Significant Difference In Prognosis Research Articles

Exploring the biological behavior differences between retroperitoneal and non-retroperitoneal liposarcomas

BackgroundLiposarcoma is a malignant tumor that originates from adipose tissue and can occur in any part of the body. There is currently no clear conclusion on whether there are significant differences in prognosis between liposarcoma at different anatomical locations, especially retroperitoneal liposarcoma (RLPS) and non retroperitoneal liposarcoma (NRLPS). The aim of this study is to reveal whether there are differences in prognosis between these two locations of liposarcoma, and further explore the fundamental reasons behind these differences. MethodsWe conducted an in-depth investigation into the factors affecting the prognosis of patients with liposarcoma by analyzing the data from the Surveillance, Epidemiology, and End Results Program (SEER) database. Then, we used propensity score matching (PSM) to balance these prognostic factors for comparative analysis of survival between RLPS and NRLPS. In addition, by analyzing transcriptome and whole exome data from TCGA and the Japan Genotypic Phenotype Archive (JGA), we identified genes with significant expression differences and explored changes in the immune microenvironment. ResultThrough analysis of RLPS and NRLPS patients in the SEER database, we observed significant prognostic differences between the two groups, with RLPS exhibiting worse prognosis (p < 0.001). Even after adjusting for confounding factors through PSM, these survival rate differences remained significant, with RLPS still showing worse prognosis (p = 0.017). Furthermore, our analysis of transcriptomic data led to the identification of 467 differentially expressed genes. Additionally, we noted significant differences in the immune microenvironment and whole exome sequencing data between the two groups. ConclusionThere are significant differences between patients with RLPS and NRLPS. Therefore, from clinical research to treatment strategies, RLPS and NRLPS should be considered as two distinct types of tumors, necessitating differentiated approaches for their study and treatment.

Investigating distinct clinical features and constructing a nomogram model for survival probability in adults with cerebellar high-grade gliomas

BackgroundThe clinical features of cerebellar high-grade gliomas (cHGGs) in adults have not been thoroughly explored. This large-scale, population-based study aimed to comprehensively outline these traits and construct a predictive model.MethodsPatient records diagnosed with gliomas were collected from various cohorts and analyzed to compare the features of cHGGs and supratentorial HGGs (sHGGs). Cox regression analyses were employed to identify prognostic factors for overall survival and to develop a nomogram for predicting survival probabilities in patients with cHGGs. Multiple machine learning methods were applied to evaluate the efficacy of the predictive model.ResultsThere were significant differences in prognosis, with SEER-cHGGs showing a median survival of 7.5 months and sHGGs 14.9 months (p < 0.001). Multivariate Cox regression analyses revealed that race, WHO grade, surgical procedures, radiotherapy, and chemotherapy were independent prognostic factors for cHGGs. Based on these factors, a nomogram was developed to predict 1-, 3-, and 5-year survival probabilities, with AUC of 0.860, 0.837, and 0.810, respectively. The model’s accuracy was validated by machine learning approaches, demonstrating consistent predictive effectiveness.ConclusionsAdult cHGGs are distinguished by distinctive clinical features different from those of sHGGs and are associated with an inferior prognosis. Based on these risk factors affecting cHGGs prognosis, the nomogram prediction model serves as a crucial tool for clinical decision-making in patient care.

The Effectiveness of High versus Low Dose Corticosteroid therapy in Pediatric Patients with Acute Respiratory Distress Syndrome

Abstract Background When the lungs fill with fluid as a result of an infection or injury, acute respiratory distress syndrome ensues. Inflamed and fluid-filled lungs occur. They become rigid as a result, making it difficult for them to breathe fully. Blood oxygen levels will fall, endangering the health of other organs. Patients will require a ventilator to help them breathe while their lungs mend. The aim of this study was: to evaluate safety and efficacy of high dose therapy in acute respiratory distress syndrome pediatric patients and to compare safety and efficacy of high dose versus low dose therapy in Acute respiratory distress syndrome patients. Patients and Methods A Prospective open label randomized trial included 15 patients as group1 received high dose corticosteroid therapy for 3 consequent days, high dose Methyl prednisone 30 mg/kg/day and 15 patients as group 2 received low dose corticosteroid therapy for 3 consequent days, low dose Methyl prednisone 2 mg/kg/day. Clinical and laboratory data chest ultrasound and bronchoalveolar lavage to measured pre collagen peptide were studied in each group before and after corticosteroid therapy. Results In the present study 15(50%) patients received high dose steroid and 15 (50%) patients received low dose steroid, male 23 (70%), female 7 (30%), age 7 (3.0-30.0), weight (9.55 ± 6.63), there was no significant difference in prognosis and total days in pediatric intensive care unit between high and low dose corticosteroid therapy as prognosis improving 17 patients (55%), death 13(45%) and As regards total days in pediatric intensive care unit median in high dose corticosteroid therapy 24.0 (15.50-33.0) and median in low dose corticosteroid therapy 18.50 (11.0-23.50) less days in pediatric intensive care unit in low dose steroid therapy group than high dose steroid therapy group but no significant difference between two groups as regarding decrease in chest ultrasound (BLUE Score) and increase in pre collagen peptide in bronchoalveolar lavage between study groups . Conclusion there was no significant difference in prognosis and total days in pediatric intensive care unit between high and low dose corticosteroid therapy as regarding change in chest ultrasound (BLUE Score) and change in pre collagen peptide in bronchoalveolar lavage between study groups.

