prognosis.

A dynamic visualization clinical tool constructed and validated based on the SEER database for screening the optimal surgical candidates for bone metastasis in primary kidney cancer.

The implementation of primary tumor resection (PTR) in the treatment of kidney cancer patients (KC) with bone metastases (BM) has been controversial. This study aims to construct the first tool that can accurately predict the likelihood of PTR benefit in KC patients with BM (KCBM) and select the optimal surgical candidates. This study acquired data on all patients diagnosed with KCBM during 2010-2015 from the Surveillance, Epidemiology, and End Results (SEER) database. Propensity score matching (PSM) was utilized to achieve balanced matching of PTR and non-PTR groups to eliminate selection bias and confounding factors. The median overall survival (OS) of the non-PTR group was used as the threshold to categorize the PTR group into PTR-beneficial and PTR-Nonbeneficial subgroups. Kaplan-Meier (K-M) survival analysis was used for comparison of survival differences and median OS between groups. Risk factors associated with PTR-beneficial were identified using univariate and multivariate logistic regression analyses. Receiver operating characteristic (ROC), area under the curve (AUC), calibration curves, and decision curve analysis (DCA) were used to validate the predictive performance and clinical utility of the nomogram. Ultimately, 1963 KCBM patients meeting screening criteria were recruited. Of these, 962 patients received PTR and the remaining 1061 patients did not receive PTR. After 1:1 PSM, there were 308 patients in both PTR and non-PTR groups. The K-M survival analysis results showed noteworthy survival disparities between PTR and non-PTR groups, both before and after PSM (p < 0.001). In the logistic regression results of the PTR group, histological type, T/N stage and lung metastasis were shown to be independent risk factors associated with PTR-beneficial. The web-based nomogram allows clinicians to enter risk variables directly and quickly obtain PTR beneficial probabilities. The validation results showed the excellent predictive performance and clinical utility of the nomograms for accurate screening of optimal surgical candidates for KCBM. This study constructed an easy-to-use nomogram based on conventional clinicopathologic variables to accurately select the optimal surgical candidates for KCBM patients.

The Effect of Nitrogen Functional Groups on Pb0, PbO, and PbCl2 Adsorption over a Carbonaceous Surface.

Lead (Pb) pollution, especially from the incineration of municipal solid waste (MSW), poses a significant threat to the environment. Among all the effective methods, activated carbon (AC) injection serves as an effective approach for lead removal from flue gas, while the modification of ACs emerges as a crucial pathway for enhancing Pb adsorption capacities. Density functional theory (DFT) is employed in this study to investigate the mechanisms underlying the enhanced adsorption of Pb species (Pb0, PbO, and PbCl2) on nitrogen-functionalized carbonaceous surfaces. The results show that nitrogen-containing groups substantially enhance lead adsorption capacity, with adsorption energies ranging from -526.18 to -288.31 kJ/mol on nitrogen-decorated carbonaceous surfaces, much higher than those on unmodified surfaces (-310.35 to -260.96 kJ/mol). Additionally, electrostatic potential and density-of-states analyses evidence that pyridinic nitrogen atoms remarkably expand charge distribution and strengthen orbital hybridization, thereby augmenting lead capture. This research elucidates the role of nitrogen-containing functional groups in lead adsorption, offering valuable insights for the development of highly efficient biomass-derived activated carbon sorbents for lead removal.

Analysis of survival benefit from primary tumor resection and individualized prediction of overall survival for lung cancer patients with bone metastasis: Worth it or not?

BACKGROUND: The clinical management of lung cancer (LC) patients with bone metastasis (BM) is still a significant challenge. This study aimed to explore the role of primary tumor resection (PTR) on survival outcome of LC patients with BM and to develop two web-based nomograms for predicting the overall survival (OS) of LC patients with BM who received PTR and those who did not. METHODS: We enrolled LC patients with BM from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015. Propensity score matching (PSM) was then conducted to balance the baseline characteristics of covariates between patients in surgery and non-surgery groups. Next, Kaplan-Meier analysis with log-rank test was performed to evaluate the survival benefit of PTR before and after PSM methods and to explore the impact of surgical resection extent on the prognosis of LC patients with BM and clinical outcomes in patients with different metastatic patterns. Cox proportional hazard regression analysis was then applied to determine the independent prognostic factors for OS of patients receiving PTR and did not receiving PTR, respectively. Subsequently, we constructed two individualized nomograms for predicting the 12-, 18- and 24-months OS. Finally, receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA) were generated to evaluate discrimination, accuracy and clinical utility of the nomograms. RESULTS: A total of 7747 eligible patients were included in this analysis. The survival analysis revealed that PTR was closely associated with better survival outcome among LC patients with BM(P < 0.05), while the survival benefit of PTR was suboptimal in patients presented with multiple metastases(P > 0.05). Besides, lobectomy shows best survival benefit. Two nomograms were then constructed based on independent prognostic factors of patients in the surgery group and the non-surgery group. The ROC curves showed good discrimination of the two nomograms, with the area under curve (AUC) of each time point being higher than 0.7 in both the training set and testing set. The calibration curves also demonstrated satisfactory consistency between actual survival and nomogram-predicted OS of both nomograms. The DCA showed high benefit of nomogram in a clinical context. Moreover, the study population was stratified into three groups based on the scores of the nomogram, and the survival analysis showed that this prognostic stratification was statistically significant (p < 0.05). CONCLUSIONS: This study showed that surgical resection of the primary site strategy can prolong survival of LC patients with BM to some extent, depending on different sites of metastasis and highly selected patients. Furthermore, the web-based nomograms showed significant accuracy in predicting OS for patients with or without surgery, which may provide valuable insights for patients' counseling and individualized decision-making for clinicians.

