Predictor Of Prognosis Research Articles

The Prognostic Value and Clinical Significance of lncRNA SNHG5 Expression in Patients with Multiple Malignancies: A Bioinformatic and Meta-analysis.

Long non-coding RNA small nucleolar RNA host gene 5 (lncRNA SNHG5) has been identified as both a promising target for treatment and a predictor of prognosis in diverse types of cancer. The objective of this study was to assess whether lncRNA SNHG5 expression can be utilized as a prognostic biomarker for human cancer. To ensure a thorough search of the literature for relevant English studies published before July 2023, several databases were searched, including PubMed, Web of Science, ProQuest, Cochrane Library, and Google Scholar. The study evaluated the impact of lncRNA SNHG5 on the overall survival (OS) of cancer by calculating the pooled hazard ratio (HR) and odds ratio (OR) with 95% confidence intervals (CIs). To further confirm the accuracy of the findings, the study investigated the expression profile and prognostic significance of lncRNA SNHG5 through the use of GenomicScape, OncoLnc, Kaplan-Meier plotter, and GEPIA databases. In this study, 995 patients were examined across a total of fourteen original studies. The findings indicated that there was a significant relationship between heightened lncRNA SNHG5 expression and reduced OS, as evidenced by both univariate and multivariate analyses (HR = 1.89; 95% CI, 1.44-2.49; p < 0.001; HR = 3.97; 95% CI, 1.80-8.73; p < 0.001, respectively). Pooled OR analysis showed a significant association between over-expression of lncRNA SNHG5 with advanced histological grade (OR = 0.28; 95% CI, 0.11-0.71; p = 0.007), present lymph node metastasis (LNM; OR = 4.28; 95% CI, 2.47-7.43; p < 0.001), and smoking history (OR = 0.27; 95% CI, 0.15-0.49; p < 0.001). Bioinformatic databases confirmed that elevated SNHG5 expression was significantly linked to poor prognosis in cancer patients, including colorectal cancer (CRC), acute myeloid leukemia (AML), and esophageal adenocarcinoma (ESAD), and a longer OS in patients with uterine corpus endometrial carcinoma (UCEC). These results suggest that lncRNA SNHG5 may serve as an adverse prognostic biomarker in several human cancers. Further investigations are needed to better understand the underlying mechanisms that link lncRNA SNHG5 to multiple malignancies.

LOXL1 promotes gastric cancer progression by β-catenin-cyclinD mediated proliferation.

Although much progress has been made in chemotherapy or target therapy for advanced gastric cancer, the prognosis is still poor. It is necessary to screen biomarkers for early diagnosis and prognosis prediction. However, the prognostic value of LOX family in gastric cancer and the underlying molecular mechanisms for promoting the progression of gastric cancer remains unclear. Among five members of LOX family, LOXL1 was the unique independent prognostic risk factor. The nomogram established based on the expression of LOXL1 and other clinical parameters could predict the overall survival rate of gastric cancer. Knockdown (KD) of LOXL1 decreased cell proliferation and led to G1 phase arrest in gastric cells. According to GSEA analysis that LOXL1 was positively correlated with the WNT signaling pathway, in vitro experiment proved that LOXL1-KD reduced the phosphorylation level of β-catenin and the expression of the downstream G1 phase checkpoint CCND1.In Conclusion, LOXL1 has been identified as a potential risk prognostic biomarker for gastric cancer by promoting gastric cancer proliferation via WNT/β-catenin/cyclinD1 pathway.

Prediction of clinical prognosis and drug sensitivity in hepatocellular carcinoma through the combination of multiple cell death pathways.

Hepatocellular carcinoma (HCC) is the sixth most common malignant tumor, highlighting a significant need for reliable predictive models to assess clinical prognosis, disease progression, and drug sensitivity. Recent studies have highlighted the critical role of various programmed cell death pathways, including apoptosis, necroptosis, pyroptosis, ferroptosis, cuproptosis, entotic cell death, NETotic cell death, parthanatos, lysosome-dependent cell death, autophagy-dependent cell death, alkaliptosis, oxeiptosis, and disulfidptosis, in tumor development. Therefore, by investigating these pathways, we aimed to develop a predictive model for HCC prognosis and drug sensitivity. We analyzed transcriptome, single-cell transcriptome, genomic, and clinical information using data from the TCGA-LIHC, GSE14520, GSE45436, and GSE166635 datasets. Machine learning algorithms were used to establish a cell death index (CDI) with seven gene signatures, which was validated across three independent datasets, showing that high CDI correlates with poorer prognosis. Unsupervised clustering revealed three molecular subtypes of HCC with distinct biological processes. Furthermore, a nomogram integrating CDI and clinical information demonstrated good predictive performance. CDI was associated with immune checkpoint genes and tumor microenvironment components using single-cell transcriptome analysis. Drug sensitivity analysis indicated that patients with high CDI may be resistant to oxaliplatin and cisplatin but sensitive to axitinib and sorafenib. In summary, our model offers a precise prediction of clinical outcomes and drug sensitivity for patients with HCC, providing valuable insights for personalized treatment strategies.

