Predictor Of Prognosis In Patients Research Articles

Identification of oxidative stress signatures of lung adenocarcinoma and prediction of patient prognosis or treatment response with single-cell RNA sequencing and bulk RNA sequencing data

Lung adenocarcinoma (LUAD), the most common subtype of lung cancer globally, has seen improved prognosis with advancements in diagnostic, surgical, radiotherapy, and molecular therapy techniques, while its 5-year survival rate remains low. Molecular biomarkers provide prognostic value. Oxidative stress factors, such as reactive nitrogen species and ROS, are crucial in various stages of tumor progression, influencing cell transformation, proliferation, angiogenesis, and metastasis. ROS demonstrate dual roles, affecting tumor cells, hypoxia sensitivity, and the microenvironment. Comprehensive analysis of oxidative stress in LUAD has not been conducted to date. Therefore, we systematically investigated the regulatory patterns of oxidative stress in LUAD based on oxidative stress-related genes and correlated these patterns with cellular infiltration characteristics of the tumor immune microenvironment. The model utilizes single-factor Cox analysis to screen key differential genes with prognostic value and employs least absolute shrinkage and selection operator (LASSO) penalized Cox regression analysis to construct a prognostic-related prediction model. Ten candidate genes were selected based on this model. The risk score was constructed using the coefficients and expression levels of these ten genes. Furthermore, the impact of this risk score on overall survival (OS) was determined. Two genes with the most significant differential expression, SFTPB and S100P, were selected through qRT-PCR. Cell experiments including CCK-8, Edu, transwell assays confirmed their effects on lung cancer cells growth, consistent with the results of bioinformatics analysis. These findings suggested that this model held potential clinical value for evaluating the prognosis of lung adenocarcinoma.

Immune- and metabolism-related gene signature analysis uncovers the prognostic and immune microenvironments of hepatocellular carcinoma

BackgroundMetabolic reprogramming is an emerging hallmark that influences the tumour microenvironment (TME) by regulating the behavior of cancer cells and immune cells. The relationship between metabolism and immunity remains elusive. The purpose of this study was to explore the predictive value of immune- and metabolism-related genes in hepatocellular carcinoma (HCC) and their intricate interplay with TME.MethodsWe established the immune- and metabolism-related signature (IMRPS) based on the LIHC cohort from The Cancer Genome Atlas (TCGA) dataset. Kaplan–Meier analysis, receiver operating characteristic (ROC) curve analysis and Cox regression analysis confirmed the prognostic value of IMRPS. We investigated differences in immune cell infiltration, clinical features, and therapeutic response between risk groups. The quantitative real-time PCR (qPCR) was used to confirm the expression of signature genes. Immunohistochemical staining was performed to evaluate immune infiltration features in HCC tissue samples. We conducted cell experiments including gene knockout, cell counting kit-8 (CCK-8), and flow cytometry to explore the role of the IMRPS key gene UCK2 in HCC. RNA-seq was used to further investigate the potential underlying mechanism involved.ResultsThe IMRPS, composed of four genes, SMS, UCK2, PFKFB4 and MAPT, exhibited significant correlations with survival, immune cell infiltration, clinical features, immune checkpoints and therapeutic response. The IMRPS was shown to be an excellent predictor of HCC prognosis. It could stratify patients appropriately and characterize the TME accurately. The high-risk HCC group exhibited an immunosuppressive microenvironment with abundant M2-like macrophage infiltration, which was confirmed by the immunohistochemistry results. The results of qPCR revealed that the expression of signature genes in 20 HCC tissues was significantly greater than that in adjacent normal tissues. After the key gene UCK2 was knocked out, the proliferation of the Huh7 cell line was significantly inhibited, and monocyte-derived macrophages polarized towards an M1-like phenotype in the coculture system. RNA-seq and GSEA suggested that the phenotypes were closely related to the negative regulation of growth and regulation of macrophage chemotaxis.ConclusionsThis study established a new IMRS for the accurate prediction of patient prognosis and the TME, which is also helpful for identifying new targets for the treatment of HCC.