Identification and characterization of RAC1-related immune and prognostic subtypes of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a malignant tumor with significant variability in prognosis among patients. Ras-related C3 botulinum toxin substrate 1 (RAC1) is a key focus in the area of cancer research. However, the molecular mechanisms of RAC1 in HCC remain incompletely elucidated. In this study, bioinformatics analysis was used, and public databases were used to obtain information about HCC cases. The samples were categorized into two groups of high and low expression based on the expression level of RAC1 gene. The limma package was used to calculate the differentially expressed genes between the two groups, and univariate Cox regression analysis was used to screen the prognostic related factors. Consensus clustering analysis was performed using the ConsensusClusterPlus package to identify molecular subtypes of HCC patients. Immune cell infiltration and ESTIMATE scores were assessed using the single sample gene set enrichment analysis and ESTIMATE algorithms. The sensitivity of different isoforms to chemotherapeutic agents was predicted by the oncoPredict package. Finally, we also performed cell function experiments to validate the biological role of RAC1 in vitro. Initially, we classified patients into high and low expression groups based on RAC1 gene expression levels and identified 195 up-regulated genes and 107 down-regulated genes. Through univariate Cox regression analysis, we screened out 169 prognosis-related factors. Furthermore, HCC patients were categorized into two subtypes. Subsequently, Kaplan-Meier survival curves showed that there was a significant difference in prognosis between the two molecular subtypes. Further analysis indicated substantial differences in gene expression levels and TIDE scores between two molecular subtypes. Moreover, these two subtypes exhibited varying sensitivity to chemotherapy drugs, as evidenced by differences in IC50 values. In addition, we found that the silence of RAC1 could effectively inhibit the migration and invasion of HCC cells in vitro. This study sheds light on the molecular intricacies of RAC1 in HCC and identifies patient populations that may benefit from immunotherapeutic interventions, with potential implications for tailored treatment strategies.

Comprehensive analysis of KLF family reveals KLF6 as a promising prognostic and immune biomarker in pancreatic ductal adenocarcinoma

BackgroundPancreatic ductal adenocarcinoma (PDAC) is one of the deadliest tumors worldwide, with extremely aggressive and complicated biology. Krüppel-like factors (KLFs) encode a series of transcriptional regulatory proteins and play crucial roles in a variety of processes, including tumor cell differentiation and proliferation. However, the potential biological functions and possible pathways of KLFs in the progression of PDAC remain elusive.MethodsWe systematically evaluated the transcriptional variations and expression patterns of KLFs in pancreatic cancer from the UCSC Xena. Based on difference analysis, the non-negative matrix factorization (NMF) algorithm was utilized to identify the immune characteristics and clinical significance of two different subtypes. The multivariate Cox regression was used to construct the risk model and then explore the differences in tumor immune microenvironment (TIME) and drug sensitivity between high and low groups. Through single-cell RNA sequencing (scRNA-seq) analysis, we screened KLF6 and further investigated its biological functions in pancreatic cancer and pan-cancer.ResultsThe KLFs exhibited differential expression and mutations in the transcriptomic profile of PDAC. According to the expression of KLFs, patients were classified into two distinct subtypes, each exhibiting significant differences in prognosis and TIME. Moreover, the KLF signature was developed using univariate Cox and Lasso regression, which proved to be a reliable and effective prognostic model. Furthermore, the KLF_Score was closely associated with immune infiltration, response to immunotherapy, and drug sensitivity and we screened small molecule compounds targeting prognostic genes separately. Through scRNA-seq analysis, KLF6 was selected to further demonstrate its role in the malignance of PC in vitro. Finally, pan-cancer analysis emphasized the biological significance of KLF6 in multiple types of tumors and its clinical utility in assessing cancer prognosis.ConclusionThis study elucidated the pivotal role of KLF family genes in the malignant development of PC through comprehensive analysis and revealed that KLF6 would be a novel diagnostic biomolecule marker and potential therapeutic target for PDAC.