Predictive value analysis of albumin-related inflammatory markers for short-term outcomes in patients with In-hospital cardiac arrest

ABSTRACT Objective This study investigated the predictive value of albumin-related inflammatory markers for short-term outcomes in in-hospital cardiac arrest (IHCA) patients. Methods A linear mixed model investigated the dynamic changes of markers within 72 hours after return of spontaneous circulation (ROSC). Time-Dependent COX regression explored the predictive value. Mediation analysis quantified the association of markers with organ dysfunctions and adverse outcomes. Results Prognostic Nutritional Index (PNI) and RDW-Albumin Ratio (RAR) slightly changed (p > 0.05). Procalcitonin-Albumin Ratio (PAR1) initially increased and then slowly decreased. Neutrophil-Albumin Ratio (NAR) and Platelet-Albumin Ratio (PAR2) decreased slightly during 24-48 hours (all p＜0.05). PNI (HR = 1.646, 95%CI (1.033,2.623)), PAR1(HR = 1.69, 95%CI (1.057,2.701)), RAR (HR = 1.752,95%CI (1.103,2.783)) and NAR (HR = 1.724,95%CI (1.078,2.759)) were independently associated with in-hospital mortality. PNI (PM = 45.64%, 95%CI (17.05%,87.02%)), RAR (PM = 45.07%,95%CI (14.59%,93.70%)) and NAR (PM = 46.23%,95%CI (14.59%,93.70%)) indirectly influenced in-hospital mortality by increasing SOFA (central) scores. PNI (PM = 21.75%, 95%CI(0.67%,67.75%)) may also indirectly influenced outcome by increasing SOFA (renal) scores (all p＜0.05). Conclusions Within 72 hours after ROSC, albumin-related inflammatory markers (PNI, PAR1, RAR, and NAR) were identified as potential predictors of short-term prognosis in IHCA patients. They may mediate the adverse outcomes of patients by causing damages to the central nervous system and renal function.

Association of Perfusion Variables with Functional Outcome in Acute Mild Ischemic Stroke Patients or Transient Ischemic Attack.

Some patients with acute minor stroke or transient ischemic attack (TIA) are at risk for a poor prognosis. There are a limited number of studies that have investigated the functional prognosis of acute mild ischemic stroke or TIA based on imaging indicators. This study aims to explore the relationship between Perfusion Variables and poor prognosis in patients with mild ischemic stroke or TIA. A retrospective analysis was conducted on a cohort of 344 patients with mild ischemic stroke or TIA, who were admitted and treated at the First Affiliated Hospital of Soochow University between January 2016 and March 2022. The criteria were National Institutes of Health Stroke Scale (NIHSS) scores of ≤5. Poor outcome was defined as a modified Rankin Scale (mRS) score of ≥2 points at 90 days. Multivariate logistic regression was performed to identify the risk factors associated with clinical outcomes. The receiver operating characteristic (ROC) analysis was used to explore the cutoff value of factors. Following a 3-month follow-up period, 49 (12.4%) out of the 344 patients with mild stroke or TIA demonstrated a poor prognosis. Multivariable regression analysis identified mismatch volume as independent predictors of a poor 90-day prognosis. The ROC curve analysis indicated that a mismatch volume exceeding 16.5 ml was associated with a higher risk of unfavorable functional outcomes. A mismatch volume of ≥16.5mL predicted poor functional outcome in mild stroke or TIA patients.

Prognostic value of the cholesterol-dependent nutritional prognostic index in patients receiving adjuvant chemotherapy after radical esophageal cancer treatment.