A dynamic online nomogram for predicting renal outcomes of idiopathic membranous nephropathy

BackgroundBecause spontaneous remission is common in IMN, and there are adverse effects of immunosuppressive therapy, it is important to assess the risk of progressive loss of renal function before deciding whether and when to initiate immunosuppressive therapy. Therefore, this study aimed to establish a risk prediction model to predict patient prognosis and treatment response to help clinicians evaluate patient prognosis and decide on the best treatment regimen.MethodsFrom September 2019 to December 2020, a total of 232 newly diagnosed IMN patients from three hospitals in Liaoning Province were enrolled. Logistic regression analysis selected the risk factors affecting the prognosis, and a dynamic online nomogram prognostic model was constructed based on extreme gradient boost, random forest, logistic regression machine learning algorithms. Receiver operating characteristic and calibration curves and decision curve analysis were utilized to assess the performance and clinical utility of the developed model.ResultsA total of 130 patients were in the training cohort and 102 patients in the validation cohort. Logistic regression analysis identified four risk factors: course ≥ 6 months, UTP, D-dimer and sPLA2R-Ab. The random forest algorithm showed the best performance with the highest AUROC (0.869). The nomogram had excellent discrimination ability, calibration ability and clinical practicability in both the training cohort and the validation cohort.ConclusionsThe dynamic online nomogram model can effectively assess the prognosis and treatment response of IMN patients. This will help clinicians assess the patient’s prognosis more accurately, communicate with the patient in advance, and jointly select the most appropriate treatment plan.

Integration of mult-omics and nucleotide metabolism reprogramming signature analysis reveals gastric cancer immunological and prognostic features

BackgroundGastric cancer is a frequent and lethal solid tumor that has a poor prognosis and treatment result. Reprogramming of nucleotide metabolism is a characteristic of cancer development and progression.MethodsWe used a variety of machine learning techniques to create a novel nucleotide metabolism-related index (NMRI) using gastric cancer sample data obtained from the TCGA and GEO databases. This index is based on genes associated to nucleotide metabolism. Gastric cancer patients were categorized into high and low NMRI groups based on NMRI results. The clinical features, tumor immune microenvironment, response to chemotherapy, and response to immunotherapy were then thoroughly examined. In vitro experiments were then used to confirm the biological role of SERPINE1 in gastric cancer.ResultsThe four nucleotide metabolism-related genes that make up NMRI (GAMT, ORC1, CNGB3, and SERPINE1) were verified in an external dataset and are a valid predictor of prognosis for patients with gastric cancer. The high NMRI group was more responsive to immunotherapy and had greater levels of immune cell infiltration than the low NMRI group. The proliferation and migration of stomach cancer was shown to be decreased by SERPINE1 knockdown in vitro.ConclusionsThis study's NMRI can reliably predict a patient's prognosis for stomach cancer and pinpoint the patient group that will benefit from immunotherapy, offering important new information on the clinical treatment of stomach cancer.

Deep learning-based pathological prediction of lymph node metastasis for patient with renal cell carcinoma from primary whole slide images

BackgroundMetastasis renal cell carcinoma (RCC) patients have extremely high mortality rate. A predictive model for RCC micrometastasis based on pathomics could be beneficial for clinicians to make treatment decisions.MethodsA total of 895 formalin-fixed and paraffin-embedded whole slide images (WSIs) derived from three cohorts, including Shanghai General Hospital (SGH), Clinical Proteomic Tumor Analysis Consortium (CPTAC) and Cancer Genome Atlas (TCGA) cohorts, and another 588 frozen section WSIs from TCGA dataset were involved in the study. The deep learning-based strategy for predicting lymphatic metastasis was developed based on WSIs through clustering-constrained-attention multiple-instance learning method and verified among the three cohorts. The performance of the model was further verified in frozen-pathological sections. In addition, the model was also tested the prognosis prediction of patients with RCC in multi-source patient cohorts.ResultsThe AUC of the lymphatic metastasis prediction performance was 0.836, 0.865 and 0.812 in TCGA, SGH and CPTAC cohorts, respectively. The performance on frozen section WSIs was with the AUC of 0.801. Patients with high deep learning-based prediction of lymph node metastasis values showed worse prognosis.ConclusionsIn this study, we developed and verified a deep learning-based strategy for predicting lymphatic metastasis from primary RCC WSIs, which could be applied in frozen-pathological sections and act as a prognostic factor for RCC to distinguished patients with worse survival outcomes.

