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Integrins play a crucial role in the regulation process of cell proliferation, migration, differentiation, tumor invasion and metastasis. , and are three encoding genes of integrins family. Accumulative evidences have proved that abnormal expression of , and are a common phenomenon in different malignances. However, their expression patterns and prognostic roles for patients with non-small cell lung cancer (NSCLC) have not been completely illustrated. We investigated the expression patterns and prognostic values of , and in patients with NSCLC through using a series of databases and various datasets, including ONCOMINE, GEPIA, HPA, TCGA and GEO datasets. We found that the expression levels of and were significantly upregulated in both LUAD and LUSC, while was obviously upregulated in LUSC. Additionally, higher expression level of revealed a worse OS in LUAD. Our findings suggested that and might have the potential ability to act as diagnostic biomarkers for both LUAD and LUSC, while might serve as diagnostic biomarker for LUSC. Furthermore, could serve as a potential prognostic biomarker for LUAD.

Recent studies suggest that cuproptosis, a novel mode of cell death, may be associated with the development of cancer. However, no studies are showing its role in tumorigenesis, progression, and prognosis. In the present study, we comprehensively analyzed the expression difference, gene variation and methylation modification of cuproptosis-related genes (CRGs) in pan-cancer. Then, Single sample gene set enrichment analysis (ssGSEA) was used to calculate individual cuproptosis scores (CS). The association of CS with copy number variation, clinical features, immune-related genes, TMB, MSI, and tumor immune dysfunction and exclusion (TIDE) was comprehensively assessed. Single-cell transcriptome sequencing (scRNA-seq) to analyze the activation of cuproptosis in the tumor microenvironment. Immunohistochemistry (IHC) were used to validate the expression of cuproptosis hub-gene. Our study shows that CRGs were significantly expressed in a variety of tumors, and CDKN2A had the highest mutation frequency (49%) in all tumors. A significant increase in the CS was observed in most cancers and were associated with poor prognosis in the majority of tumors. CS was significantly negatively correlated with tumor microenvironment scores in more than 10 tumors and positively correlated with PD-L1 in 11 tumors, suggesting involvement in tumor immune escape. scRNA-seq suggests that CRG scores significantly increased in the cancer cells. This study opens avenues for further research on the role of cuproptosis in the occurrence and development of cancer and the development of targeted therapies based on cuproptosis.

Multiple myeloma (MM) is a common hematological malignancy, and its prognostic factors have been extensively studied. Progression of disease within 24 months (POD24) suggests a poor prognosis in many malignancies, but is rarely mentioned in MM. This study aimed to investigate the prognostic value of POD24 in MM and risk factors of POD24, and to evaluate the predictive value of existing MM prognostic models for POD24. The research retrospectively analyzed the clinical data of MM patients and found that the occurrence of POD24 is an independent prognostic factor affecting overall survival in MM, while non-transplantion and genetic abnormality are independent risk factors for the occurrence of POD24. The existing prognostic models are not effective in predicting POD24. Therefore, it’s still necessary to explore a prognostic model that can predict POD24 more accurately.

Acute flaccid myelitis (AFM) is an acute flaccid paralysis syndrome with spinal motor neuron involvement of unknown etiology. We investigated the characteristics and prognostic factors of AFM clusters coincident with an enterovirus D68 (EV-D68) outbreak in Japan during autumn 2015. An AFM case series study was conducted following a nationwide survey from August to December 2015. Radiographic and neurophysiologic data were subjected to centralized review, and virology studies were conducted for available specimens. Fifty-nine AFM cases (58 definite, 1 probable) were identified, including 55 children and 4 adults (median age, 4.4 years). The AFM epidemic curve showed strong temporal correlation with EV-D68 detection from pathogen surveillance, but not with other pathogens. EV-D68 was detected in 9 patients: 5 in nasopharyngeal, 2 in stool, 1 in cerebrospinal fluid (adult case), and 1 in tracheal aspiration, nasopharyngeal, and serum samples (a pediatric case with preceding steroid usage). Cases exhibited heterogeneous paralysis patterns from 1- to 4-limb involvement, but all definite cases had longitudinal spinal gray matter lesions on magnetic resonance imaging (median, 20 spinal segments). Cerebrospinal fluid pleocytosis was observed in 50 of 59 cases (85%), and 8 of 29 (28%) were positive for antiganglioside antibodies, as frequently observed in Guillain-Barré syndrome. Fifty-two patients showed variable residual weakness at follow-up. Good prognostic factors included a pretreatment manual muscle strength test unit score >3, normal F-wave persistence, and EV-D68-negative status. EV-D68 may be one of the causative agents for AFM, while host susceptibility factors such as immune response could contribute to AFM development.

