Predictor Of Prognosis In Patients Research Articles

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.

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

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

Trajectory mapping of renal clear cell carcinoma transcriptomes identifies stage-independent predictors of favorable prognosis

Abstract Objectives The prognosis of clear cell renal cell carcinoma (ccRCC) is typically based on clinical stage, but it can vary for some patients. Transcriptomic analysis is vital for understanding ccRCC progression, though its correlation with the clinical stage in predicting prognosis is uncertain. We aim to employ trajectory inference to study ccRCC’s molecular progression and identify potential new markers for judging disease progression and prognosis. Methods Using a trajectory inference approach, we characterize the molecular progression profile of ccRCC based on transcriptome profiling. Additional pathway activity, immune response, and miRNA profiling scoring were integrated to identify possible drivers of trajectory progression. Results Scoring based on the trajectory demonstrates a significant improvement in patient prognosis prediction and identifies 10 risk factors in patients with low-grade tumors, and nine protective factors in patients with high-grade tumors. Mechanistically, we demonstrate an association between solute light carrier transporters are associated with ccRCC progression, with SLC7A5 expression being validated through immunohistochemistry to increase in metastatic patients. Conclusions Trajectory analysis of ccRCC transcriptomes can be used to model the molecular progression of disease and may assist in ccRCC prognosis. SLC7A5 is aberrantly expressed in ccRCC and may be a risk factor for poor prognosis.

Stage-Specific Alteration and Prognostic Relationship of Serum Fumarate Hydratase Autoantibodies in Gastric Cancer.

The relationship between energy production and cancer is attracting attention. This study aimed to investigate the clinicopathological significance of fumarate hydratase (FH), a tricarboxylic acid cycle enzyme, in gastric cancer using autoantibodies as biomarkers. The study analyzed 116 patients who underwent gastric cancer surgery and 96 healthy controls. Preoperative serum FH autoantibody (s-FH-Ab) titers were analyzed using an immunosorbent assay with an amplified luminescent proximity homogeneous assay. Receiver operating characteristic analysis was used to determine the cutoff s-FH-Ab titer. Clinicopathological factors and prognosis were compared between the high and low s-FH-Ab groups. The s-FH-Ab levels were significantly higher in the gastric cancer group than in the control group (p = 0.01). Levels were elevated even in patients with stage I gastric cancer compared with healthy controls (p = 0.02). A low s-FH-Ab level was significantly associated with distant metastasis (p = 0.01), peritoneal dissemination (p < 0.05), and poor overall survival (p < 0.01). Multivariate analysis revealed that low s-FH-Ab levels were an independent risk factor for poor prognosis (p < 0.01). Therefore, s-FH-Ab levels may be a useful biomarker for early diagnosis and the prediction of prognosis in patients with gastric cancer.

Elucidating the impact of parthanatos-related microRNAs on the tumoral immune microenvironment and clinical outcome in low-grade gliomas

Parthanatos, a cell death mechanism triggered by PARP-1 activation, is implicated in oncogenic processes, yet their role in low-grade gliomas (LGG) remains poorly understood. This research investigates Parthanatos-related miRNAs' prognostic and immunomodulatory potential, alongside their influence on therapeutic outcomes in LGGs. Comprehensive miRNA and mRNA profiles of LGG patients were extracted from TCGA and CGGA databases, integrating clinical parameters to identify Parthanatos-associated miRNAs. IHC data validated the expression levels of Parthanatos-related genes in glioma versus normal brain tissues. Protein–protein interaction networks and Spearman correlation analysis facilitated the identification of key miRNAs. Parthanatos-related miRNA indices (PMI) were screened using Lasso and assessed for their accuracy in predicting prognosis, comparing their associated potential molecular functions and heterogeneity of the immune microenvironment. Drug sensitivity was assessed between different groups and optimal therapeutic agents were predicted. Validate the expression levels of key miRNAs by qPCR. Ninety-one miRNAs significantly associated with Parthanatos were screened, through which a PMI prognosis model of nine miRNAs was constructed. The PMI score was able to independently predict the prognosis of patients with LGG, and the nomogram constructed based on the PMI provided a practical tool for clinical prediction of patient prognosis. The proportion of immune response was lower in patients in the high-risk group, and there were significant differences in drug sensitivity between different risk classes, while drugs such as Fasudil were identified as the most promising therapeutic agents for patients in the high-risk group. Our findings highlight the critical role of Parthanatos-associated miRNAs in the progression and treatment of LGG, offering novel insights into their prognostic value and therapeutic potential.