Prediction of Prognosis in Patients with Sepsis Based on Platelet-Related Genes.

The study aimed to develop a risk prognostic model using platelet-related genes (PRGs) to predict sepsis patient outcomes. Sepsis patient data from the Gene Expression Omnibus (GEO) database and PRGs from the Molecular Signatures Database (MSigDB) were analyzed. Differential analysis identified 1139 differentially expressed genes (DEGs) between sepsis and control groups. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses revealed enrichment in functions related to immune cell regulation and pathways associated with immune response and infectious diseases. A risk prognostic model was established using LASSO and Cox regression analyses, incorporating 10 PRGs selected based on their association with sepsis prognosis. The model demonstrated good stratification and prognostic effects, confirmed by survival and receiver operating characteristic (ROC) curve analyses. It served as an independent prognostic factor in sepsis patients. Further analysis using the CIBERSORT algorithm showed higher infiltration of activated natural killer (NK) cells and lower infiltration of CD8 T cells and CD4 T cells naïve in the high-risk group compared to the low-risk group. Additionally, expression levels of human leukocyte antigen (HLA) genes were significantly lower in the high-risk group. In conclusion, the 10-gene risk model based on PRGs accurately predicted sepsis patient prognosis and immune infiltration levels. This study provides valuable insights into the role of platelets in sepsis prognosis and diagnosis, offering potential implications for personalized treatment strategies.

The mechanism of RGS5 regulating gastric cancer mismatch repair protein.

The incidence and mortality rates of gastric cancer (GC) remain alarmingly high worldwide, imposing a substantial healthcare burden. In this study, we utilized data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A 4-gene prognostic model was developed to predict patient prognosis, and its accuracy was validated across multiple datasets. Patients with a low-risk score exhibited improved prognosis, elevated tumor mutation burden, heightened sensitivity to both immunotherapy and conventional chemotherapy. Notably, our investigation revealed that the key gene RGS5 positively modulates the expression of mismatch repair proteins via c-Myc. Furthermore, co-immunoprecipitation (COIP) assays demonstrated the interaction between RGS5 and c-Myc. Additionally, we confirmed that RGS5 regulates c-Myc through the ubiquitin-proteasome pathway. Moreover, RGS5 was identified as a positive regulator of PD-L1 expression and exhibited a negative correlation with the majority of immune cells. These findings underscore the potential of RGS5 as a novel biomarker and therapeutic target in the context of GC.

Non-lipid genetic predictors of the development of coronary heart disease and myocardial infarction in patients with hypertension

The associations between the main non-lipid genomic biomarkers and coronary heart disease occurrence and the unstable course of the disease with the development of myocardial infarction in 164 patients with stage I–III hypertension were investigated. The study was conducted in two stages. At the first stage, the non-lipid genetic predictors of coronary heart disease were determined without considering the characteristics of its clinical course. At the second stage, the possibility of identifying genetic predictors of the complicated course of coronary heart disease with the development of myocardial infarction was studied. In hypothesis testing, the difference was considered significant at p 0.05. It was found that the presence of coronary heart disease in patients with hypertension was associated with a significant predominance of genetic biomarkers in four single-nucleotide polymorphisms: in the hemostasis system (4G4G SERPINE 1), pro-inflammatory cytokines (T allele IL-1b-511, C allele IL-1b-1473), and innate immunity (FF TLR3-412). The development of myocardial infarction was associated with two genetic polymorphisms: pro-inflammatory cytokine system (CC IL-6-174) and hemostasis (4G4G SERPINE 1). In general, among the single-nucleotide polymorphisms studied, statistically significant results in predicting coronary heart disease were demonstrated by genetic biomarkers of the hemostatic system, pro-inflammatory cytokines, and innate immunity. In relation to myocardial infarction, genetic biomarkers of the hemostatic system and pro-inflammatory cytokines may have prognostic value. The use of genetic biomarker data as unfavorable prognosis predictors in patients with hypertension enables better risk stratification and assessment of the prognosis in these patients, which will significantly reduce the costs of conducting a genome-wide study.