Esophageal cancer is the 6th most common cancer in terms of incidence and the 4th most common cause of mortality in China. Given the limited survival outcomes observed in patients with locally advanced disease managed exclusively with surgical intervention, postoperative treatment is critically important. This study sought to assess the prognostic value of total cholesterol (TC) levels and the prognostic nutritional index (PNI) in patients undergoing postoperative chemotherapy for esophageal cancer. A total of 101 patients who underwent postoesophagectomy chemotherapy were included in this retrospective analysis. PNI values for prechemotherapy were calculated for each patient [PNI = albumin + 5 × lymphocyte count] and TC. The optimal cutoff values for these indices were calculated from the receiver operating characteristic (ROC) curve. Patients were stratified into three groups on the basis of their PNI and TC levels. A novel nutritional prognostic index, termed the cholesterol-dependent nutritional prognostic index (CPNI), was developed on the basis of the PNI and TC. Univariate and multivariate Cox analyses were employed to determine the associations between each indicator and clinical outcomes. The prechemotherapy PNI, TC level, and TNM stage became independent risk factors for OS (p < 0.05). Patients in the high PNI-high TC group had significantly improved DFS and OS compared with those in the low PNI-low TC group (p < 0.001) and had a lower early recurrence rate (P = 0.008). In contrast, patients with a high CPNI had a higher mortality rate. The prechemotherapy PNI combined with TC is an accurate and useful predictor of patient prognosis.

Pan-cancer analysis of oncogenic role of CEP55 and experiment validation in clear cell renal cell carcinoma.

Immunotherapy has emerged as a vital component in the contemporary landscape of cancer treatment. Recent studies have indicated that CEP55 plays an oncogenic role; however, its specific mechanisms in promoting tumor proliferation and its potential value in prognosis and immunotherapy prediction across various cancers remain to be elucidated. CEP55 was significantly overexpressed in 22 cancer types compared with their adjacent normal tissues. Elevated CEP55 expression was positively correlated with younger onset age, worse tumor stage, lower response rate to the first treatment, lower tumor-free survival rate, and poorer overall survival (OS) and disease-free survival (DFS) prognosis in most cancers. Moreover, CEP55 expression was positively correlated with its binding and related genes, such as KIF11 (R = 0.83, P < 0.001), CDK1 (R = 0.77, P < 0.001) and CCNA2 (R = 0.76, P < 0.001), and the classic proliferation markers, including MKI67 and PCNA. Enrichment analyses indicated that CEP55 was predominantly associated with cell division, cell cycle activities and proliferation. Immune cell infiltration analysis by TIMER2.0 revealed that CEP55 expression was positively correlated with many kinds of infiltrating cells, such as Th2 cells and some CD4+ T cell subsets. The CEP55 expression was positively associated with increased MSI and TMB in various cancers. Our analyzation indicated that the CEP55 expression level in patients with complete remission (CR) or partial remission (PR) to anti-PDL1 therapy was significantly higher than patients with stable disease (SD) or progressive disease (PD) based on IMvigor210 cohort. We also used Gene Set Cancer Analysis (GSCA) to predict a serious of small molecule CEP55 targeted drugs, such as AZ628, SB52334, SB590885, A-770,041, AZD7762, Elesclomol, panobinostat, BRD-A94377914, and LRRK2-IN-1. Furthermore, the patients with high level of CEP55-posivie tumor epithelial cells had inferior overall survival in ccRCC according to single-cell analysis. Finally, our wet lab experiments verified that the CEP55-positive rate in ccRCC tissues (19/30, 63.3%) was significantly higher than that in renal adjacent tissues (10/30, 33.3%). The clinicopathologic analysis revealed that CEP55 protein level was significantly associated with tumor size (P = 0.044), histology grade (P < 0.001) and stage (P = 0.034). Our study indicated that CEP55 overexpression in most caner types was associated with poor prognosis. Notably, CEP55 was closely relevant to immune cell infiltration and impacted the response to immunotherapy and small molecule drugs against cancers.

Abstract A019: Identification and Genomic Analysis of miRNA-mRNA Binding Site SNPs in Hepatitis C Virus-Associated Hepatocellular Carcinoma