Self-fueling ferroptosis-inducing microreactors based on pH-responsive Lipiodol Pickering emulsions enable transarterial ferro-embolization therapy.

Lipiodol chemotherapeutic emulsions remain one of the main choices for the treatment of unresectable hepatocellular carcinoma (HCC) via transarterial chemoembolization (TACE). However, the limited stability of Lipiodol chemotherapeutic emulsions would lead to rapid drug diffusion, which would reduce the therapeutic benefit and cause systemic toxicity of administrated chemotherapeutics. Therefore, the development of enhanced Lipiodol-based formulations is of great significance to enable effective and safe TACE treatment. Herein, a stable water-in-oil Lipiodol Pickering emulsion (LPE) stabilized by pH-dissociable calcium carbonate nanoparticles and hemin is prepared and utilized for efficient encapsulation of lipoxygenase (LOX). The obtained LOX-loaded CaCO3&hemin-stabilized LPE (LHCa-LPE) showing greatly improved emulsion stability could work as a pH-responsive and self-fueling microreactor to convert polyunsaturated fatty acids (PUFAs), a main component of Lipiodol, to cytotoxic lipid radicals through the cascading catalytic reaction driven by LOX and hemin, thus inducing ferroptosis of cancer cells. As a result, such LHCa-LPE upon transcatheter embolization can effectively suppress the progression of orthotopic N1S1 HCC in rats. This study highlights a concise strategy to prepare pH-responsive and stable LPE-based self-fueling microreactors, which could serve as bifunctional embolic and ferroptosis-inducing agents to enable proof-of-concept transarterial ferro-embolization therapy of HCC.

Development and Validation of a Prognostic Model for Transarterial Chemoembolization in Unresectable Hepatocellular Carcinoma Based on Preoperative Serum Prealbumin.

Purpose: We aimed to develop a prognostic nomogram utilizing preoperative serum prealbumin levels to predict the overall survival (OS) in patients undergoing transarterial chemoembolization (TACE) for unresectable hepatocellular carcinoma (HCC). Patients and Methods: A total of 768 individuals with unresectable HCC who underwent TACE at three medical facilities in Suzhou between January 2007 December 2018 were included. The patient cohort was assigned to a training set (n = 461) and a validation set (n = 307). Cox regression analysis identified independent prognostic factors, which were then used to construct a prognostic nomogram. Internal validation was performed in the testing group, and its effectiveness and capability were evaluated with reference to the concordance index (C-index), area under the curve (AUC), calibration curve, and decision curve analysis (DCA). Results: Independent risk factors identified through Cox regression analyses included the BCLC stage, cirrhosis, invasion, tumor number, preoperative serum PALB, performance status (PS), and tumor size. The nomogram demonstrated a C-index of 0.734 (95% confidence interval (CI): 0.710-0.758) in the training set and 0.717 (95% CI: 0.678-0.756) in the validation set, indicating strong discriminatory ability. The nomogram also demonstrated favorable discriminatory performance with AUC values of 0.873, 0.820, and 0.833 for 1-, 2-, and 3-year OS, respectively, in the training set, and 0.854, 0.765, and 0.724 in the validation set. The AUC value of the nomogram (0.843) was significantly higher than that of the four conventional staging systems. Moreover, calibration graphs confirmed a strong concordance between the predicted and observed results. Furthermore, DCA underscored the significant clinical utility of the nomogram. Additionally, the low-risk group exhibited considerably superior rates of survival compared to the high-risk group. Conclusion: The developed nomogram demonstrated excellent prognostic capability, which served as a valuable tool for personalized clinical decision-making for patients with HCC.