Vitamin D-Regulated miR-589-3p in Patients with Cervical Cancer Predicts Patient Prognosis and is Involved in Tumor Progression

The study aims to evaluate the performance of Vitamin D/calcitriol-induced miR-589-3p in predicting the prognosis of cervical cancer patients and its role in cancer cell function. To identify differentially expressed miRNAs (DEMs) related to calcitriol treatment, the GSE61829 dataset was analyzed. MiR-589-3p expression levels were verified in cervical cancer patients. The association of miR-589-3p with overall survival was investigated using Kaplan-Meier survival analyses and the multi-variate Cox proportional hazards model analysis. The effects of miR-589-3p on cervical cancer cells and calcitriol-treated cells were examined using the MTT assay and Transwell migration/invasion assay. From GSE61829 dataset, a total of eleven DEMs were identified, including miR-589-3p. MiR-589-3p was found to be decreased in cervical cancer but increased after one-year intake of Vitamin D. Low miR-589-3p after one-year intake of Vitamin D was identified as a predictive factor for low survival probability (p = 0.0059) with a significant impact on the death risk (HR: 3.04; 95%CI: 1.47-6.29; p = 0.003). MiR-589-3p overexpression inhibited the proliferation and migration/invasion of cervical cancer cells and calcitriol-treated cervical cancer cells. In conclusion, miR-589-3p can be induced by Vitamin D/calcitriol treatment and inhibit cervical cancer progression. MiR-589-3p has the potential to predict overall survival in patients with cervical cancer.

Serum beta2-microglobulin acts as a biomarker for severity and prognosis in glioma patients: a preliminary clinical study

BackgroundGliomas are the deadliest malignant tumors of the adult central nervous system. We previously discovered that beta2-microglobulin (B2M) is abnormally upregulated in glioma tissues and that it exerts a range of oncogenic effects. Besides its tissue presence, serum B2M levels serve as biomarkers for various diseases. This study aimed to explore whether serum B2M levels can be used in the diagnosis and prognosis of gliomas.MethodsMedical records from 246 glioma patients were retrospectively analyzed. The relationship between preoperative serum B2M levels and clinicopathological features was examined. Kaplan-Meier analysis, alongside uni- and multivariate Cox regression, assessed the association between B2M levels, systemic inflammatory markers, and glioma patient prognosis. Receiver operating characteristic (ROC) curve analysis evaluated the diagnostic significance of these biomarkers specifically for glioblastoma (GBM).ResultsPatients with malignant gliomas exhibited elevated preoperative serum B2M levels. Glioma patients with high serum B2M levels experienced shorter survival times. Multivariate Cox analysis determined the relationship between B2M levels (hazard ratio = 1.92, 95% confidence interval: 1.05–3.50, P = 0.034) and the overall survival of glioma patients. B2M demonstrated superior discriminatory power in distinguishing between GBM and non-GBM compared to inflammation indicators. Moreover, postoperative serum B2M levels were lower than preoperative levels in the majority of glioma patients.ConclusionsHigh preoperative serum B2M levels correlated with malignant glioma and a poor prognosis. Serum B2M shows promise as a novel biomarker for predicting patient prognosis and reflecting the therapeutic response.

Circulating tumor cells are a good predictor of tumor recurrence in clinical patients with gastric cancer