Clear Cell Renal Cell Carcinoma (ccRCC) is the most common type of kidney cancer, characterized by high heterogeneity and complexity. Recent studies have identified mitochondrial defects and autophagy as key players in the development of ccRCC. This study aims to delve into the changes in mitophagic activity within ccRCC and its impact on the tumor microenvironment, revealing its role in tumor cell metabolism, development, and survival strategies. Comprehensive analysis of ccRCC tumor tissues using single cell sequencing and spatial transcriptomics to reveal the role of mitophagy in ccRCC. Mitophagy was determined to be altered among renal clear cells by gene set scoring. Key mitophagy cell populations and key prognostic genes were identified using NMF analysis and survival analysis approaches. The role of UBB in ccRCC was also demonstrated by experiments. Compared to normal kidney tissue, various cell types within ccRCC tumor tissues exhibited significantly increased levels of mitophagy, especially renal clear cells. Key genes associated with increased mitophagy levels, such as UBC, UBA52, TOMM7, UBB, MAP1LC3B, and CSNK2B, were identified, with their high expression closely linked to poor patient prognosis. Particularly, the ubiquitination process involving the UBB gene was found to be crucial for mitophagy and its quality control. This study highlights the central role of mitophagy and its regulatory factors in the development of ccRCC, revealing the significance of the UBB gene and its associated ubiquitination process in disease progression.

Endometrial cancer (EC) is a common gynecologic malignancy, with a rising trend in related mortality rates. The assessment based on imaging examinations contributes to the preoperative staging and surgical management of EC. However, conventional imaging diagnosis has limitations such as low accuracy and subjectivity. Radiomics, utilizing advanced feature analysis from medical images, extracts more information, ultimately establishing associations between imaging features and disease phenotypes. In recent years, radiomic studies on EC have emerged, employing radiomic features combined with clinical characteristics to model and predict histopathological features, protein expression, and clinical prognosis. This article elaborates on the application of radiomics in EC research and discusses its implications.

Gene prognostic meta-analyses should benefit from breast tumour genomic data obtained during the last decade. The aim was to develop a user-friendly, web-based application, based on DNA microarrays results, called “breast cancer Gene-Expression Miner” (bc-GenExMiner) to improve gene prognostic analysis performance by using the same bioinformatics process. bc-GenExMiner was developed as a web-based tool including a MySQL relational database. Survival analyses are performed with R statistical software and packages. Molecular subtyping was performed by means of three single sample predictors (SSPs) and three subtype clustering models (SCMs). Twenty-one public data sets have been included. Among the 3,414 recovered breast cancer patients, 1,209 experienced a pejorative event. Molecular subtyping by means of three SSPs and three SCMs was performed for 3,063 patients. Furthermore, three robust lists of stable subtyped patients were built to maximize reliability of molecular assignment. Gene prognostic analyses are done by means of univariate Cox proportional hazards model and may be conducted on cohorts split by nodal (N), oestrogen receptor (ER), or molecular subtype status. To evaluate independent prognostic impact of genes relative to Nottingham Prognostic Index and Adjuvant! Online, adjusted Cox proportional hazards models are performed. bc-GenExMiner allows researchers without specific computation skills to easily and quickly evaluate the in vivo prognostic role of genes in breast cancer by means of Cox proportional hazards model on large pooled cohorts, which may be split according to different prognostic parameters: N, ER, and molecular subtype. Prognostic analyses by molecular subtype may also be performed in three robust molecular subtype classifications.

This study aims to develop a risk model for the prognostic prediction for colorectal cancer (CRC) patients according to the phenotype related to damage-associated molecular patterns (DAMPs). The data were sourced from the Cancer Genome Atlas (TCGA) and cBioportal databases. The DAMP score was calculated based on the TCGA cohort data using the “ssGSEA” method. Differentially expressed genes (DEGs) identified by the “limma” package were compressed by performing Lasso Cox regression analysis using the “glmnet” package. Subsequently, biomarkers obtained were used to construct a risk model and a nomogram. The CRC subjects were divided by the median RiskScore into low- and high-risk groups. Kaplan-Meier (KM) survival analysis was conducted, and the “timeROC” package was used for model validation. The “estimate” package, “MCP-COUNTER”, “ssGSEA” and “TIDE” were employed to perform immune infiltration analyses. Drug sensitivity analysis and pathway analysis were conducted using the “pRRophetic” package and “ssGSEA”, respectively. According to the results, cancer-adjacent samples showed higher DAMP score and immune cell infiltration, lower tumor purity, and a better prognosis. Nine biomarkers ( PAH , SIGLEC14 , MMP1 , JAKMIP1 , FCGR3B , KCNT1 , SLC2A3 , SLC11A1 , and HOXC4) were determined to build a reliable risk model, which showed a relatively high AUC value. Notably, patients classified by the model into the high-risk group had a worse prognostic outcome. Furthermore, a nomogram was constructed, and both the nomogram and RiskScore demonstrated a strong predictive power. The results of immune infiltration and drug sensitivity analysis showed higher immune infiltration and greater immunotherapy benefit in the low-risk group. Also, the low-risk group was enriched in immune-related pathways. We developed a reliable DAMP signature for CRC, contributing to the diagnosis and treatment of CRC.