Evaluation of the Efficacy of Transcatheter Intervention, Surgery, and Pharmacological Treatment of Functional Mitral Regurgitation — A Bayesian Network Meta-Analysis with ≥12-Month Follow-up

Aim: Evaluate, using a Bayesian network meta-analysis system, the long-term prognosis of patients with functional mitral regurgitation (FMR) undergoing individual or combined treatment with percutaneous intervention, surgical intervention, or optimal medical therapy. Compare the prognostic outcomes of the different treatment modalities. Methods: Computerized searches of Embase, PubMed, and the Cochrane Library databases were performed. Randomized controlled trials (RCTs) and observational studies were searched to compare prognoses following transcatheter interventions, surgery, and optimal pharmacological treatment for FMR, all with a construction timeframe of 21 October 2023. The primary endpoint event was all-cause mortality. The secondary endpoint events were heart failure readmission rate, mitral regurgitation (MR) ≤2+ improvement rate, New York Heart Association (NYHA) improvement rate (improvement to I–II), and degree of left ventricular ejection fraction (LVEF) improvement. Results: Twenty-six (26) papers were included, comprising 10 RCTs and 16 observational studies involving 5443 patients. A network meta-analysis showed no significant difference in prognosis for all-cause mortality among transcatheter interventions, surgical procedures, and optimal pharmacological treatments. For heart failure readmission rates, mitral valve surgery was superior to MitraClip (odds ratio (OR) = 11.82; 95% confidence interval (CI): 1.67, 90.13). For NYHA (improvement to I–II) improvement rates, the results showed no significant differences for the various mitral interventions. For MR ≤2+ improvement rates, the MitraClip (OR = 3.07; 95% CI: 2.42, 3.76), MitraClip+Guideline-directed medical therapy (GDMT) (OR = 2.93; 95% CI: 2.38, 3.52), mitral valve surgery (OR = 3.01; 95% CI: 2.24, 3.8), and annuloplasty (OR = 4.31; 95% CI: 3.12, 5.58) were superior to GDMT, and mitral valve surgery (OR = 0.07; 95% CI: –0.45, 0.62) was superior to MitraClip+GDMT. For the degree of improvement in LVEF, Carillon+GDMT (mean difference (MD) = –0.97; 95% CI: –1.72, –0.22) was superior to GDMT, mitral valve surgery was superior to Carillon+GDMT (MD = 4.67; 95% CI: 0.92, 8.39); MitraClip+GDMT (MD = 4.01; 95% CI: 1.28, 6.66), GDMT (MD = 3.71; 95% CI: 0.04, 7.35), and annuloplasty were superior to mitral valve surgery (MD = –6.42; 95% CI: –11.96, –0.78). Conclusion: There were no significant differences among the three treatment modalities of transcatheter intervention, surgery, and optimal drug therapy in improving all-cause mortality hard endpoint events, and no significant differences were seen in the rates of heart failure readmission and NYHA improvement (improvement to I–II). However, surgery was superior to transcatheter intervention and optimal drug therapy in terms of improvement in the degree of regurgitation and LVEF.

Combination of advanced lung cancer inflammation index and nonalcoholic fatty liver disease fibrosis score as a promising marker for surgical procedure selection for hepatocellular carcinoma.

Methods of predicting severe postoperative complications after anatomical resection for hepatocellular carcinoma are yet to be established. We aimed to clarify the relationship between inflammation-based prognostic scores and liver fibrosis markers and the incidence of postoperative complications after anatomical resection for hepatocellular carcinoma as well as the usefulness of these markers in surgical procedure selection. We included 374 patients with hepatocellular carcinoma who had undergone initial hepatectomy between January 2007 and December 2021. The association between inflammation-based prognostic scores or liver fibrosis markers and postoperative complications was evaluated, and severe postoperative complication rates in the high-risk group defined by these markers were compared in terms of surgical procedure. The advanced lung cancer inflammation index and nonalcoholic fatty liver disease fibrosis score correlated significantly with severe postoperative complications after anatomical resection, with areas under the curve of 0.67 and 0.61, respectively. The combined advanced lung cancer inflammation index and nonalcoholic fatty liver disease fibrosis score resulted in a larger area under the curve (0.69). Furthermore, in the high-risk group determined by the combined score, the anatomical resection group had a significantly higher incidence of severe complications than the partial resection group (P < 0.01). There were no significant differences in prognosis among the surgical procedures in the high-risk group. The combined advanced lung cancer inflammation index and nonalcoholic fatty liver disease fibrosis score serves as a predictive marker for severe postoperative complications after anatomical resection. This combined marker may contribute to appropriate surgical procedure selection.