Circulating tumor cells (CTCs) as a liquid biopsy have great potential in clinical applications and basic cancer research, but their clinical use in gastric cancer remains unclear. This study investigated whether CTCs could be used as a potential prognosis predictor in patients with gastric cancer. A total of 120 patients with pathologically confirmed gastric cancer were enrolled from January 1, 2015, to December 1, 2019. All patients were initially diagnosed without previous treatment, and then the number of CTCs was detected using the NEimFISH method before radical surgical resection. Regular follow-up was performed in all patients, and the correlations between the number of CTCs and clinical endpoints, such as disease-free survival (DFS) and overall survival (OS), were evaluated. The univariate and multivariate hazard ratios were calculated using the Cox proportional hazard model. Based on the number of CTCs, we defined CTCs ≥ 2 per 7.5 mL of whole blood as the positive group and CTCs < 2 as the negative group. Among the 120 patients who underwent CTC detection before surgery, the rate of CTC-positive patients was 64.17% (77/120) of which stage I and II patients accounted for 22.50% and stage III patients accounted for 41.67% (P = 0.014). By detecting CTCs before surgery and at the time of recurrence, the number of CTCs tends to increase concomitantly with disease progression (median: 2 VS 5 per 7.5 mL). Multivariate analysis showed that age (HR, 0.259; 95% CI, 0.101–0.662; P = 0.005), D-dimer (HR, 3.146; 95% CI, 1.169–8.461; P = 0.023), and lymph node metastasis (HR, 0.207; 95% CI, 0.0071–0.603; P = 0.004) were factors correlated with CTCs. In addition, the median follow-up of all the patients was 38.0 months (range of 28–80 months); the DFS in CTC-positive patients was significantly shorter than that of the CTC-negative patients, and a significant difference was found based on the Cox proportional hazard regression model analysis (44.52 ± 2.83 m vs. 74.99 ± 2.78 m, HR = 4.550, P = 0.018). The OS was shorter in the CTC-positive group than in the CTC-negative group before the operation, but the result was not significant based on the Cox proportional hazard regression model analysis (47.58 ± 2.46 m vs. 70.68 ± 3.53 m, HR = 2.261, P = 0.083). The number of CTCs tends to increase concomitantly with disease progression. In addition, the detection of CTCs was an independent predictor of shorter DFS in gastric cancer. However, the relationship between CTCs and OS needs to be determined in future studies.

Identification of disulfidptosis-associated genes and characterization of immune cell infiltration in thyroid carcinoma.

The primary objective of this study is to conduct a comprehensive screening and analysis of differentially expressed genes related to disulfidoptosis (DEDRGs) in thyroid carcinoma (THCA). This entails delving into the intricate characterization of immune cell infiltration within the THCA context and subsequently formulating and validating a novel prognostic model. To achieve our objectives, we first delineated two distinct subtypes of disulfidoptosis-related genes (DRGs) via consensus clustering methodology. Subsequently, employing the limma R package, we identified the DEDRGs critical for our investigation. These DEDRGs underwent meticulous validation across various databases, alongside an in-depth analysis of gene regulation. Employing functional enrichment techniques, we explored the potential molecular mechanisms underlying disulfidoptosis in THCA. Furthermore, we scrutinized the immune landscape within the two identified subtypes utilizing CIBERSORT and ESTIMATE algorithms. The construction of the prognostic model for THCA entailed intricate methodologies including univariate, multivariate Cox regression, and LASSO regression algorithms. The validity and efficacy of our prognostic model were corroborated through Kaplan-Meier survival curves and ROC curves. Additionally, a nomogram was meticulously formulated to facilitate the prediction of patient prognosis. To fortify our findings, we conducted a comprehensive Bayesian co-localization analysis coupled with rigorous in vitro experimentation, aimed at unequivocally establishing the validity of the identified DEDRGs. Our analyses unveiled Cluster C1, characterized by elevated expression levels of DEDRGs, as harboring a favorable prognosis accompanied by abundant immune cell infiltration. Correlation analyses underscored predominantly positive associations among the DEDRGs, further affirming their significance in THCA. Differential expression patterns of DEDRGs between tumor samples and normal tissues were evident across the GEPIA and HPA databases. Insights from the TIMER database underscored a robust correlation between DEDRGs and immune cell infiltration. KEGG analysis elucidated the enrichment of DEDRGs primarily in pivotal pathways including MAPK, PPAR signaling pathway, and Proteoglycans in cancer. Furthermore, analyses using CIBERSORT and ESTIMATE algorithms shed light on the crucial role played by DEDRGs in shaping the immune microenvironment. The prognostic model, anchored by five genes intricately associated with THCA prognosis, exhibited commendable predictive accuracy and was intricately linked to the tumor immune microenvironment. Notably, patients categorized with low-risk scores stood to potentially benefit more from immunotherapy. The validation of DEDRGs unequivocally underscores the protective role of INF2 in THCA. In summary, our study delineates two discernible subtypes intricately associated with DRGs, revealing profound disparities in immune infiltration and survival prognosis within the THCA milieu. The implications of our findings extend to potential treatment strategies for THCA patients, which could entail targeted interventions directed towards DEDRGs and prognostic genes, thereby influencing disulfidptosis and the immune microenvironment. Moreover, the robust predictive capability demonstrated by our prognostic model, based on the five genes (ANGPTL7, FIRRE, ODAPH, PROKR1, SFRP5), underscores its potential clinical utility in guiding personalized therapeutic approaches for THCA patients.