A visualized model for identifying optimal candidates for aggressive locoregional surgical treatment in patients with bone metastases from breast cancer.

Background: The impact of surgical resection of primary (PTR) on the survival of breast cancer (BC) patients with bone metastasis (BM) has been preliminarily investigated, but it remains unclear which patients are suitable for this procedure. Finally, this study aims to develop a predictive model to screen BC patients with BM who would benefit from local surgery. Methods: BC patients with BM were identified using the Surveillance, Epidemiology, and End Results (SEER) database (2010 and 2015), and 39 patients were obtained for external validation from an Asian medical center. According to the status of local surgery, patients were divided into Surgery and Non-surgery groups. Propensity score matching (PSM) analysis was performed to reduce selection bias. Kaplan-Meier (K-M) survival and Cox regression analyses were conducted before and after PSM to study the survival difference between the two groups. The survival outcome and treatment modality were also investigated in patients with different metastatic patterns. The logistic regression analyses were utilized to determine significant surgery-benefit-related predictors, develop a screening nomogram and its online version, and quantify the beneficial probability of local surgery for BC patients with BM. Receiver operating characteristic (ROC) curves, the area under the curves (AUC), and calibration curves were plotted to evaluate the predictive performance and calibration of this model, whereas decision curve analysis (DCA) was used to assess its clinical usefulness. Results: This study included 5,625 eligible patients, of whom 2,133 (37.92%) received surgical resection of primary lesions. K-M survival analysis and Cox regression analysis demonstrated that local surgery was independently associated with better survival. Surgery provided significant survival benefits in most subgroups and metastatic patterns. After PSM, patients who received surgery had a longer survival time (OS: 46 months vs. 32 months, p < 0.001; CSS: 50 months vs. 34 months, p < 0.001). Logistic regression analysis determined six significant surgery-benefit-related variables: T stage, radiotherapy, race, liver metastasis, brain metastasis, and breast subtype. These factors were combined to establish the nomogram and a web probability calculator (https://sunshine1.shinyapps.io/DynNomapp/), with an AUC of 0.673 in the training cohort and an AUC of 0.640 in the validation cohort. The calibration curves exhibited excellent agreement. DCA indicated that the nomogram was clinically useful. Based on this model, surgery patients were assigned into two subsets: estimated sur-non-benefit and estimated sur-benefit. Patients in the estimated sur-benefit subset were associated with longer survival (median OS: 64 months vs. 33 months, P < 0.001). Besides, there was no difference in survival between the estimated sur-non-benefit subset and the non-surgery group. Conclusion: Our study further confirmed the significance of local surgery in BC patients with BM and proposed a novel tool to identify optimal surgical candidates.

Occlusion facial expression recognition based on feature fusion residual attention network.

Recognizing occluded facial expressions in the wild poses a significant challenge. However, most previous approaches rely solely on either global or local feature-based methods, leading to the loss of relevant expression features. To address these issues, a feature fusion residual attention network (FFRA-Net) is proposed. FFRA-Net consists of a multi-scale module, a local attention module, and a feature fusion module. The multi-scale module divides the intermediate feature map into several sub-feature maps in an equal manner along the channel dimension. Then, a convolution operation is applied to each of these feature maps to obtain diverse global features. The local attention module divides the intermediate feature map into several sub-feature maps along the spatial dimension. Subsequently, a convolution operation is applied to each of these feature maps, resulting in the extraction of local key features through the attention mechanism. The feature fusion module plays a crucial role in integrating global and local expression features while also establishing residual links between inputs and outputs to compensate for the loss of fine-grained features. Last, two occlusion expression datasets (FM_RAF-DB and SG_RAF-DB) were constructed based on the RAF-DB dataset. Extensive experiments demonstrate that the proposed FFRA-Net achieves excellent results on four datasets: FM_RAF-DB, SG_RAF-DB, RAF-DB, and FERPLUS, with accuracies of 77.87%, 79.50%, 88.66%, and 88.97%, respectively. Thus, the approach presented in this paper demonstrates strong applicability in the context of occluded facial expression recognition (FER).

Unveiling the role of regulatory T cells in the tumor microenvironment of pancreatic cancer through single-cell transcriptomics and in vitro experiments.