Prognostic Factors and Impact of Therapeutic Intervention in Patients With Brain Metastases at the Initial Presentation.

Background Studies investigating the normative characteristics and prognosis of patients diagnosed with brain metastases (BMs) at the onset of cancer are scarce. Therefore, we analyzed real-world treatment options. Methodology This retrospective study enrolled 112 patients newly diagnosed with BM between May 2006 and October 2021. The variables examined included patients' age, sex, recurrence split analysis, Glasgow prognostic score (GPS), number of lesions, tumor size, peripheral brain tumor edema, targeted therapy, supportive care, chemotherapy, and date of onset. Prognostic factors were assessed using recursive partitioning analysis (RPA), graded prognostic assessment (GPA) scores, and GPS scoring, with magnetic resonance imaging (MRI) andcomputed tomography (CT) studies. Primary treatment comprised whole-brain radiotherapy (WBRT), with regular follow-up. Results Data from 112 survivors were analyzed, revealing a median overall survival time (MST) of 7.7 months, with some patients surviving beyond 24 months post-WBRT. Univariate analysis revealed associations between MST and RPA class, GPS, and treatment modalities (including targeted therapy and chemotherapy). RPA class 2, GPSof 0, and targeted therapy were identified as predictors of better prognosis in the multivariate analysis. In the subgroup not receiving chemotherapy, no significant difference in prognosis was seen between groups with or without WBRT. Conclusions Alongside RPA, scores indicating chronic inflammatory changes, including GPS, were confirmed as crucial prognostic factors. Moreover, treatment with molecularly targeted drugs correlated with favorable prognoses. The treatment-naïve group exhibited poorer prognoses, and WBRT was not deemed a significant prognostic factor in the chemotherapy group.

A Novel Neutrophil Extracellular Trap Signature Predicts Patient Chemotherapy Resistance and Prognosis in Lung Adenocarcinoma.

Chemoresistance is a key obstacle in the long-term survival of patients with locally and advanced lung adenocarcinoma (LUAD). This study used bioinformatic analysis to reveal the chemoresistance of gene-neutrophil extracellular traps (NETs) associated with LUAD. RNA sequencing data and LUAD expression patterns were obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, respectively. The GeneCards database was used to identify NETosis-related genes (NRGs). To identify hub genes with significant and consistent expression, differential analysis was performed using the TCGA-LUAD and GEO datasets. LUAD subtypes were determined based on these hub genes, followed by prognostic analysis. Immunological scoring and infiltration analysis were conducted using NETosis scores (N-scores) derived from the TCGA-LUAD dataset. A clinical prognostic model was established and analyzed, and its clinical applications explored. Twenty-two hub genes were identified, and consensus clustering was used to identify two subgroups based on their expression levels. The Kaplan-Meier (KM) curves demonstrated statistically significant differences in prognosis between the two LUAD subtypes. Based on the median score, patients were further divided into high and low N-score groups, and KM curves showed that the N-scores were more precise at predicting the prognosis of patients with LUAD for overall survival (OS). Immunological infiltration analysis revealed significant differences in the abundances of 10 immune cell infiltrates between the high and low N-score groups. Risk scores indicated significant differences in prognosis between the two extreme score groups. The risk scores for the prognostic model also indicated significant differences between the two groups. The results provide new insights into NETosis-related differentially expressed genes (NRDEGs) associated with chemotherapy resistance in patients with LUAD. The established prognostic model is promising and could help with clinical applications to evaluate patient survival and therapeutic efficiency.

Expression of Anoikis-Related Genes and Potential Biomarkers in Colon Cancer Based on RNA-seq and scRNA-seq.