Abstract Hepatitis C virus (HCV) infection is a major risk factor for hepatocellular carcinoma (HCC), a leading cause of cancer-related deaths globally. MicroRNAs (miRNAs) play critical roles in gene regulation, and single nucleotide polymorphisms (SNPs) within miRNA binding sites can disrupt this regulation, potentially influencing HCC pathogenesis. The present study aims to identify and characterize SNPs within miRNA binding sites of genes associated with HCV- induced HCC (HCV-HCC). A comprehensive bioinformatics analysis workflow was devised. HCV-specific differentially expressed (DE) miRNAs and mRNAs were extracted from literature and databases. SNPs in the 3' UTRs of HCV-HCC genes were identified, and their impact on miRNA binding was predicted using PoymiRTS and TargetScan. RNAhybrid was used to estimate minimum folding energy (MFE) differences (ΔMFE) between mutant and wild types. Functional enrichment analysis was conducted to elucidate the biological processes affected by these SNPs. Our analysis revealed several significant alterations in miRNA-mRNA binding pairs due to SNPs within genes associated with HCV-HCC. For instance, among the downregulated genes in HCV-HCC, the SNP in CTSB (rs3088245; 193T&amp;gt;C) predicted to increase the stability of the miR-27b - CTSB duplex (ΔMFE: 1.4), suggesting enhanced binding and potentially improved repression of CTSB. Conversely, the SNP in EGR2 (rs61865883; 287 T&amp;gt;A) decreased the stability of the miR-140-5p - EGR2 mRNA interaction (ΔMFE: -1.9), suggesting a weaker binding affinity and potentially reduced repression of EGR2 expression. Considering the tumor suppressor role of EGR2, presence of this SNP is likely to reflect good prognosis. For the upregulated gene oncogene, XIAP, the SNP (rs6648556; 5892 T&amp;gt;C) is predicted to enhance the binding of miR- 200c (ΔMFE: 1.5), likely resulting in increased suppression of XIAP expression in patients carrying this SNP and improved prognosis. Our study emphasizes the importance of considering genetic variations in miRNA binding sites in the context of HCV-HCC. These SNPs may serve as genetic modifiers influencing disease susceptibility and progression. Further experimental studies are warranted to explore their potential as therapeutic agents and predictors of prognosis in HCV-HCC patients. Citation Format: Azeem M Butt, Anum Qamar, Shafiqa Siddique. Identification and Genomic Analysis of miRNA-mRNA Binding Site SNPs in Hepatitis C Virus-Associated Hepatocellular Carcinoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: RNAs as Drivers, Targets, and Therapeutics in Cancer; 2024 Nov 14-17; Bellevue, Washington. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(11_Suppl):Abstract nr A019.

Advantage of Log Odds of Metastatic Lymph Nodes After Curative-Intent Resection of Gallbladder Cancer.

Lymph node metastasis (LNM) is among the most important predictors of poor prognosis after surgery for gallbladder cancer (GBC). Traditionally, staging has been based on the raw count of LNM, with a high risk of understaging patients who undergo inadequate lymph node dissection (LND). The log odds of metastatic lymph nodes (LODDS) may represent an alternative staging approach to stratify patients more accurately after resection of GBC. In this cross-sectional study, patients who underwent curative-intent surgery with LND for GBC were identified from an international database. Two predictive models were built and compared, each integrating a different lymph nodes status indicator [i.e., American Joint Committee on Cancer (AJCC) and LODDS]. Among 199 patients, the median number of lymph nodes examined was 5 [interquartile range (IQR): 3.0, 8.0]; most patients had T1 (n = 26, 13.1%) or T2 (n = 97, 48.7%) disease, and a subset had LNM (n = 87, 44.0%). Multivariable Cox analysis demonstrated LODDS was an independent predictor of overall survival [hazard ratio (HR) 1.84, 95% confidence interval (CI) 1.5-2.3; p < 0.001]. The LODDS model demonstrated better performance compared with a traditional model that utilized the AJCC N category [concordance (C) index: 0.814 versus 0.763; p < 0.001]. Patients classified as high- versus low-risk based on LODDS had much worse overall survival (OS) (4.9% versus 83.7%, respectively; p < 0.001). The LODDS model performance remained high even among patients with inadequateLND (< 6 LN) (C index: 0.87). An online calculator was developed ( https://catalano-giovanni.shinyapps.io/LoddsGBC/ ). A novel prognostic model based on LODDS may overcome the inherent limitations of the current AJCC staging system, reducing understaging among patients with fewer than six total lymph nodes evaluated.

Diagnosis and prognosis prediction of gastric cancer by high-performance serum lipidome fingerprints.

Early detection is warranted to improve prognosis of gastric cancer (GC) but remains challenging. Liquid biopsy combined with machine learning will provide new insights into diagnostic strategies of GC. Lipid metabolism reprogramming plays a crucial role in the initiation and development of tumors. Here, we integrated the lipidomics data of three cohorts (n = 944) to develop the lipid metabolic landscape of GC. We further constructed the serum lipid metabolic signature (SLMS) by machine learning, which showed great performance in distinguishing GC patients from healthy donors. Notably, the SLMS also held high efficacy in the diagnosis of early-stage GC. Besides, by performing unsupervised consensus clustering analysis on the lipid metabolic matrix of patients with GC, we generated the gastric cancer prognostic subtypes (GCPSs) with significantly different overall survival. Furthermore, the lipid metabolic disturbance in GC tissues was demonstrated by multi-omics analysis, which showed partially consistent with that in GC serums. Collectively, this study revealed an innovative strategy of liquid biopsy for the diagnosis of GC on the basis of the serum lipid metabolic fingerprints.