Background: In order to investigate the impact of Treg cell infiltration on the immune response against pancreatic cancer within the tumor microenvironment (TME), and identify crucial mRNA markers associated with Treg cells in pancreatic cancer, our study aims to delve into the role of Treg cells in the anti-tumor immune response of pancreatic cancer. Methods: The ordinary transcriptome data for this study was sourced from the GEO and TCGA databases. It was analyzed using single-cell sequencing analysis and machine learning. To assess the infiltration level of Treg cells in pancreatic cancer tissues, we employed the CIBERSORT method. The identification of genes most closely associated with Treg cells was accomplished through the implementation of weighted gene co-expression network analysis (WGCNA). Our analysis of single-cell sequencing data involved various quality control methods, followed by annotation and advanced analyses such as cell trajectory analysis and cell communication analysis to elucidate the role of Treg cells within the pancreatic cancer microenvironment. Additionally, we categorized the Treg cells into two subsets: Treg1 associated with favorable prognosis, and Treg2 associated with poor prognosis, based on the enrichment scores of the key genes. Employing the hdWGCNA method, we analyzed these two subsets to identify the critical signaling pathways governing their mutual transformation. Finally, we conducted PCR and immunofluorescence staining in vitro to validate the identified key genes. Results: Based on the results of immune infiltration analysis, we observed significant infiltration of Treg cells in the pancreatic cancer microenvironment. Subsequently, utilizing the WGCNA and machine learning algorithms, we ultimately identified four Treg cell-related genes (TRGs), among which four genes exhibited significant correlations with the occurrence and progression of pancreatic cancer. Among them, CASP4, TOB1, and CLEC2B were associated with poorer prognosis in pancreatic cancer patients, while FYN showed a correlation with better prognosis. Notably, significant differences were found in the HIF-1 signaling pathway between Treg1 and Treg2 cells identified by the four genes. These conclusions were further validated through in vitro experiments. Conclusion: Treg cells played a crucial role in the pancreatic cancer microenvironment, and their presence held a dual significance. Recognizing this characteristic was vital for understanding the limitations of Treg cell-targeted therapies. CASP4, FYN, TOB1, and CLEC2B exhibited close associations with infiltrating Treg cells in pancreatic cancer, suggesting their involvement in Treg cell functions. Further investigation was warranted to uncover the mechanisms underlying these associations. Notably, the HIF-1 signaling pathway emerged as a significant pathway contributing to the duality of Treg cells. Targeting this pathway could potentially revolutionize the existing treatment approaches for pancreatic cancer.

Clinical characteristic-assisted surgical benefit stratification for resection of primary tumor in patients with advanced primary malignant bone neoplasms: a population-based propensity score-matched analysis.

Background: Primary tumor resection (PTR) is the standard treatment for patients with primary malignant bone neoplasms (PMBNs). However, it remains unclear whether patients with advanced PMBNs still benefit from PTR. This study aimed to develop a prediction model to estimate the beneficial probability of PTR for this population. Methods: This study extracted data from patients diagnosed with advanced PMBNs, as recorded in the Surveillance, Epidemiology, and End Results (SEER) database, with the period from 2004 to 2015. The patient cohort was then bifurcated into two groups: those who underwent surgical procedures and the non-surgery group. Propensity score matching (PSM) was utilized to mitigate any confounding factors in the study. The survival rates of patients from both the surgical and non-surgery groups were evaluated using Kaplan-Meier (K-M) curves analysis. Moreover, the study used this method to assess the capacity of the nomogram to distinguish patients likely to derive benefits from surgical intervention. The study was grounded in the hypothesis that patients who underwent PTR and survived beyond the median overall survival (OS) time would potentially benefit from the surgery. Subsequently, logistic regression analysis was performed to ascertain significant predictors, facilitating the development of a nomogram. This nomogram was subjected to both internal and external validation using receiver operating characteristic curves, area under the curve analysis, calibration plots, and decision curve analysis. Results: The SEER database provided a total of 839 eligible patients for the study, among which 536 (63.9%) underwent PTR. Following a 2:1 PSM analysis, patients were classified into two groups: 364 patients in the surgery group and 182 patients in the non-surgery group. Both K-M curves and multivariate Cox regression analysis revealed that patients who received PTR had a longer survival duration, observed both before and after PSM. Crucial factors such as age, M stage, and tumor size were identified to be significantly correlated with surgical benefits in patients with advanced PMBNs. Subsequently, a nomogram was developed that uses these independent predictors. The validation of this predictive model confirmed its high accuracy and excellent discrimination ability of the nomogram to distinguish patients who would most likely benefit from surgical intervention. Conclusion: In this study, we devised a user-friendly nomogram to forecast the likehood of surgical benefits for patients diagnosed with advanced PMBNs. This tool facilitates the identification of the most suitable candidates for PTR, thus promoting more discerning and effective use of surgical intervention in this patient population.

GADD45B regulates the carcinogenesis process of chronic atrophic gastritis and the metabolic pathways of gastric cancer.