Surgical outcome and risk scoring to predict survival after hepatic resection for hepatocellular carcinoma with portal vein tumor thrombosis.

The hepatocellular carcinoma (HCC) with portal vein tumor thrombosis (PVTT) is classified as the advanced stage (BCLC stage C) with extremely poor prognosis, and in current guidelines is recommended for systemic therapy. This study aimed to evaluate the surgical outcomes and long-term prognosis after hepatic resection (HR) for patients who have HCC combined with PVTT. We retrospectively analyzed 332 patients who underwent HR for HCC with PVTT at ten tertiary referral hospitals in South Korea. The median overall and recurrence-free survival after HR were 32.4 and 8.6 months, while the 1-, 3-, and 5-year overall survival rates were 75%, 48%, and 39%, respectively. In multivariate analysis, tumor number, tumor size, AFP, PIVKA-II, neutrophil-to-lymphocyte ratio, and albumin-bilirubin (ALBI) grade were significant prognostic factors. The risk scoring was developed using these seven factors-tumor, inflammation and hepatic function (TIF), to predict patient prognosis. The prognosis of the patients was well stratified according to the scores (log-rank test, p < 0.001). HR for patients who have HCC combined with PVTT provided favorable survival outcomes. The risk scoring was useful in predicting prognosis, and determining the appropriate treatment strategy for those patients who have HCC with PVTT.

Cancer-associated fibroblast-derived gene signature discriminates distinct prognoses by integrated single-cell and bulk RNA-seq analyses in breast cancer.

Cancer-associated fibroblasts (CAFs) are one of the most predominant cellular subpopulations in the tumor stroma and play an integral role in cancer occurrence and progression. However, the prognostic role of CAFs in breast cancer remains poorly understood. We identified a number of CAF-related biomarkers in breast cancer by combining single-cell and bulk RNA-seq analyses. Based on univariate Cox regression as well as Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, a novel CAF-associated prognostic model was developed. Breast cancer patients were grouped according to the median risk score and further analyzed for outcome, clinical characteristic, pathway activity, genomic feature, immune landscape, and drug sensitivity. A total of 341 CAF-related biomarkers were identified from single-cell and bulk RNA-seq analyses. We eventually screened eight candidate prognostic genes, including CERCAM, EMP1, SDC1, PRKG1, XG, TNN, WLS, and PDLIM4, and constructed the novel CAF-related prognostic model. Grouped by the median risk score, high-risk patients showed a significantly worse prognosis and exhibited distinct pathway activities such as uncontrolled cell cycle progression, angiogenesis, and activation of glycolysis. In addition, the combined risk score and tumor mutation burden significantly improved the ability to predict patient prognosis. Importantly, patients in the high-risk group had a higher infiltration of M2 macrophages and a lower infiltration of CD8+ T cells and activated NK cells. Finally, we calculated the IC50 for a range of anticancer drugs and personalized the treatment regimen for each patient. Integrating single-cell and bulk RNA-seq analyses, we identified a list of compositive CAF-associated biomarkers and developed a novel CAF-related prognostic model for breast cancer. This robust CAF-derived gene signature acts as an excellent predictor of patient outcomes and treatment responses in breast cancer.

Abstract PO1-14-03: Discovery of proliferation essential gene signature and ACTL6A as potential biomarker for predicting prognosis and neoadjuvant therapy response in triple-positive breast cancer