Integrated analysis reveals a novel 5-fluorouracil resistance-based prognostic signature with promising implications for predicting the efficacy of chemotherapy and immunotherapy in patients with colorectal cancer.

5-Fluorouracil (5-FU) has been used as a standard first-line treatment for colorectal cancer (CRC) patients. Although 5-FU-based chemotherapy and immune checkpoint blockade (ICB) have achieved success in treating CRC, drug resistance and low response rates remain substantial limitations. Thus, it is necessary to construct a 5-FU resistance-related signature (5-FRSig) to predict patient prognosis and identify ideal patients for chemotherapy and immunotherapy. Using bulk and single-cell RNA sequencing data, we established and validated a novel 5-FRSig model using stepwise regression and multiple CRC cohorts and evaluated its associations with the prognosis, clinical features, immune status, immunotherapy, neoadjuvant therapy, and drug sensitivity of CRC patients through various bioinformatics algorithms. Unsupervised consensus clustering was performed to categorize the 5-FU resistance-related molecular subtypes of CRC. The expression levels of 5-FRSig, immune checkpoints, and immunoregulators were determined using quantitative real-time polymerase chain reaction (RT‒qPCR). Potential small-molecule agents were identified via Connectivity Map (CMap) and molecular docking. The 5-FRSig and cluster were confirmed as independent prognostic factors in CRC, as patients in the low-risk group and Cluster 1 had a better prognosis. Notably, 5-FRSig was significantly associated with 5-FU sensitivity, chemotherapy response, immune cell infiltration, immunoreactivity phenotype, immunotherapy efficiency, and drug selection. We predicted 10 potential compounds that bind to the core targets of 5-FRSig with the highest affinity. We developed a valid 5-FRSig to predict the prognosis, chemotherapeutic response, and immune status of CRC patients, thus optimizing the therapeutic benefits of chemotherapy combined with immunotherapy, which can facilitate the development of personalized treatments and novel molecular targeted therapies for patients with CRC.

Survival implications and radiographic predictors of prognosis in patients with IDH-mutant astrocytoma with CDKN2A/b homozygous deletion: An international, multi-centered study.

e14035 Background: Isocitrate Dehydrogenase (IDH) 1 or 2 mutations are associated with improved prognosis in glioma patients. Despite their less aggressive clinical course when compared to IDH-wild type gliomas, molecularly-defined IDH-mutant glioma subtypes with poor prognosis have been identified. Among these, CDKN2A/B homozygous deletion represents the strongest negative genetic predictor of prognosis yet identified in IDH-mutant astrocytoma. With the WHO 2021 classification recognizing CDKN2A/B homozygous deletion as sufficient for the designation of astrocytoma, IDH-mutant, WHO Grade 4, the translational implications and potential for therapies targeting CDKN2A/B homozygous deletion are an area of active investigation. We sought to further characterize overall (OS) and progression-free survival (PFS) of CDKN2A/B deleted patients within a large, multi-centered, international cohort and identify clinical characteristics that predict survival for patients with CDKN2A/B deletion. Methods: Patients with IDH-mutant glioma with CDKN2A/B homozygous deletion were retrospectively identified across four institutions (Massachusetts General Hospital, USA; University Hospital Heidelberg, Germany; University Hospital Dresden, Germany, and Foudan-Shanghai, China). IDH-mutation status was determined by immunohistochemistry or next-generation sequencing. CDKN2A/B status was determined by hybridized DNA capture or FISH. Results: We identified 75 patients with IDH-mutant astrocytoma with CDKN2A/B homozygous deletion. The cohort had a median follow-up time of 5.0 years, was 56% male, and had an average age of 38.8 years. Patients were treated with chemotherapy and radiation following initial surgery in 77% of cases. Preoperative contrast enhancement and T2 FLAIR mismatch were identified in 56 and 44% of cases, respectively. Compared to a cohort of 51 patients with CDKN2A/B intact, grade 4, IDH-mutant astrocytoma, those with CDKN2A/B-deleted tumors had significantly shorter median OS and PFS (OS: 4 vs 13.5 years, HR = 6.8, HR 95% CI = 3.1, 14.6, p &lt; 0.0001; PFS: 2.8 vs 5.9 years, HR = 3.0, HR 95% CI = 1.7, 5.2, p = 0.0001). Within the CDKN2A/B deleted cohort, preoperative contrast enhancement predicted worse OS, but not PFS (OS: HR = 2.7, 95% CI = 1.1, 7.4, p = 0.04; PFS: HR = 1.17, 95% CI = 0.7, 3.7, p = 0.24). Conclusions: These data reinforce the strong negative prognostic impact of CDKN2A/B homozygous deletion on OS and PFS and identify a novel association between preoperative contrast enhancement and shorter OS in patients with CDKN2A/Bdeleted, IDH-mutant astrocytoma.