Background: Gastric cancer continues to be a significant global healthcare challenge, and its burden remains substantial. The development of gastric cancer (GC) is closely linked to chronic atrophic gastritis (CAG), yet there is a scarcity of research exploring the underlying mechanisms of CAG-induced carcinogenesis. Methods: In this study, we conducted a comprehensive investigation into the oncogenes involved in CAG using both bulk transcriptome and single-cell transcriptome data. Our approach employed hdWGCNA to identify pathogenic genes specific to CAG, with non-atrophic gastritis (NAG) serving as the control group. Additionally, we compared CAG with GC, using normal gastric tissue as the control group in the single-cell transcriptome analysis. By intersecting the identified pathogenic genes, we pinpointed key network molecules through protein interaction network analysis. To further refine the gene selection, we applied LASSO, SVM-RFE, and RF techniques, which resulted in a set of cancer-related genes (CRGs) associated with CAG. To identify CRGs potentially linked to gastric cancer progression, we performed a univariate COX regression analysis on the gene set. Subsequently, we explored the relationship between CRGs and immune infiltration, drug sensitivity, and clinical characteristics in gastric cancer patients. We employed GSVA to investigate how CRGs regulated signaling pathways in gastric cancer cells, while an analysis of cell communication shed light on the impact of CRGs on signal transmission within the gastric cancer tumor microenvironment. Lastly, we analyzed changes in metabolic pathways throughout the progression of gastric cancer. Results: Using hdWGCNA, we have identified a total of 143 pathogenic genes that were shared by CAG and GC. To further investigate the underlying mechanisms, we conducted protein interaction network analysis and employed machine learning screening techniques. As a result, we have identified 15 oncogenes that are specifically associated with chronic atrophic gastritis. By performing ROC reanalysis and prognostic analysis, we have determined that GADD45B is the most significant gene involved in the carcinogenesis of CAG. Immunohistochemical staining and differential analysis have revealed that GADD45B expression was low in GC tissues while high in normal gastric tissues. Moreover, based on prognostic analysis, high expression of GADD45B has been correlated with poor prognosis in GC patients. Additionally, an analysis of immune infiltration has shown a relationship between GADD45B and the infiltration of various immune cells. By correlating GADD45B with clinical characteristics, we have found that it primarily affects the depth of invasion in GC. Through cell communication analysis, we have discovered that the CD99 signaling pathway network and the CDH signaling pathway network are the main communication pathways that significantly alter the microenvironment of gastric tissue during the development of chronic atrophic gastritis. Specifically, GADD45B-low GC cells were predominantly involved in the network communication of the CDH signaling pathway, while GADD45B-high GC cells played a crucial role in both signaling pathways. Furthermore, we have identified several metabolic pathways, including D-Glutamine and D-glutamate metabolism and N-Glycan biosynthesis, among others, that played important roles in the occurrence and progression of GC, in addition to the six other metabolic pathways. In summary, our study highlighted the discovery of 143 pathogenic genes shared by CAG and GC, with a specific focus on 15 oncogenes associated with CAG. We have identified GADD45B as the most important gene in the carcinogenesis of CAG, which exhibited differential expression in GC tissues compared to normal gastric tissues. Moreover, GADD45B expression was correlated with patient prognosis and is associated with immune cell infiltration. Our findings also emphasized the impact of the CD99 and CDH signaling pathway networks on the microenvironment of gastric tissue during the development of CAG. Additionally, we have identified key metabolic pathways involved in GC progression. Conclusion: GADD45B, an oncogene implicated in chronic atrophic gastritis, played a critical role in GC development. Decreased expression of GADD45B was associated with the onset of GC. Moreover, GADD45B expression levels were closely tied to poor prognosis in GC patients, influencing the infiltration patterns of various cells within the tumor microenvironment, as well as impacting the metabolic pathways involved in GC progression.

Prevalence and correlates of malnutrition risk among Chinese centenarians and oldest-old adults.

This study was to explore epidemiological characteristics of malnutrition and factors associated with malnutrition in centenarians and oldest-old adults, so as to provide a reference for family members and government departments to take effective measures and promote healthy aging. Median age of all 1,654 participants was 100 (85, 102) years old, and prevalence of high malnutrition risk was 65.54% in all participants. Proportion of high-malnutrition risk was higher, and proportion of normal physical function was lower, in centenarians than those in oldest-old adults (p < 0.05 for all). Univariate and multivariate Poisson regression analyses showed that normal physical function was negatively associated with malnutrition risk in all participants, centenarians, and oldest-old adults (p < 0.05 for all). In conclusion, proportion of centenarians at malnutrition risk was significantly higher than that of oldest-old adults, and the independent factor associated with malnutrition in people aged over 80 years was physical function.

Web-based nomogram and risk stratification system constructed for predicting the overall survival of older adults with primary kidney cancer after surgical resection.