Colon cancer (CC) is a malignant tumor in the colon. Despite some progress in the early detection and treatment of CC in recent years, some patients still experience recurrence and metastasis. Therefore, it is urgent to better predict the prognosis of CC patients and identify new biomarkers. Recent studies have shown that anoikis-related genes (ARGs) play a significant role in the progression of many tumors. Hence, it is essential to confirm the role of ARGs in the development and treatment of CC by integrating scRNA-seq and transcriptome data. This study integrated transcriptome and single-cell sequencing (scRNA-seq) data from CC samples to evaluate patient stratification, prognosis, and ARG expression in different cell types. Specifically, differential expression of ARGs was identified through consensus clustering to classify CC subtypes. Subsequently, a CC risk model composed of CDKN2A, NOX4, INHBB, CRYAB, TWIST1, CD36, SERPINE1, and MMP3 was constructed using prognosis-related ARGs. Finally, using scRNA-seq data of CC, the expression landscape of prognostic genes in different cell types and the relationship between important immune cells and other cells were explored. Through the above analysis, two CC subtypes were identified, showing significant differences in prognosis and clinical factors. Subsequently, a risk model comprising aforementioned genes successfully categorized all CC samples into two risk groups, which also exhibited significant differences in prognosis, clinical factors, involved pathways, immune landscape, and drug sensitivity. Multiple pathways (cell adhesion molecules (CAMs), and extracellular matrix (ECM) receptor interaction) and immune cells/immune functions (B cell naive, dendritic cell activate, plasma cells, and T cells CD4 memory activated) related to CC were identified. Furthermore, it was found that prognostic genes were highly expressed in various immune cells, and B cells exhibited more and stronger interaction pathways with other cells. The results of this study may provide references for personalized treatment and potential biomarker identification in CC.

The CONUT score is associated with the pathologic grade in non-small cell lung cancer.

Nutritional scores have been reported to be useful prognostic factors for various cancers. This study evaluated the usefulness of the preoperative controlling nutritional status (CONUT) score as a predictor of recurrence of non-small cell lung cancer (NSCLC). The present study included 422 patients with stage I-IIIA NSCLC who underwent complete resection at Tohoku University Hospital between January 2010 and December 2016. The patients were divided into the low-CONUT and high-CONUT groups based on their CONUT scores. Overall survival (OS), recurrence-free survival (RFS), and cumulative recurrence rates in the low- and high-CONUT groups were evaluated retrospectively. One hundred forty-seven patients (34.8%) were assigned to the high-CONUT group. The high-CONUT group had a significantly worse performance status, pleural invasion, vascular invasion, and lung metastasis. In the whole cohort, the low-CONUT group showed better overall survival, recurrence-free survival, and a low cumulative recurrence rate in comparison to the high-CONUT group. There was no significant difference in prognosis or recurrence between the low- and high-CONUT groups after propensity score matching. Patients with a high CONUT score may be at high risk of recurrence because of the high frequency of pleural invasion, vascular invasion, and lung metastasis.

Decoding the molecular landscape: A novel prognostic signature for uveal melanoma unveiled through programmed cell death-associated genes.

Uveal melanoma (UM) is a rare but aggressive malignant ocular tumor with a high metastatic potential and limited therapeutic options, currently lacking accurate prognostic predictors and effective individualized treatment strategies. Public databases were utilized to analyze the prognostic relevance of programmed cell death-related genes (PCDRGs) in UM transcriptomes and survival data. Consensus clustering and Lasso Cox regression analysis were performed for molecular subtyping and risk feature construction. The PCDRG-derived index (PCDI) was evaluated for its association with clinicopathological features, gene expression, drug sensitivity, and immune infiltration. A total of 369 prognostic PCDRGs were identified, which could cluster UM into 2 molecular subtypes with significant differences in prognosis and clinicopathological characteristics. Furthermore, a risk feature PCDI composed of 11 PCDRGs was constructed, capable of indicating prognosis in UM patients. Additionally, PCDI exhibited correlations with the sensitivity to 25 drugs and the infiltration of various immune cells. Enrichment analysis revealed that PCDI was associated with immune regulation-related biological processes and pathways. Finally, a nomogram for prognostic assessment of UM patients was developed based on PCDI and gender, demonstrating excellent performance. This study elucidated the potential value of PCDRGs in prognostic assessment for UM and developed a corresponding risk feature. However, further basic and clinical studies are warranted to validate the functions and mechanisms of PCDRGs in UM.

Unraveling the unfolded protein response signature: implications for tumor immune microenvironment heterogeneity and clinical prognosis in stomach cancer.