Neutrophil-Lymphocyte Ratio as Predictor of Prognosis In Patient with Heart Failure with Reduced Ejection Fraction that Treated With Angiotensin Converting Enzyme Inhibitor or Angiotensin Receptor Neprilysin Inhibitor: A Prospective Cohort Study in Aceh,

Introduction: Heart failure (HF) and inflammation are related. Angiotensin Converting Enzyme Inhibitors (ACE-I) and Angiotensin Receptor Neprilysin Inhibitors (ARNI) can reduce inflammation by lowering neutrophil counts. The neutrophil-lymphocyte ratio (NLR) is a simple marker of inflammation. There is a lack of studies showing that NLR can predict prognosis in patients with HFrEF, and further investigation is needed.Objectives: Investigating whether NLR could be a prognostic indicator for mortality and rehospitalization in patients with heart failure and reduced ejection fraction receiving treatment with ACE-I or ARNI.Methods: A prospective observational cohort study with double-blind methods was conducted at Zainal Abidin General Hospital, Banda Aceh, Indonesia. Two groups, ACE-I and ARNI, each comprised 40 participants. Initially, blood sampling was performed to determine NLR. Participants were observed weekly for three months for prognosis outcomes (death and rehospitalization). After three months, the remaining participants were invited for re-evaluation of the NLR.Results: This study found that pre-treatment NLR had a hazard ratio (HR) of 1.454 (95% CI: 1.266-1.670, p &lt; 0.001). The risk ratio of abnormal pre-treatment NLR for rehospitalization was 6.56 in the ACE-I group and 8.33 in the ARNI group. Nevertheless, both ACE-I (p = 0.014) and ARNI (p = 0.032) decreased NLR levels.Conclusions: High NLR could be as a predictor of prognosis in patients with heart failure with a decrease in left ventricular ejection fraction that treated with ACE-I or ARNI, also ACE-I and ARNI could reduce NLR levels.

Comparison of multiple machine learning models for predicting prognosis of pancreatic ductal adenocarcinoma based on contrast-enhanced CT radiomics and clinical features

ObjectiveThe aim of this study was to evaluate the prognostic potential of combining clinical features and radiomics with multiple machine learning (ML) algorithms in pancreatic ductal adenocarcinoma (PDAC).MethodsA total of 116 patients with PDAC who met the eligibility criteria were randomly assigned to a training or validation cohort. Seven ML algorithms, including Supervised Principal Components, stepwise Cox, Random Survival Forest, CoxBoost, Least absolute shrinkage and selection operation (Lasso), Ridge, and Elastic network, were integrated into 43 algorithm combinations. Forty-three radiomics models were constructed separately using radiomics features extracted from arterial phase (AP), venous phase (VP), and combined arterial and venous phase (AP+VP) images. The concordance index (C-index) of each model was calculated. The model with the highest mean C-index was identified as the best model for calculating the radiomics score (Radscore). Univariate and multivariate Cox analyses were used to identify independent prognostic indicators and create a clinical model for prognosis prediction. The multivariable Cox regression was used to combine Radscore with clinical features to create a combined model. The efficacy of the model was evaluated using the C-index, calibration curves, and decision curve analysis (DCA).ResultsThe model based on the Lasso+StepCox[both] algorithm constructed using AP+VP radiomics features showed the best predictive ability among the 114 radiomics models. The C-indices of the model in the training and validation cohorts were 0.742 and 0.722, respectively. Based on the results of the univariate and multivariate Cox regression analyses, sex, Tumor-Node-Metastasis (TNM) stage, and systemic inflammation response index were included to build the clinical model. The combined model, incorporating three clinical factors and AP+VP-Radscore, achieved the highest C-indices of 0.764 and 0.746 in the training and validation cohorts, respectively. In terms of preoperative prognosis prediction for PDAC, the calibration curve and DCA showed that the combined model had a good consistency and greatest net benefit.ConclusionA combined model of clinical features and AP+VP-Radscore screened using multiple ML algorithms has an excellent ability to predict the prognosis of PDAC and may provide a noninvasive and effective method for clinical decision-making.