Abstract Background: Based on the expression of estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2), a special subtype of breast cancer which expresses all three receptors can be distinguished, namely triple-positive breast cancer (TPBC). TPBC patients has a higher risk of recurrence and lower survival rate compared to other luminal breast cancers. However, there are few studies on predicting molecules of prognosis and treatment response for TPBC. Methods: Proliferation essential genes (PEGs) were identified using CRISPR-Cas9 dataset of DepMap. The expression profiles and clinical data of TPBC were obtained from the TCGA database, GEO datasets and our center cohort. To develop a TPBC-PEG prognostic signature, cox regression and Lasso (Least absolute shrinkage and selection operator) regression analysis were applied. Functional analysis was performed using Gene Set Enrichment analysis. Finally, the relationship between candidate genes and neoadjuvant therapy (NAT) sensitivity was explored using real-time qPCR (RT-qPCR) and immunohistochemistry (IHC) based on clinical samples. Results: Among 900 TPBC-PEGs, 437 genes showed significant differential expression between TPBC and normal tissues. Subsequently, we identified 3 prognostic PEGs (ACTL6A, CCT2, TARS) and used them to construct a PEG signature. Patients with high PEG signature scores exhibited worse overall survival and lower sensitivity to NAT compared to those with low PEG signature scores. RT-qPCR indicated that in patients who lacked sensitivity to NAT, ACTL6A and CCT2 were significantly upregulated. The IHC results showed that ACTL6A protein was highly expressed in the NAT-insensitive group and non-pathological complete response patients. Conclusion: In this study, we developed an efficient PEG prognostic model that can predict the outcome for TPBC. Furthermore, we found that the expression of ACTL6A is associated with neoadjuvant therapy sensitivity in TPBC patients and can serve as an important factor in predicting patient prognosis and drug sensitivity. Figure 3. Construction and validation of a PEG signature for TPBC patients. Figure 6. The predictive value of ACTL6A on NAT efficacy and its correlation with clinical characteristics. Citation Format: Xiaofen Li, Wenfen Fu, Shiping Luo, Jie Zhang, Qingshui Wang, Chuangui Song. Discovery of proliferation essential gene signature and ACTL6A as potential biomarker for predicting prognosis and neoadjuvant therapy response in triple-positive breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-14-03.

Abstract PO2-25-04: Anti-TIGIT upregulate CD226 on CD8+T cell as a potential therapeutic manner in HER2 positive breast cancer

Abstract Purpose: HER2 positive breast cancer subtype shows high malignancy, aggressive invasion, frequent recurrence, and low efficacy to PD-1 inhibitor though enriches in immune microenvironment. TIGIT is an immune checkpoint which expresses on T/NK cells and participates in immune evasion of cancer cells. Here, we investigated the effect of anti-HER2 and anti-TIGIT combination regarding to HER2 breast cancer treatment and tumor microenvironment, further exploring mechanisms behind the combination utilization. Methods: We re-analyzed the untreated human and mouse HER2 breast cancer scRNA-seq dataset to explore proportions of unique cell clusters in tumor immune microenvironment and communicated signals between immune cells and tumor cells. Mouse HER2 non-sensitive model and 27 non-pCR patients were utilized to investigate the variation of TIGIT and its ligand CD112/CD155 in HER2 non-sensitive population. Relevance between CD112/CD155 and prognosis was verified by 200 patients tissue microarray IHC. We validated the efficacy of combination of anti-TIGIT and anti-Neu and delineated scRNA-seq landscape via mouse model. Multilayer analysis including CellChat and AuCell were used to investigate difference of immune network between ctrl group, monotherapy and combination. In vitro and in vivo studies were used to verify the efficacy of anti-TIGIT and pivotal clusters. Results: We found that CD8+T cells occupied major proportion in TILs in both human and mouse HER2 breast cancer tumor immune microenvironment. CD8+T cells showed TIGIT signal to malignant epithelial cells instead of PD-1 signal. Increased CD8+TIGIT+T cells were related to poor prognosis in 200-patients cohort. In HER2 non-sensitive mouse model, significantly increased CD8+TIGIT+T cells were found in HER2 non-sensitive group. Multiplex IHF of 27 non-pCR patients also confirmed that CD8+TIGIT+T cells increased after treatment. CD112, ligand of TIGIT on malignant cells, demonstrated higher expression after anti-HER2 treatment in HER2 non-sensitive population and negatively correlated with prognosis. In scRNA-seq about control group, monotherapy and combination group, we defined malignant cells as sensitive subclone, Neu resistant subclone and others according to cell ratio variation in ctrl, monotherapy and combination group. CD112 upregulated in all subclones in monotherapy group. GSVA analysis indicated Neu resistant subclone had highest IFN-γ response among subclones. In Neu resistant subclone, combination group showed highest expression of MHC-I genes and upregulated IFN-γresponse, suggested the restored antigen presenting ability of Neu resistant subclone. Higher CD226, MHC-I and IFN-γ signals between malignant epithelial cells and CD8+T cells was found in combination group via CellChat. AuCell manifested upregulated T cell activation and IFN-γ production pathways in combination group. Public dataset showed favorable results. Increased CD226 expression and promoted cytotoxicity on CD8+T cells can only be induced by anti-TIGIT therapy in vitro and in vivo. 200-patients cohort indicated the predictive values of CD8+CD226+T cells in HER2 breast cancer. Conclusion: In conclusion, engaged immune checkpoint TIGIT shades malignant cells from CD8+T mediated immune surveillance in HER2 breast cancer. Disruption of TIGIT inhibit tumor growth in vivo by immune elimination of malignant cells. Besides, CD8+CD226+T cells induced by combined therapy can predict patient prognosis. Citation Format: Liyi Zhang, Qi Zhang, Zehao Wang, Zhibo Shao, Jingyan Xue, Yayun Chi, Bingqiu Xiu, Jiong Wu. Anti-TIGIT upregulate CD226 on CD8+T cell as a potential therapeutic manner in HER2 positive breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-25-04.