Association of Pan Immune-Inflammation Value with Long Term Outcomes of Acute Decompensated Heart Failure.

Although there have been significant improvements in the treatment of heart failure (HF) in recent decades, its prognosis remains poor. Although there are many biomarkers that can help predict the prognosis of patients with HF, there is a need for simpler, cheaper, and more easily available biomarkers. To evaluate the predictive value of pan-immune-inflammation value (PIV) in patients with acute decompensated HF. We analyzed 409 patients with HF with reduced ejection fraction who were hospitalized for acute decompensated HF. Patients were divided into 3 groups according to tertiles of PIV: tertile 1 (PIV < 357.25), tertile 2 (PIV ≥ 357.25 and < 834.55), and tertile 3 (PIV ≥ 834.55). P values < 0.05 were considered statistically significant. Kaplan-Meier curves and Cox proportional hazards regression models were used to evaluate the association between PIV and all-cause mortality. The primary outcome was 5-year all-cause mortality, and the secondary outcomes were in-hospital 30 days,, 180-day, and 1-year all-cause mortality. We showed that higher PIV value was associated with both primary and secondary outcomes. The Kaplan-Meier curve showed that patients with higher PIV values had an increased risk of short- and long-term all-cause mortality (log-rank p < 0.001). In the multivariate analysis, PIV was identified as an independent predictor of long-term all-cause mortality in patients with acute decompensated HF, and we observed a 1.96-fold increase in the hazard of an event (odds ratio: 1.96, 95% confidence interval: 1.330 to 2.908, p = 0.001). Our study showed that the novel biomarker PIV can be used as a predictor of prognosis in patients with acute decompensated HF.

A clinical-radiomic-pathomic model for prognosis prediction in patients with hepatocellular carcinoma after radical resection.

Radical surgery, the first-line treatment for patients with hepatocellular cancer (HCC), faces the dilemma of high early recurrence rates and the inability to predict effectively. We aim to develop and validate a multimodal model combining clinical, radiomics, and pathomics features to predict the risk of early recurrence. We recruited HCC patients who underwent radical surgery and collected their preoperative clinical information, enhanced computed tomography (CT) images, and whole slide images (WSI) of hematoxylin and eosin (H & E) stained biopsy sections. After feature screening analysis, independent clinical, radiomics, and pathomics features closely associated with early recurrence were identified. Next, we built 16 models using four combination data composed of three type features, four machine learning algorithms, and 5-fold cross-validation to assess the performance and predictive power of the comparative models. Between January 2016 and December 2020, we recruited 107 HCC patients, of whom 45.8% (49/107) experienced early recurrence. After analysis, we identified two clinical features, two radiomics features, and three pathomics features associated with early recurrence. Multimodal machine learning models showed better predictive performance than bimodal models. Moreover, the SVM algorithm showed the best prediction results among the multimodal models. The average area under the curve (AUC), accuracy (ACC), sensitivity, and specificity were 0.863, 0.784, 0.731, and 0.826, respectively. Finally, we constructed a comprehensive nomogram using clinical features, a radiomics score and a pathomics score to provide a reference for predicting the risk of early recurrence. The multimodal models can be used as a primary tool for oncologists to predict the risk of early recurrence after radical HCC surgery, which will help optimize and personalize treatment strategies.