Identification of Molecular Subtypes and Prognostic Traits Based on Chromosomal Instability Phenotype-Related Genes in Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) exhibits significant molecular heterogeneity; however, previous studies have not fully explored its classification into distinct molecular subtypes. Here, we identified LUAD-significant chromosomal instability (CIN) phenotype genes (n = 24) using a TCGA-LUAD cohort (n = 592) and evaluated their ability to predict pathologic grade. Unsupervised clustering and principal component analysis revealed that LUAD patients could be classified into CIN phenotype-related subtypes (GroupLow, GroupModerate, and GroupHigh), each exhibiting distinct transcriptomic patterns. Notably, the GroupHigh showed significantly poor overall survival [OS; hazard ratio (HR) = 1.43, p-value &lt; 10−3] and disease-free survival (DFS; HR = 1.27, p-value &lt; 10−3). Univariate and multivariate analysis confirmed that its expression status was an independent prognostic predictor (p-value &lt; 10−3, HR = 2.18, 95% C.I = 1.26–3.76) of the clinical outcomes, outperforming pathologic grade (p-value &lt; 10−3, HR = 1.2, 95% C.I = 1.08–1.33). Moreover, analysis of surfactant metabolism-related genes revealed higher expression in the GroupLow, which was associated with a favorable prognosis. By integrating multiple independent cohorts (n = 779), we validated these findings and confirmed that CIN phenotype gene status serves as a critical prognostic marker in LUAD. Furthermore, genomic profiling showed that the GroupHigh exhibited frequent mutations in key genes such as KEAP1, LYST, SETD2, and TP53, with oncogenes in this group preferentially showing copy number gains. Our study highlights the significance of CIN phenotype gene status as a predictor of LUAD prognosis and its association with transcriptomic and genomic alterations, paving the way for further clinical validation and potential therapeutic interventions.

A novel platelets-related gene signature for predicting prognosis, immune features and drug sensitivity in gastric cancer

BackgroundPlatelets can dynamically regulate tumor development and progression. Nevertheless, research on the predictive value and specific roles of platelets in gastric cancer (GC) is limited. This research aims to establish a predictive platelets-related gene signature in GC with prognostic and therapeutic implications.MethodsWe downloaded the transcriptome data and clinical materials of GC patients (n=378) from The Cancer Genome Atlas (TCGA) database. Prognostic platelets-related genes screened by univariate Cox regression were included in Least Absolute Shrinkage and Selection Operator (LASSO) analysis to construct a risk model. Kaplan-Meier curves and receiver operating characteristic curves (ROCs) were performed in the TCGA cohort and three independent validation cohorts. A nomogram integrating the risk score and clinicopathological features was constructed. Functional enrichment and tumor microenvironment (TME) analyses were performed. Drug sensitivity prediction was conducted through The Cancer Therapeutics Response Portal (CTRP) database. Finally, the expression of ten signature genes was validated by quantitative real-time PCR (qRT-PCR).ResultsA ten-gene (SERPINE1, ANXA5, DGKQ, PTPN6, F5, DGKB, PCDH7, GNG11, APOA1, and TF) predictive risk model was finally constructed. Patients were categorized as high- or low-risk using median risk score as the threshold. The area under the ROC curve (AUC) values for the 1-, 2-, and 3-year overall survival (OS) in the training cohort were 0.670, 0.695, and 0.707, respectively. Survival analysis showed a better OS in low-risk patients in the training and validation cohorts. The AUCs of the nomogram for predicting 1-, 2-, and 3-year OS were 0.708, 0.763, and 0.742, respectively. TME analyses revealed a higher M2 macrophage infiltration and an immunosuppressive TME in the high-risk group. Furthermore, High-risk patients tended to be more sensitive to thalidomide, MK-0752, and BRD-K17060750.ConclusionThe novel platelets-related genes signature we identified could be used for prognosis and treatment prediction in GC.

Prognosis and diagnosis prediction of lung adenocarcinoma outcome based on a novel model anchored in circadian clock‐related genes

AbstractThe transition of JCCS from Springer to Wiley was rich in future prospects and was confirmed by the Journal reorganization launched in 2019. However, in spite of my recently renewed demand, the “B&amp;B” section of the Journal (Bits and Bytes not be confused with Bed and Breakfast, albeit…) did not fit into the Wiley Publishing categories and had to be dropped, to the great disappointment of many authors and our readership. This Editorial will fill the gap by discussing new published aspects on the topic of the connections existing between circadian cellular signaling and cancer prognosis, with the identification of eight genetically significant clock‐related genes in lung cancer.

A machine learning model for predicting worsening renal function using one-year time series data in patients with type 2 diabetes.