Spatial distance between tumor and lymphocyte can predict the survival of patients with resectable lung adenocarcinoma

Background and objectiveSpatial interaction between tumor-infiltrating lymphocytes (TILs) and tumor cells is valuable in predicting the effectiveness of immune response and prognosis amongst patients with lung adenocarcinoma (LUAD). Recent evidence suggests that the spatial distance between tumor cells and lymphocytes also influences the immune responses, but the distance analysis based on Hematoxylin and Eosin (H&E) -stained whole-slide images (WSIs) remains insufficient. To address this issue, we aim to explore the relationship between distance and prognosis prediction of patients with LUAD in this study. MethodsWe recruited patients with resectable LUAD from three independent cohorts in this multi-center study. We proposed a simple but effective deep learning-driven workflow to automatically segment different cell types in the tumor region using the HoVer-Net model, and quantified the spatial distance (DIST) between tumor cells and lymphocytes based on H&E-stained WSIs. The association of DIST with disease-free survival (DFS) was explored in the discovery set (D1, n = 276) and the two validation sets (V1, n = 139; V2, n = 115). ResultsIn multivariable analysis, the low DIST group was associated with significantly better DFS in the discovery set (D1, HR, 0.61; 95 % CI, 0.40–0.94; p = 0.027) and the two validation sets (V1, HR, 0.54; 95 % CI, 0.32–0.91; p = 0.022; V2, HR, 0.44; 95 % CI, 0.24–0.81; p = 0.009). By integrating the DIST with clinicopathological factors, the integrated model (full model) had better discrimination for DFS in the discovery set (C-index, D1, 0.745 vs. 0.723) and the two validation sets (V1, 0.621 vs. 0.596; V2, 0.671 vs. 0.650). Furthermore, the computerized DIST was associated with immune phenotypes such as immune-desert and inflamed phenotypes. ConclusionsThe integration of DIST with clinicopathological factors could improve the stratification performance of patients with resectable LUAD, was beneficial for the prognosis prediction of LUAD patients, and was also expected to assist physicians in individualized treatment.

Estimated pulse wave velocity predicts mortality in patients with heart failure with preserved ejection fraction

AIMEstimated pulse wave velocity (ePWV), a newly established arterial stiffness (AS) parameter, predicts development of cardiovascular disease (CVD) and death in general population or patients with CVD risk factors. However, whether ePWV is associated with adverse outcome in heart failure with preserved ejection fraction (HFpEF) patients remains unknown. Our study aimed to evaluate the prognostic value of ePWV on clinical outcomes in HFpEF. Methods and resultsWe analyzed HFpEF participants from the Americas in the Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist (TOPCAT) trial with available baseline data (n = 1764). Cox proportional hazard model was used to explore the prognostic value of ePWV on the long-term clinical outcomes (all-cause mortality, cardiovascular mortality, all-cause hospitalization and heart failure hospitalization). Each ePWV increase by 1 m/s increased the risk for all-cause death by 16% (HR:1.16; 95% CI:1.10-1.23; P<0.001) and CVD mortality by 13% (HR:1.13; 95% CI:1.04-1.21; P=0.002) after adjusting for confounders. Patients were then grouped into 4 quartiles of ePWV. Our study indicated that the highest ePWV quartile (ePWV ≥12.806 m/s) was associated with increased risk of all-cause mortality (HR, 1.96; 95% CI, 1.43-2.69; P<0.001) and CVD mortality (HR, 1.72; 95% CI, 1.16-2.56; P=0.008) after adjusting for potential confounders. ConclusionThese results suggested ePWV is independently associated with increased all-cause mortality and CVD mortality in HFpEF patients, indicating ePWV is an appropriate predictor of prognosis in patients with HFpEF.