Stomach cancer is a leading cause of cancer-related deaths globally due to its high grade and poor response to treatment. Understanding the molecular network driving the rapid progression of stomach cancer is crucial for improving patient outcomes. This study aimed to investigate the role of unfolded protein response (UPR) related genes in stomach cancer and their potential as prognostic biomarkers. RNA expression data and clinical follow-up information were obtained from the TCGA and GEO databases. An unsupervised clustering algorithm was used to identify UPR genomic subtypes in stomach cancer. Functional enrichment analysis, immune landscape analysis, and chemotherapy benefit prediction were conducted for each subtype. A prognostic model based on UPR-related genes was developed and validated using LASSO-Cox regression, and a multivariate nomogram was created. Key gene expression analyses in pan-cancer and in vitro experiments were performed to further investigate the role of the identified genes in cancer progression. A total of 375 stomach cancer patients were included in this study. Analysis of 113 UPR-related genes revealed their close functional correlation and significant enrichment in protein modification, transport, and RNA degradation pathways. Unsupervised clustering identified two molecular subtypes with significant differences in prognosis and gene expression profiles. Immune landscape analysis showed that UPR may influence the composition of the tumor immune microenvironment. Chemotherapy sensitivity analysis indicated that patients in the C2 molecular subtype were more responsive to chemotherapy compared to those in the C1 molecular subtype. A prognostic signature consisting of seven UPR-related genes was constructed and validated, and an independent prognostic nomogram was developed. The gene IGFBP1, which had the highest weight coefficient in the prognostic signature, was found to promote the malignant phenotype of stomach cancer cells, suggesting its potential as a therapeutic target. The study developed a UPR-related gene classifier and risk signature for predicting survival in stomach cancer, identifying IGFBP1 as a key factor promoting the disease's malignancy and a potential therapeutic target. IGFBP1's role in enhancing cancer cell adaptation to endoplasmic reticulum stress suggests its importance in stomach cancer prognosis and treatment.

Identification and Validation of Basement Membrane Related LncRNA Signatures as a Novel Prognostic Model for Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is a significant cancer with limited treatments and a poor prognosis, with the basement membrane (BM) playing a crucial role in its initiation and growth. This study utilized data from The Cancer Genome Atlas and the Gene Expression Omnibus (GEO) databases to identify basement membrane-related genes differentially expressed in HCC. Through gene co-expression analysis, BM-associated long non-coding RNAs (lncRNAs) were discovered. LncRNAs related to HCC survival were selected via univariate analysis, and a prognostic model was constructed using LASSO regression and multivariate analysis. This model effectively classified HCC patients into high and low-risk groups, uncovering significant differences in prognosis, immune response, mutation, and drug sensitivity. Six BM-related lncRNAs (GSEC, MIR4435-2HG, AC092614.1, AC127521.1, LINC02580, and AC008050.1) were validated in normal and HCC cell lines, and the key role of AC092614.1 in regulating proliferation, migration, and invasion of HCC cells in vitro was explored. This research emphasizes the prognostic and therapeutic relevance of BM-related lncRNAs in HCC, highlighting AC092614.1's role in disease progression and as a potential target for targeted therapy.

Crosstalk of disulfidptosis-related subtypes identifying a prognostic signature to improve prognosis and immunotherapy responses of clear cell renal cell carcinoma patients

BackgroundDisulfidptosis is a novel form of programmed cell death induced by high SLC7A11 expression under glucose starvation conditions, unlike other known forms of cell death. However, the roles of disulfidptosis in cancers have yet to be comprehensively well-studied, particularly in ccRCC.MethodsThe expression profiles and somatic mutation of DGs from the TCGA database were investigated. Two DGs clusters were identified by unsupervised consensus clustering analysis, and a disulfidptosis-related prognostic signature (DR score) was constructed. Furthermore, the predictive capacity of the DR score in prognosis was validated by several clinical cohorts. We also developed a nomogram based on the DR score and clinical features. Then, we investigated the differences in the clinicopathological information, TMB, tumor immune landscapes, and biological characteristics between the high- and low-risk groups. We evaluated whether the DR score is a robust tool for predicting immunotherapy response by the TIDE algorithm, immune checkpoint genes, submap analysis, and CheckMate immunotherapy cohort.ResultsWe identified two DGs clusters with significant differences in prognosis, tumor immune landscapes, and clinical features. The DR score has been demonstrated as an independent risk factor by several clinical cohorts. The high-risk group patients had a more complicated tumor immune microenvironment and suffered from more tumor immune evasion in immunotherapy. Moreover, patients in the low-risk group had better prognosis and response to immunotherapy, particularly in anti-PD1 and anti-CTLA-4 inhibitors, which were verified in the CheckMate immunotherapy cohort.ConclusionThe DR score can accurately predict the prognosis and immunotherapy response and assist clinicians in providing a personalized treatment regime for ccRCC patients.

Construction and verification of an endoplasmic reticulum stress-related prognostic model for endometrial cancer based on WGCNA and machine learning algorithms.