BACKGROUND: Kidney cancer (KC) is one of the most common malignant tumors in adults which particularly affects the survival of elderly patients. We aimed to construct a nomogram to predict overall survival (OS) in elderly KC patients after surgery. METHODS: Information on all primary KC patients aged more than 65 years and treated with surgery between 2010 and 2015 was downloaded from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression analysis was used to identify the independent prognostic factors. Consistency index (C-index), receiver operating characteristic curve (ROC), the area under curve (AUC), and calibration curve were used to assess the accuracy and validity of the nomogram. Comparison of the clinical benefits of nomogram and the TNM staging system is done by decision curve analysis (DCA) and time-dependent ROC. RESULTS: A total of 15,989 elderly KC patients undergoing surgery were included. All patients were randomly divided into training set (N = 11,193, 70%) and validation set (N = 4796, 30%). The nomogram produced C-indexes of 0.771 (95% CI 0.751-0.791) and 0.792 (95% CI 0.763-0.821) in the training and validation sets, respectively, indicating that the nomogram has excellent predictive accuracy. The ROC, AUC, and calibration curves also showed the same excellent results. In addition, DCA and time-dependent ROC showed that the nomogram outperformed the TNM staging system with better net clinical benefits and predictive efficacy. CONCLUSIONS: Independent influencing factors for postoperative OS in elderly KC patients were sex, age, histological type, tumor size, grade, surgery, marriage, radiotherapy, and T-, N-, and M-stage. The web-based nomogram and risk stratification system could assist surgeons and patients in clinical decision-making.

The novel dynamic nomogram and risk classification system constructed for predicting post-surgical overall survival and mortality risk in primary chondrosarcoma: a population study based on SEER database.

BACKGROUND: Surgery is the predominant method to improve the prognosis of primary chondrosarcoma patients. We aimed to construct the first reliable nomogram to predict the post-surgical overall survival (OS) of primary chondrosarcoma patients. METHODS: We downloaded all primary chondrosarcoma patients treated with surgery between 2004 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database, and randomized them into training set (60%) and validation set (40%). Cox proportional regression analysis was applied to the training set to identify independent prognostic variables, and then constructed a nomogram for predicting 3-, 5-, and 8-year OS. The Harrell's concordance index (C-index), receiver operating characteristic curve (ROC), the area under curve (AUC), calibration curve and decision curve analysis (DCA) was used to assess the predictive efficacy and clinical applicability of the nomogram. The nomogram was also compared with The American Joint Committee on Cancer (AJCC) staging system. RESULTS: A total of 1005 post-surgical primary chondrosarcoma patients were included in this study. We finally identified five independent prognostic variables to construct the nomogram, being age, grade, tumor size, disease stage and histological type. The C-index results showed that the prediction performance of the nomogram was significantly better than the AJCC staging system. In the training set, (C-index: 0.805, 95% CI 0.879-0.730 vs 0.686, 95% CI 0.606-0.766); in the validation set, (C-index: 0.811, 95% CI 0.895-0.727 vs 0.697, 95% CI 0.647-0.799). Additionally, the AUC values generated by the ROC were all greater than 0.8, which also indicated the excellent predictive performance of the nomogram. The calibration curves showed that the predicted survival rate was highly similar to the actual. Time-dependent ROC and DCA showed that the nomogram has better predictive performance and net clinical benefits than the AJCC staging system. Finally, a risk stratification system based on nomogram was constructed. CONCLUSION: We successfully constructed and validated the first nomogram that could reliably predict 3-, 5-, and 8-year post-surgical OS in primary chondrosarcoma patients. Furthermore, the web-based dynamic nomogram could be more conveniently applied to clinic, providing assistance to surgeons and patients.

Two novel clinical tools to predict the risk of bone metastasis and overall survival in esophageal cancer patients: a large population-based retrospective cohort study.