Early detection, clinicopathological subtyping and prognosis prediction for patients with endometrial cancer using fragmentoimcs-based liquid-biopsy assay.

Early detection, clinicopathological subtyping and prognosis prediction for patients with endometrial cancer using fragmentoimcs-based liquid-biopsy assay.

Artificial intelligence and machine learning for early cancer prediction and response

Worldwide, cancer claims more lives than any other disease. Although cancer detection, prognosis, and treatment have all advanced, one major obstacle is the lack of personalized, data-driven care. Artificial intelligence (AI), used to forecast and automate numerous malignancies, has emerged as a viable solution for increasing healthcare accuracy and patient outcomes. Artificial intelligence applications in cancer include risk evaluation, early detection, patient prognosis prediction, and treatment selection based on comprehensive data. Machine learning (ML), a form of artificial intelligence that allows computers to learn from training data, is very successful in predicting breast, brain, lung, liver, and prostate cancers. Indeed, AI and machine learning have outperformed physicians in predicting cancer. These technologies can potentially enhance the diagnosis, prediction, and quality of life of patients with a wide range of disorders, not just cancer. As a result, it is critical to enhance existing AI and ML technologies and create new programs to help patients. This article discusses the use of AI and machine learning algorithms in cancer prediction, including its present uses, future possibilities, and limitations.

Role of pathological tumor regression grade of lymph node metastasis following neoadjuvant chemotherapy in locally advanced gastric cancer

AimsTo confirm whether the pathological response of lymph node metastasis (LNM) to neoadjuvant chemotherapy (NCT) can predict the prognosis of patients with gastric cancer (GC). MethodsA total of 979 patients with locally advanced GC were included. χ2 test was used to analyze the relationship between LNM TRG and clinicopathological factors. Cox proportional hazards model was used to analyze the relationship between LNM TRG, clinicopathological factors, and overall survival (OS). ResultsA total of 21,162 lymph nodes were evaluated, with 237 patients (35.4%) in the response group and 433 patients (64.6%) in the non-response group. The non-responsive group was strongly associated with higher ypT, ypN, ypTNM, primary tumor (PT) TRG (all p < 0.001), positive cancer nodules (p = 0.001), and more distant LNM location (p < 0.001). Patients with the same PT TRG but different LNM TRG had different prognosis. There was no difference in OS between the responding and non-responding groups of LNM at location 2, 3, and M. YpN, tumor location, and LNM location were independent prognostic factors. ConclusionsThe combination of LNM TRG and PT TRG could better predict patient prognosis. Lymph node dissection should be routinely performed after NCT to provide the reference of radical resection.

Lactate Profile Assessment-A Good Predictor of Prognosis in Patients with COVID-19 and Septic Shock Requiring Continuous Renal Therapy.

Lactate is a useful prognostic marker, as its level increases in hypoxic tissue and/or during accelerated aerobic glycolysis due to excessive beta-adrenergic stimulation and decreased lactate clearance. The Surviving Sepsis Campaign Bundle 2018 Update suggests premeasurement of lactate within 2-4 h so that physicians perform, assist, administer, and introduce lactate-guided resuscitation to reduce mortality due to sepsis. A total of 108 patients with septic shock who underwent continuous renal replacement therapy (CRRT) for acute kidney injury were enrolled in this observational study. Demographic, clinical, and laboratory data were collected, and patients were divided into two groups: survivors and non-survivors. Multivariate analysis demonstrated that lactate levels at 24 h after initiation of CRRT treatment, but not lactate levels at intensive care unit (ICU) admission, were associated with mortality. Lactate clearance was associated with lower mortality among the survivors (OR = 0.140) at 6 h after ICU admission and late mortality (OR = 0.260) after 24 h. The area under the ROC curves for mortality was 0.682 for initial lactate; 0.797 for lactate at 24 h; and 0.816 for lactate clearance at 24 h. Our result reinforces that the determination of lactate dynamics represents a good predictor for mortality, and serial lactate measurements may be more useful prognostic markers than initial lactate in patients with septic shock.