Endoplasmic reticulum (ER) stress arises from the accumulation of misfolded or unfolded proteins within the cell and is intricately linked to the initiation and progression of various tumors and their therapeutic strategies. However, the precise role of ER stress in uterine corpus endometrial cancer (UCEC) remains unclear. Data on patients with UCEC and control subjects were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Using differential expression analysis and Weighted Gene Co-expression Network Analysis (WGCNA), we identified pivotal differentially expressed ER stress-related genes (DEERGs). Further validation of the significance of these genes in UCEC was achieved through consensus clustering and bioinformatic analyses. Using Cox regression analysis and several machine learning algorithms (least absolute shrinkage and selection operator [LASSO], eXtreme Gradient Boosting [XGBoost], support vector machine recursive feature elimination [SVM-RFE], and Random Forest), hub DEERGs associated with patient prognosis were effectively identified. Based on the four identified hub genes, a prognostic model and nomogram were constructed. Additionally, a drug sensitivity analysis and in vitro validation experiments were performed. A total of 94 DEERGs were identified in patients with UCEC and healthy controls. Consensus clustering analysis revealed significant differences in prognosis, typical immune checkpoints, and tumor microenvironments between the subtypes. Using Cox regression analysis and machine learning, four hub DEERGs, MYBL2, RADX, RUSC2, and CYP46A1, were identified to construct a prognostic model. The reliability of the model was validated using receiver operating characteristic (ROC) curves. Decision curve analysis (DCA) demonstrated the superior predictive ability of the nomogram in terms of 3- and 5-year survival, compared with that of other clinical indicators. Drug sensitivity analysis revealed increased sensitivity to dactinomycin, docetaxel, selumetinib, and trametinib in the low-risk group. The expressions of RADX, RUSC2, and CYP46A1 were downregulated, whereas that of MYBL2 was upregulated in UCEC tissues, as demonstrated by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and immunofluorescence assays. This study developed a stable and accurate prognostic model based on multiple bioinformatics analyses, which can be used to assess the prognosis of UCEC. This model may contribute to future research on the risk stratification of patients with UCEC and the formulation of novel treatment strategies.

Identification of two distinct head and neck squamous cell carcinoma subtypes based on fatty acid metabolism-related signatures: Implications for immunotherapy and chemotherapy.

The dysregulation of lipid metabolism is a critical factor in the initiation and progression of tumors. In this investigation, we aim to characterize the molecular subtypes of head and neck squamous cell carcinoma (HNSCC) based on their association with fatty acid metabolism and develop a prognostic risk model. The transcriptomic and clinical data about HNSCC were obtained from public databases. Clustering analysis was conducted on fatty acid metabolism genes (FAMG) associated with prognosis, utilizing the non-negative matrix factorization algorithm. The immune infiltration, response to immune therapy, and drug sensitivity between molecular subtypes were evaluated. Differential expression genes were identified between subtypes, and a prognostic model was constructed using Cox regression analyses. A nomogram for HNSCC was constructed and evaluated. Thirty FAMGs have been found to exhibit differential expression in HNSCC, out of which three are associated with HNSCC prognosis. By performing clustering analysis on these 3 genes, 2 distinct molecular subtypes of HNSCC were identified that exhibit significant heterogeneity in prognosis, immune landscape, and treatment response. Using a set of 7778 genes that displayed differential expression between the 2 molecular subtypes, a prognostic risk model for HNSCC was constructed comprising 11 genes. This model has the ability to stratify HNSCC patients into high-risk and low-risk groups, which exhibit significant differences in prognosis, immune infiltration, and immune therapy response. Moreover, our data suggest that this risk model is negatively correlated with B cells and most T cells, but positively correlated with macrophages, mast cells, and dendritic cells. Ultimately, we constructed a nomogram incorporating both the risk signature and radiotherapy, which has demonstrated exceptional performance in predicting prognosis for HNSCC patients. A molecular classification system and prognostic risk models were developed for HNSCC based on FAMGs. This study revealed the potential involvement of FAMGs in modulating tumor immune microenvironment and response to treatment.