BACKGROUND: The aim of this study was to construct two web-based nomograms to predict the probability of bone metastasis (BM) in esophageal cancer (EC) patients and the prognostic of EC patients with BM (ECBM). METHODS: We collected the data of EC and ECBM patients in the Surveillance, Epidemiology and End Results (SEER) database from 2010 to 2015. Independent risk variables for the development of BM in EC patients were identified using univariate and multivariate logistic regression analyses. Univariate and multivariate Cox regression analyses were used to assess independent prognostic variables in ECBM patients. And then, constructed two nomograms to predict the risk of bone metastases and overall survival (OS) of ECBM patients. Survival differences were studied by Kaplan-Meier (K-M) survival analysis. The predictive efficacy and clinical applicability of these two nomograms were assessed by using receiver operating characteristic (ROC) curve, the area under curve (AUC), calibration curve and decision curve analysis (DCA). RESULTS: We selected a total of 6839 patients with EC, of which 326 (4.77%) had BM at the time of initial diagnosis. The results of K-M survival and Cox regression analysis showed significant effects of BM on the OS in EC patients. Age, N stage, tumor size and brain/liver/lung organ metastasis were identified as BM-related risk variables. Chemotherapy and brain/liver organ metastasis were identified as ECBM-related prognostic variables. The ROC, AUC, calibration curves and DCA of two nomograms all showed excellent predictive efficacy and clinical applicability. CONCLUSIONS: These two nomograms were constructed and validated, which could objectively predict the risk of BM in EC patients and the prognostic in ECBM patients. These tools are expected to make valuable contributions in clinical work, informing surgeons in making decisions about patient care.

Construction of novel predictive tools for post-surgical cancer-specific survival probability in patients with primary chondrosarcoma and external validation in Chinese cohorts: a large population-based retrospective study.

BACKGROUND: Surgery is the predominant treatment modality for chondrosarcoma. This study aims to construct a novel clinic predictive tool that accurately predicts the 3-, 5-, and 8-year probability of cancer-specific survival (CSS) for primary chondrosarcoma patients who have undergone surgical treatment. METHODS: The Surveillance, Epidemiology, and End Results (SEER) database was used to identify 982 primary chondrosarcoma patients after surgery, who were randomly divided into two sets: training set (60%) and internal validation set (40%). Cox proportional regression analyses were used to screen post-surgical independent prognostic variables in primary chondrosarcoma patients. These identified variables were used to construct a nomogram to predict the probability of post-surgical CSS of primary chondrosarcoma patients. The k-fold cross-validation method (k = 10), Harrell's concordance index (C-index), receiver operating characteristic curve (ROC) and area under curve (AUC) were used to assess the predictive accuracy of the nomogram. Calibration curve and decision curve analysis (DCA) were used to validate the clinical application of the nomogram. RESULTS: Age, tumor size, disease stage and histological type were finally identified post-surgical independent prognostic variables. Based the above variables, a nomogram was constructed to predict the 3-, 5- and 8-year probability of post-surgical CSS in primary chondrosarcoma patients. The results of the C-index showed excellent predictive performance of the nomogram (training set: 0.837, 95% CI: 0.766-0.908; internal validation set: 0.835, 95% CI: 0.733-0.937; external validation set: 0.869, 95% CI: 0.740-0.998). The AUCs of ROC were all greater than 0.830 which again indicated that the nomogram had excellent predictive performance. The results of calibration curve and DCA indicated that the clinical applicability of this nomogram was outstanding. Finally, the risk classification system and online access version of the nomogram was developed. CONCLUSION: We constructed the first nomogram to accurately predict the 3-, 5- and 8-year probability of post-surgical CSS in primary chondrosarcoma patients. This nomogram would assist surgeons to provide individualized post-surgical survival predictions and clinical strategies for primary chondrosarcoma patients.

Development and Validation of Nomograms to Predict the Prognosis of Patients With Unresectable Hepatocellular Carcinoma Receiving Transarterial Chemoembolization.

Background: Recent studies have shown that inflammatory indicators are closely related to the prognosis of patients with hepatocellular carcinoma, and they can serve as powerful indices for predicting recurrence and survival time after treatment. However, the predictive ability of inflammatory indicators has not been systematically studied in patients receiving transarterial chemoembolization (TACE). Therefore, the objective of this research was to determine the predictive value of preoperative inflammatory indicators for unresectable hepatocellular carcinoma treated with TACE. Methods: Our retrospective research involved 381 treatment-naïve patients in 3 institutions, including the First Affiliated Hospital of Soochow University, Nantong First People's Hospital, and Nantong Tumor Hospital, from January 2007 to December 2020 that received TACE as initial treatment. Relevant data of patients were collected from the electronic medical record database, and the recurrence and survival time of patients after treatment were followed up. Least absolute shrinkage and selection operator (LASSO) algorithm was used to compress and screen the variables. We utilized Cox regression to determine the independent factors associated with patient outcomes and constructed a nomogram based on multivariate results. Finally, the nomogram was verified from discriminability, calibration ability, and practical applicability. Results: Multivariate analysis revealed that the levels of aspartate aminotransferase-to-platelet ratio index (APRI) and lymphocyte count were independent influential indicators for overall survival (OS), whereas the levels of platelet-to-lymphocyte ratio (PLR) was an independent influential index for progression. Nomograms exhibited an excellent concordance index (C-index), in the nomogram of OS, the C-index was 0.753 and 0.755 in training and validation cohort, respectively; and in the nomogram of progression, the C-index was 0.781 and 0.700, respectively. The time-dependent C-index, time-dependent receiver operating characteristic (ROC), and time-dependent area under the curve (AUC) of the nomogram all exhibited ideal discrimination ability. Calibration curves significantly coincided with the standard lines, which indicated that the nomogram had high stability and low degree of over-fitting. Decision curve analysis revealed a wider range of threshold probabilities and could augment net benefits. The Kaplan-Meier curves for risk stratification indicated that the prognosis of patients varied significantly between risk categories (P < .0001). Conclusions: The developed prognostic nomograms based on preoperative inflammatory indicators revealed high predictive accuracy for survival and recurrence. It can be a valuable clinical instrument for guiding individualized treatment and predicting prognosis.