Cerebral autoregulation: A reliable predictor of prognosis in patients receiving intravenous thrombolysis.

To investigate the characteristics of dynamic cerebral autoregulation (dCA) after intravenous thrombolysis (IVT) and assess the relationship between dCA and prognosis. Patients with unilateral acute ischemic stroke receiving IVT were prospectively enrolled; those who did not were selected as controls. All patients underwent dCA measurements, by quantifying the phase difference (PD) and gain, at 1-3 and 7-10 days after stroke onset. Simultaneously, two dCA-based nomogram models were established to verify the predictive value of dCA for patients with mild-to-moderate stroke. Finally, 202 patients who received IVT and 238 who did not were included. IVT was positively correlated with higher PD on days 1-3 and 7-10 after stroke onset. PD values in both sides at 1-3 days after stroke onset and in the affected side at 7-10 days after onset were independent predictors of unfavorable outcomes in patients who received IVT. Additionally, in patients with mild-to-moderate stroke who received IVT, the dCA-based nomogram models significantly improved the risk predictive ability for 3-month unfavorable outcomes. IVT has a positive effect on dCA in patients with acute stroke; furthermore, dCA may be useful to predict the prognosis of patients with IVT.

AI-Derived Body Composition Parameters as Prognostic Factors in Patients with HCC Undergoing TACE: Results from a Multicenter Study

Background & AimsBody composition assessment (BCA) parameters have recently been identified as relevant prognostic factors for patients with hepatocellular carcinoma (HCC). Here we aimed to investigate the role of BCA parameters for prognosis prediction in patients with HCC undergoing transarterial chemoembolization (TACE). MethodsThis retrospective multicenter study included a total of 754 treatment-naïve patients with HCC who underwent TACE at six tertiary care centers between 2010–2020. Fully automated artificial intelligence-based quantitative 3D volumetry of abdominal cavity tissue composition was performed to assess skeletal muscle volume (SM), total adipose tissue (TAT), intra- and intermuscular adipose tissue (IMAT), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) on pre-intervention computed tomography scans. BCA parameters were normalized to the slice number of the abdominal cavity. We assessed the influence of BCA parameters on median overall survival (OS) and performed multivariate analysis including established estimates of survival. ResultsUnivariate survival analysis revealed that impaired median OS was predicted by low SM volume (p < 0.001), high TAT volume (p = 0.013), and high SAT volume (p = 0.006). In multivariate survival analysis, SM remained an independent prognostic factor (p = 0.039), while TAT and SAT volumes no longer showed predictive ability. ConclusionsSkeletal muscle volume is an independent prognostic factor for survival prediction. Thus, the integration of SM into novel scoring systems could potentially improve survival prediction and clinical decision-making. Fully automated approaches are needed to foster the implementation of this imaging biomarker into daily routine. Impact and implicationsBody composition assessment parameters, especially skeletal muscle mass, have been identified as relevant prognostic factors for many diseases and treatments. In this study skeletal muscle volume has been identified as an independent prognostic factor for patients with HCC undergoing TACE. Therefore, skeletal muscle mass as a metaparameter could play a role as an opportunistic biomarker in holistic patient assessment and be integrated into decision support systems. Workflow integration with AI evaluation is essential for automated body composition assessment quantification output enabling broad availability in multidisciplinary case discussions.

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.

Quantitative Evaluation of Right Ventricular Workload Based on the Stroke Work Index in Patients after Right Ventricular Outflow Tract Reconstruction.