Objective In recent years, an increasing number of studies have revealed that patients’ preoperative inflammatory response, coagulation function, and nutritional status are all linked to the occurrence, development, angiogenesis, and metastasis of various malignant tumors. The goal of this study is to determine the relationship between preoperative peripheral blood neutrophil to lymphocyte ratio (NLR), monocyte to lymphocyte ratio (MLR), systemic immune-inflammatory index (SII), platelet to lymphocyte ratio (PLR), and platelet to fibrinogen ratio (FPR). Prognostic nutritional index (PNI) and the prognosis of glioblastoma multiforme (GBM) patients, as well as establish a forest prediction model that includes preoperative hematological markers to predict the individual GBM patient’s 3-year survival status after treatment. Methods The clinical and hematological data of 281 GBM patients were analyzed retrospectively; overall survival (OS) was the primary endpoint. X-Tile software was used to determine the best cut-off values for NLR, SII, and PLR, and the survival analysis was carried out by the Kaplan–Meier method as well as univariate and multivariate COX regression. Afterward, we created a random forest model that predicts the individual GBM patient’s 3-year survival status after treatment, and the area under the curve (AUC) is used to validate the model’s effectiveness. Results The best cut-off values for NLR, SII, and PLR in GBM patients’ preoperative peripheral blood were 2.12, 537.50, and 93.5 respectively. The Kaplan–Meier method revealed that preoperative GBM patients with high SII, high NLR, and high PLR had shorter overall survival, and the difference was statistically significant. In addition to clinical and pathological factors. Univariate Cox showed NLR (HR = 1.456, 95% CI: 1.286 ~ 1.649, P  < 0.001) MLR (HR = 1.272, 95% CI: 1.120 ~ 1.649, P  < 0.001), FPR (HR = 1.183,95% CI: 1.049 ~ 1.333, P  < 0.001), SII (HR = 0.218,95% CI: 1.645 ~ 2.127, P  < 0.001) is related to the prognosis and overall survival of GBM. Multivariate Cox proportional hazard regression showed that SII (HR = 1.641, 95% CI: 1.430 ~ 1.884, P  < 0.001) is also related to the overall survival of patients with GBM. In the random forest prognostic model with preoperative hematologic markers, the AUC in the test set and the validation set was 0.907 and 0.900, respectively. Conclusion High levels of NLR, MLR, PLR, FPR, and SII before surgery are prognostic risk factors for GBM patients. A high preoperative SII level is an independent risk factor for GBM prognosis. The random forest model that includes preoperative hematological markers has the potential to predict the individual GBM patient’s 3-year survival status after treatment,and assist the clinicians for making a good clinical decision.

This study presents a forensic diagnostic framework aimed at enhancing the early detection, fault classification and remaining useful life (RUL) prediction of rolling bearing failures. The proposed network integrates a novel three-stage machine learning formulation – (1) identification of health state using voting ensemble, (2) prognostic analysis via a hybrid convolutional neural network and gated recurrent unit (CNN-GRU), and (3) fault type identification through the segment anything model (SAM) based on time-frequency representations. The ensemble and CNN-GRU models are trained on both time- and frequency-domain features from vibration signals, while SAM leverages this data in visual sense through iterative masking for zero-shot spatial-temporal fault segmentation. Pre-processing techniques, including piecewise aggregate approximation and singular spectrum analysis, are used to denoise and compress the vibration response without impacting key statistical traits. The proposed e-CNN-GRU-SAM network demonstrates better accuracy in diagnosing fault types, predicting RUL and identifying root causes under different operational conditions. This is established using diverse operating benchmark datasets that simulate induced and real-world degradation scenarios for generalization. Thus, the proposed framework offers a comprehensive forensic analysis toolkit for diagnosis and prognosis of bearings.