To prevent end-stage renal disease caused by diabetic kidney disease, we created a predictive model for high-risk patients using machine learning. The reference point was the time at which each patient's estimated glomerular filtration rate (eGFR) first fell below 60 mL/min/1.73 m2. The input period spanned the reference point to 1 year prior. The primary endpoint was a 50% decrease in eGFR from the mean of the input period over the 3 year evaluation period. We created predictive models for patients' primary endpoints using time series data of various variables over the input period. Among 2,533 total patients, 1,409 had reference points, 31 had records for their input and evaluation periods and had reached their primary endpoints, and 317 patients had not. The area under the curve (AUC) of the predictive model peaked (0.81) when the minimum eGFR, the difference between maximum and minimum eGFR, and both maximum and minimum urinary protein values were included in the features. The accuracy of prognosis prediction can be improved by considering the variable components of urinary protein and eGFR levels. This model will allow us to identify patients whose renal functions are relatively preserved with eGFR of more than 60 mL/min/1.73 m2 and are likely to benefit clinically from immediate treatment intensification.

Abstract A004: Full spectrum isolation technique-based dual circulating rare cells analysis to maximize early cancer diagnosis

Abstract Liquid biopsy (LB) is essential in precision medicine, enabling real-time tumor monitoring through repeated sampling. However, its diagnostic sensitivity and accuracy are limited by the low detection rate of circulating tumor cells (CTCs). For example, in breast cancer, the CTC detection rate exhibited only 20%, significantly limiting its clinical utility. This limitation arises since existing technologies selectively isolate specific phenotypes of CTCs, failing to capture others, such as mesenchymal CTCs, which are associated with poor prognosis. These mesenchymal CTCs often evade detection due to their lack of EpCAM expression and high deformability, making difficult to isolate them with traditional filter or EpCAM based methods. Additionally, conventional LB methods fail to provide critical insights into the tumor microenvironment (TME). To overcome these limitations, we developed a novel approach that improves LB diagnostic sensitivity and accuracy by (1) creating a technology to isolate all types of CTCs, regardless of marker expression or size, and (2) simultaneously analyzing CTCs and circulating cancer-associated fibroblasts (cCAFs). This dual approach addresses the challenges of low sensitivity and accuracy by enabling the isolation of a broader range of CTCs, including mesenchymal types, and by incorporating cCAF analysis. cCAFs, derived from the TME and found in the bloodstream, provide valuable insights into the TME that are hard to obtain through traditional methods. Analyzing cCAFs alongside CTCs offers a more comprehensive understanding of tumor biology and its microenvironment, leading to improved diagnostic accuracy, especially in early-stage cancer. In our study involving 55 breast cancer patients, we demonstrated that while CTCs were undetectable in 42.9% of early-stage patients, cCAFs were detected in all cases and were found to be 10 times more abundant than CTCs. Importantly, for the first time, this study uncovers that the diverse CAF subtypes previously identified in tissue samples also exist in the bloodstream, highlighting their critical role in providing TME-related insights. In addition, cCAF subtypes vary with hormonal receptor expression, and their marker analysis allows for 80.9% accurate classification of breast cancer subtypes. We applied this approach to early-stage breast cancer patients, for whom diagnosis through liquid biopsy is particularly difficult, and found that the combined analysis of CTCs and cCAFs achieved a 18% improvement in accuracy, with the AUC increasing from 0.6817 to 0.8009 compared to CTC analysis alone. This innovative approach significantly enhances LB by simultaneously isolating all CTC subtypes and incorporating cCAF analysis, thereby offering a more sensitive and accurate diagnostic tool. Beyond improving breast cancer diagnostics, this method has the potential to be applied to other tumor types and could also prove valuable for prognosis prediction and therapeutic response monitoring, ultimately leading to better patient outcomes and more effective cancer treatment strategies. Citation Format: Hyeongjung Woo, Simon A Joosse, Frederike Harten, Felix Hilpert, Klaus Pantel, Minseok S. Kim. Full spectrum isolation technique-based dual circulating rare cells analysis to maximize early cancer diagnosis [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A004.

Developing hypoxia and lactate metabolism-related molecular subtypes and prognostic signature for clear cell renal cell carcinoma through integrating machine learning.