Integrated Somatic Mutation Network Diffusion Model for Stratification of Breast Cancer into Different Metabolic Mutation Subtypes

Background: Mutations in metabolism-related genes in somatic cells potentially lead to disruption of metabolic pathways, which results in patients exhibiting different molecular and pathological features. background: Mutations in metabolism-related genes in somatic cells potentially lead to disruption of metabolic pathways, which results in patients exhibiting different molecular and pathological features. Objective: In this study, we focused on somatic mutation data to investigate the significance of metabolic mutation typing in guiding the prognosis and treatment of breast cancer patients. objective: In this study, we focused on somatic mutation data to investigate the significance of metabolic mutation typing in guiding the prognosis and treatment of breast cancer patients. Methods: The somatic mutation profile of breast cancer patients was analyzed and smoothed by utilizing a network diffusion model within the protein-protein interaction network to construct a comprehensive somatic mutation network diffusion profile. Subsequently, a deep clustering approach was employed to explore metabolic mutation typing in breast cancer based on integrated metabolic pathway information and the somatic mutation network diffusion profile. In addition, we employed deep neural networks and machine learning prediction models to assess the feasibility of predicting drug responses through somatic mutation network diffusion profiles. Results: Significant differences in prognosis and metabolic heterogeneity were observed among the different metabolic mutation subtypes, characterized by distinct alterations in metabolic pathways and genetic mutations, and these mutational features offered potential targets for subtype-specific therapies. Furthermore, there was a strong consistency between the results of the drug response prediction model constructed on the somatic mutation network diffusion profile and the actual observed drug responses. Conclusion: Metabolic mutation typing of cancer assists in guiding patient prognosis and treatment.

Identifying an immunogenic cell death-related gene signature contributes to predicting prognosis, immunotherapy efficacy, and tumor microenvironment of lung adenocarcinoma.

Immunogenic cell death (ICD) is a regulated form of cell death that triggers an adaptive immune response. The objective of this study was to investigate the correlation between ICD-related genes (ICDGs) and the prognosis and the immune microenvironment of patients with lung adenocarcinoma (LUAD). ICD-associated molecular subtypes were identified through consensus clustering. Subsequently, a prognostic risk model comprising 5 ICDGs was constructed using Lasso-Cox regression in the TCGA training cohort and further tested in the GEO cohort. Enriched pathways among the subtypes were analyzed using GO, KEGG, and GSVA. Furthermore, the immune microenvironment was assessed using ESTIMATE, CIBERSORT, and ssGSEA analyses. Consensus clustering divided LUAD patients into three ICDG subtypes with significant differences in prognosis and the immune microenvironment. A prognostic risk model was constructed based on 5 ICDGs and it was used to classify the patients into two risk groups; the high-risk group had poorer prognosis and an immunosuppressive microenvironment characterized by low immune score, low immune status, high abundance of immunosuppressive cells, and high expression of tumor purity. Cox regression, ROC curve analysis, and a nomogram indicated that the risk model was an independent prognostic factor. The five hub genes were verified by TCGA database, cell sublocalization immunofluorescence analysis, IHC images and qRT-PCR, which were consistent with bioinformatics analysis. The molecular subtypes and a risk model based on ICDGs proposed in our study are both promising prognostic classifications in LUAD, which may provide novel insights for developing accurate targeted cancer therapies.

DIMINISHED EXPRESSION OF GLS IN CD4 + T CELLS SERVES AS A PROGNOSTIC INDICATOR ASSOCIATED WITH CUPROPTOSIS IN SEPTIC PATIENTS.

Objectives: Sepsis is defined as a life-threatening disease associated with a dysfunctional host immune response. Stratified identification of critically ill patients might significantly improve the survival rate. The present study sought to probe molecular markers associated with cuproptosis in septic patients to aid in stratification and improve prognosis. Methods: We studied expression of cuproptosis-related genes (CRGs) using peripheral blood samples from septic patients. Further classification was made by examining levels of expression of these potential CRGs in patients. Coexpression networks were constructed using the Weighted Gene Coexpression Network Analysis (WGCNA) method to identify crucial prognostic CRGs. Additionally, we utilized immune cell infiltration analysis to further examine the immune status of septic patients with different subtypes and its association with the CRGs. scRNA-seq data were also analyzed to verify expression of key CRGs among specific immune cells. Finally, immunoblotting, flow cytometry, immunofluorescence, and CFSE analysis were used to investigate possible regulatory mechanisms. Results: We classified septic patients based on CRG expression levels and found significant differences in prognosis and gene expression patterns. Three key CRGs that may influence the prognosis of septic patients were identified. A decrease in GLS expression was subsequently verified in Jurkat cells, accompanied by a reduction in O-GlcNAc levels, and chelation of copper by tetrathiomolybdate could not rescue the reduction in GLS and O-GLcNAc levels. Moreover, immoderate chelation of copper was detrimental to mitochondrial function, cell viability, and cell proliferation, as well as the immune status of the host. Conclusion: We have identified novel molecular markers associated with cuproptosis, which could potentially function as diagnostic indicators for septic patients. The reversible nature of the observed alterations in FDX1 and LIAS was demonstrated through copper chelation, whereas the correlation between copper and the observed changes in GLS requires further investigation.