The evaluation of right ventricular workload is sometimes complicated in patients after right ventricular outflow tract reconstruction (RVOTR) because both stenotic and regurgitation lesions are involved. In this study, we modified the right ventricular stroke work index (RVSWI) and evaluated the relationship between the modified RVSWI (mRVSWI) and patient prognosis after RVOTR.We enrolled 69 patients who underwent RVOTR (the RVOTR group), including those who needed early reoperation (early reoperation subgroup) and those who did not (follow-up subgroup), and 13 age-matched control participants (control group). Based on the catheterization results 1year after RVOTR, we compared the mRVSWI between these groups. Additionally, we evaluated the influence of the mRVSWI on the reoperation avoidance rate and survival.The mRVSWI in the RVOTR group was significantly greater than that in the control group (17.7 ± 8.6 vs. 11.0 ± 2.7g·m/m2, p = 0.008). The mRVSWI in the early reoperation subgroup was significantly greater than that in the follow-up subgroup (32.5 ± 11.1 vs. 15.8 ± 6.0g·m/m2, p < 0.0001). In the follow-up subgroup, patients with an mRVSWI higher than the upper limit of normal (16.4g·m/m2) had a greater rate of reoperation than did the other patients (p = 0.0013). One patient died suddenly, and her mRVSWI was consistently high throughout her life.We established the mRVSWI as an index that integrates the pressure and volume load on the right ventricle. Our results indicate the utility of the mRVSWI for predicting patient prognosis after RVOTR.

Expression of 10 circulating cytokines/chemokines in HBV-related liver disease

BackgroundCytokines/chemokines play essential roles in the occurrence and progression of hepatitis B virus (HBV) infection. This study aimed to observe the expression patterns of 10 related cytokines/chemokines in the serum of healthy individuals, self-limited patients and HBV-infected patients at different stages of disease (chronic hepatitis B (CHB), liver cirrhosis (LC), hepatocellular dysplastic nodules (DNs) and hepatocellular carcinoma (HCC)) and to analyze the relationships of these cytokines/chemokines with disease progression.MethodsThe levels of six cytokines (FGF-2, IFN-α2, IL-4, IL-6, IL-10 and VEGF-A) and four chemokines (GRO-α, IL-8, IP-10 and MCP-1) were quantified using Luminex multiplex technology.ResultsThere were no significant differences in the expression of the 10 cytokines/chemokines between healthy individuals and self-limited patients. The levels of IL-4, IL-6, and IL-8 increased significantly in the CHB and LC groups. IL-10 was highly expressed in the HCC group. The level of IP-10 was significantly greater in all liver disease groups (CHB, LC, DN and HCC) than in the HI and SL-HBV groups, while the level of GRO was significantly lower in all liver disease groups than in the HI and SL-HBV groups. The levels of the 10 cytokines/chemokines were not significantly different between the preoperative group and the two-day postoperative group. Significant increases in the levels of IL-4, VEGF-A and IL-8 and significant decreases in those of IL-10 and GRO-α were observed 3 months after surgery. Correlation analysis revealed that most of the cytokines/chemokines with significant correlation differences were positively correlated before and after HCC surgery.ConclusionOur results highlight the fluctuating status of specific cytokines in HBV infection-related disease progression. It is speculated that these cytokines may be used as serum markers to monitor dynamic changes during the progression of HBV-related liver disease and to predict patient prognosis.

Heart failure prognosis prediction : Let’s start with the MDL-HFP model

Heart failure, as a critical symptom or terminal stage of assorted heart diseases, is a world-class public health problem. Establishing a prognostic model can help identify high dangerous patients, save their lives promptly, and reduce medical burden. Although integrating structured indicators and unstructured text for complementary information has been proven effective in disease prediction tasks, there are still certain limitations. Firstly, the processing of single branch modes is easily overlooked, which can affect the final fusion result. Secondly, simple fusion will lose complementary information between modalities, limiting the network’s learning ability. Thirdly, incomplete interpretability can affect the practical application and development of the model. To overcome these challenges, this paper proposes an MDL-HFP multimodal model for predicting patient prognosis using the MIMIC-III public database. Firstly, the ADASYN algorithm is used to handle the imbalance of data categories. Then, the proposed improved Deep&Cross Network is used for automatic feature selection to encode structured sparse features, and implicit graph structure information is introduced to encode unstructured clinical notes based on the HR-BGCN model. Finally, the information of the two modalities is fused through a cross-modal dynamic interaction layer. By comparing multiple advanced multimodal deep learning models, the model’s effectiveness is verified, with an average F1 score of 90.42% and an average accuracy of 90.70%. The model proposed in this paper can accurately classify the readmission status of patients, thereby assisting doctors in making judgments and improving the patient’s prognosis.Further visual analysis demonstrates the usability of the model, providing a comprehensive explanation for clinical decision-making.