The microenvironment of clear cell renal cell carcinoma (ccRCC) is characterized by hypoxia and increased lactate production. However, the impact of hypoxia and lactate metabolism on ccRCC remains incompletely understood. In this study, a new molecular subtype is developed based on hypoxia-related genes (HRGs) and lactate metabolism-related genes (LMRGs), aiming to create a tool that can predict the survival rate, immune microenvironment status, and responsiveness to treatment of ccRCC patients. We obtained RNA-seq data and clinical information of patients with ccRCC from TCGA and GEO. HRGs and LMRGs are sourced from the Molecular Signatures Database. Integrating 10 machine learning algorithms and 101 frameworks, we constructed a prognostic model related to hypoxia and lactate metabolism. Its accuracy and reliability are evaluated through constructing prognostic nomograms, drawing ROC curves, and validating with clinical datasets. Additionally, risk subgroups are evaluated based on functional enrichment, tumor mutational burden (TMB), immune cell infiltration degree, and immune checkpoint expression level. Finally, we evaluate the responsiveness of risk subgroups to immunotherapy and determine personalized drugs for specific risk subgroups. 85 valuable prognostic genes were screened out. Functional enrichment analysis shows that the group with high-risk hypoxia and lactate metabolism-related genes scores (HLMRGS) is mainly involved in the activation of immune-related activities, while the low risk HLMRGS group is more active in metabolic and tumor-related pathways. At the same time, differences in the cellular functional states in the tumor microenvironment between the high risk HLMRGS group and the low risk HLMRGS group were observed. Finally, potential drugs for specific risk subgroups were determined. We have developed a novel prognostic signature that integrates hypoxia and lactate metabolism. It is expected to become an effective tool for prognosis prediction, immunotherapy and personalized medicine of ccRCC.

Lactylation Modification as a Promoter of Bladder Cancer: Insights from Multi-Omics Analysis

Bladder cancer (BLAC) is a malignant tumor with high morbidity and mortality. The establishment of a prognostic model for BLAC is of great significance for clinical prognosis prediction and treatment guidance. Lactylation modification is a newly discovered post-transcriptional modification of proteins, which is closely related to the occurrence and development of tumors. Multiple omics data of BLAC were obtained from the GEO database and TCGA database. The Lasso algorithm was used to establish a prognostic model related to lactylation modification, and its predictive ability was tested with a validation cohort. Functional enrichment analysis, tumor microenvironment analysis, and treatment response evaluation were performed on the high- and low-risk groups. Single-cell and spatial transcriptome data were used to analyze the distribution characteristics of model genes and their changes during epithelial carcinogenesis. A prognostic model consisting of 12 genes was constructed. The survival rate of the high-risk group was significantly lower than that of the low-risk group. The multiple ROC curve showed that the prediction efficiency of the model was higher than that of the traditional clinical tumor grading. Functional enrichment analysis showed that glycolysis and hypoxia pathways were significantly upregulated in the high-risk group. The high-risk group was more sensitive to most first-line chemotherapy drugs, while the low-risk group had a better response to immunotherapy. Single-cell sequencing analysis revealed the dynamic changes of model genes during the transition of epithelial cells to squamous-differentiated cells. Spatial transcriptome analysis showed the spatial distribution characteristics of the model genes. The lactylation-related models have a satisfactory predictive ability and the potential to guide the clinical treatment of BLAC. This model has significant biological implications at the single-cell level as well as at the spatial level.

Abstract 4125719: Efficacy of Prodromal Symptom Assessment in Prognostic Stratification of Acute Myocarditis

Introduction: The outcomes of acute myocarditis are heterogeneous and can lead to clinically significant consequences. However, the patient characteristics associated with adverse outcomes at presentation are not fully understood. Objection: To investigate the association between the presence or absence of prodromal symptoms and in-hospital mortality in patients admitted with acute myocarditis. Methods: We analyzed multicenter data registry (Tokyo CCU Network Database, comprising 76 institutions in the Tokyo metropolitan area). The study included 261 cases with acute myocarditis from January 2017 to December 2020, among which 260 required hospitalization from the emergency department. Patients were grouped based on the presence or absence of prodromal symptoms prior to hospitalization. Prodromal symptoms include the following: (a) chest pain; (b) dyspnea; (c) fever; (d) ful-like symptoms; and (e) others.The primary outcome was in-hospital death. Results: Among the 261 patients with acute myocarditis, 187 patients (71.6%) exhibited prodromal symptoms, and 74 patients (28.3%) did not. The primary outcome occurred in 19 patients (25.6%) without prodromal symptoms and in 20 patients (10.6%) with prodromal symptoms (hazard ratio, 2.87; 95% CI, 1.34 to 6.13; P=0.004, Figure 1A). A multivariable-adjusted Cox regression analysis confirmed that the absence of prodromal symptoms was an independent predictor of poor prognosis, even after adjusting for age, sex and the history of diabetes and hypertension. Notably, this trend was pronounced among patients with Killip classes 2-4, revealing higher in-hospital mortality rate in the patients without prodromal symptoms compared to those with prodromal symptoms (48.6% vs 11.7%; P &lt; 0.001, Figure 1B). Conclusions: This study demonstrates that the absence of prodromal symptoms in patients with acute myocarditis is an independent predictor of poor prognosis, especially for patients presented with Killip classes 2-4.