Increased methylation of ZNF671 suppresses tumor progression by promoting MAPK6 transcription in laryngeal carcinoma.

Background: Laryngeal squamous cell carcinoma (LSCC) is a malignant tumor of the head and neck, the exact mechanism of which has not been explored. Methods: By analyzing the GEO data, we found the highly methylated and low expression gene ZNF671. The expression level of ZNF671 in clinical samples was verified by RT-PCR, western blotting and methylation-specific PCR. The function of ZNF671 in LSCC was detected by cell culture and transfection, MTT, Edu, TUNEL assays and flow cytometry analysis. The binding sites of ZNF671 to MAPK6 promoter region were detected and verified by luciferase reporter gene and chromatin immunoprecipitation. Finally, the effect of ZNF671 on LSCC tumors was tested in vivo. Results: In this study, by analyzing GEO data GSE178218 and GSE59102, we found that zinc finger protein (ZNF671) expression was decreased, and DNA methylation level was increased in laryngeal cancer. Moreover, the abnormal expression of ZNF671 was associated with poor survival prognosis of patients. In addition, we found that overexpression of ZNF671 could inhibit the viability, proliferation, migration and invasion of LSCC cells, while promoting cell apoptosis. In contrast, the opposite effects were observed after knockdown of ZNF671. Through the prediction website and chromatin immunoprecipitation and luciferase reporter experiments, it was found that ZNF671 could bind to the promoter region of MAPK6, thereby inhibiting the expression of MPAK6. In vivo experiments confirmed that overexpression of ZNF671 could inhibit tumor growth. Conclusion: Our study found that ZNF671 expression was down-regulated in LSCC. ZNF671 up-regulates the expression of MAPK6 by binding to its promoter region, thus participating in cell proliferation, migration and invasion in LSCC. Our study may provide new ideas for early prediction and treatment of LSCC.

Individualized assessment predictive models for risk and overall survival in elderly patients of primary kidney cancer with bone metastases: A large population-based study.

Background: Elderly people are at high risk of metastatic kidney cancer (KC), and, the bone is one of the most common metastatic sites for metastatic KC. However, studies on diagnostic and prognostic prediction models for bone metastases (BM) in elderly KC patients are still vacant. Therefore, it is necessary to establish new diagnostic and prognostic nomograms. Methods: We downloaded the data of all KC patients aged more than 65 years during 2010-2015 from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate logistic regression analyses were used to study independent risk factors of BM in elderly KC patients. Univariate and multivariate Cox regression analysis for the study of independent prognostic factors in elderly KCBM patients. Survival differences were studied using Kaplan-Meier (K-M) survival analysis. The predictive efficacy and clinical utility of nomograms were assessed by receiver operating characteristic (ROC) curve, the area under curve (AUC), calibration curve, and decision curve analysis (DCA). Results: A final total of 17,404 elderly KC patients (training set: n = 12,184, validation set: n = 5,220) were included to study the risk of BM. 394 elderly KCBM patients (training set: n = 278, validation set: n = 116) were included to study the overall survival (OS). Age, histological type, tumor size, grade, T/N stage and brain/liver/lung metastasis were identified as independent risk factors for developing BM in elderly KC patients. Surgery, lung/liver metastasis and T stage were identified as independent prognostic factors in elderly KCBM patients. The diagnostic nomogram had AUCs of 0.859 and 0.850 in the training and validation sets, respectively. The AUCs of the prognostic nomogram in predicting OS at 12, 24 and 36 months were: training set (0.742, 0.775, 0.787), and validation set (0.721, 0.827, 0.799), respectively. The calibration curve and DCA also showed excellent clinical utility of the two nomograms. Conclusion: Two new nomograms were constructed and validated to predict the risk of developing BM in elderly KC patients and 12-, 24-, and 36-months OS in elderly KCBM patients. These models can help surgeons provide more comprehensive and personalized clinical management programs for this population.