Prognostic Significance of NQO1 Expression in Non-neoplastic Esophageal Squamous Epithelium for Patients With Esophageal Cancer.

NAD(P)H dehydrogenase [quinone] 1 (NQO1), an antioxidant enzyme, confers resistance to anticancer agents. NQO1 C609T is a single-nucleotide polymorphism associated with reduced protein expression in the non-neoplastic esophageal squamous epithelium (ESE). This study aimed to investigate immunohistochemical NQO1 expression in non-neoplastic ESE and to elucidate its prognostic significance in patients with esophageal squamous cell carcinoma (ESCC) undergoing neoadjuvant therapy followed by esophagectomy. NQO1 expression in non-neoplastic ESE was determined in surgical specimens from 83 patients with ESCC using immunohistochemistry. The association between NQO1 expression and clinicopathological factors, and the prognostic significance of NQO1 expression for relapse-free survival (RFS) were statistically evaluated. Patients with complete loss or weak NQO1 expression and patients with moderate or strong NQO1 expression were classified into the NQO1-negative (n=29) and NQO1-positive (n=54) groups, respectively. The downstaging of T classification status after neoadjuvant therapy was significantly more frequent in the NQO1-negative group than in the NQO1-positive group (59% vs. 33%; p=0.036). The NQO1-negative group had significantly more favorable RFS than the NQO1-positive group (p=0.035). Multivariate survival analysis demonstrated that NQO1 negative expression had a favorable prognostic impact on RFS (HR=0.332; 95%CI=0.136-0.812; p=0.016). Immunohistochemical evaluation of NQO1 expression in non-neoplastic ESE has clinical utility for predicting patient prognosis after neoadjuvant therapy followed by esophagectomy and might be helpful for selecting candidates for adjuvant therapy to treat ESCC.

Identification of a novel prognostic cuproptosis-associated LncRNA signature for predicting prognosis and immunotherapy response in patients with esophageal cancer

Nowadays, effective prognostic models for esophageal cancer (ESCA) are still lacking. Long noncoding RNAs (lncRNAs) are commonly utilized as indicators for diagnosing cancer and forecasting patient outcomes. Cuproptosis is regulated by multiple genes and is crucial to the progression of ESCA. However, it is not yet clear what role the cuproptosis-associated lncRNAs (CuALs) play in ESCA. To tackle this problem, a prognostic signature incorporating three CuALs was created. This signature was constructed by the use of the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression. Subsequently, the signature effectively stratified ESCA samples into a high-risk group and a low-risk group. Those in the low-risk group demonstrated extended overall survival (OS), as well as increased infiltration of T cells, macrophages, and NK cells, suggesting a potentially enhanced response to immunotherapy. The ROC curve analysis demonstrated that this prognostic signature outperformed conventional clinical factors in predicting patient prognosis (AUC = 0.708). K-M survival analysis and correlation analysis identified UGDH-AS1 (a CuAL) as a protective factor positively associated with patient prognosis. The results of RT-qPCR and wound healing assays indicated that UGDH-AS1 is overexpressed in ESCA and could inhibit cancer cell migration. In general, the prognostic signature of CuALs demonstrated a robust capability in forecasting the immune environment and patient prognosis, highlighting its potential as a tool for enhancing personalized treatment strategies in ESCA.

Identification and validation of an anoikis-related genes signature for prognostic implication in papillary thyroid cancer.

Thyroid cancer, notably papillary thyroid cancer (PTC), is a global health concern with increasing incidence. Anoikis, a regulator of programmed cell death, is pivotal in normal physiology and, when dysregulated, can drive cancer progression and metastasis. This study explored the impact of anoikis on PTC prognosis. Analyzing data from GEO, TCGA, and GeneCards, we identified a prognostic signature consisting of six anoikis-related genes (ARGs): EZH2, PRKCQ, CD36, INHBB, TDGF1, and MMP9. This signature independently predicted patient outcomes, with high-risk scores associated with worse prognoses. A robust predictive ability was confirmed via ROC analysis, and a nomogram achieved a C-index of 0.712. Differences in immune infiltration levels were observed between high- and low-risk groups. Importantly, the high-risk group displayed reduced drug sensitivity and poor responses to immunotherapy. This research provides insights into anoikis in PTC, offering a novel ARG signature for predicting patient prognosis and guiding personalized treatment strategies.