Predictor Of Prognosis In Patients Research Articles

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

ObjectiveEstimated pulse wave velocity (ePWV), a newly established arterial stiffness (AS) parameter, predicts the development of cardiovascular disease (CVD) and death in the general population or in patients with CVD risk factors. However, whether ePWV is associated with adverse outcomes 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 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.

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 the 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.

Phase angle: A robust predictor of malnutrition and poor prognosis in gastrointestinal cancer

ObjectivesThe aim of this study was to investigate the relationship among phase angle (PA), malnutrition, and prognosis in patients with gastrointestinal cancer. MethodsIn total, 870 patients with gastrointestinal cancer were enrolled. Kaplan-Meier curves and Cox proportional hazards models were used to evaluate the association between PA and survival risk. Restricted cubic spline regression was used for flexibility analysis to explore sex-specific associations between PA and survival. Logistic regression was used to assess the relationships among PA, malnutrition, and cachexia. ResultsLow PA was closely associated with poor physical conditions, diminished quality of life, and malnutrition. Patients with low PA had a significantly worse prognosis than those with high PA (60.6% versus 72.8%; log-rank P < 0.001). PA was suitable for the prognostic assessment of patients with advanced-stage tumors. Regardless of sex, patients with lower PA showed significantly poorer survival rates. Cox proportional hazards models identified PA as an independent predictor of prognosis in patients with gastrointestinal cancer (hazard ratio (HR)=0.534; 95% confidence interval (CI)=0.409–0.696, P < 0.001). Subgroup analysis indicated that a high PA was an independent risk factor affecting the prognoses of patients with esophageal, liver, and intrahepatic bile duct cancers. Interestingly, variations in PA had a more significant prognostic effect on survival in men than in women. The logistic regression model confirmed that PA is a valuable indicator for assessing malnutrition and cachexia in patients with gastrointestinal cancer. Among all body composition indicators, PA demonstrated the highest accuracy for prognostic prediction. ConclusionsPA was identified as a robust predictor of malnutrition and poor prognosis in patients with gastrointestinal cancer.

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.

Prognostic performance of MRI LI-RADS version 2018 features and clinical-pathological factors in alpha-fetoprotein-negative hepatocellular carcinoma.

To evaluate the role of the magnetic resonance imaging (MRI) Liver Imaging Reporting and Data System (LI-RADS) version 2018 features and clinical-pathological factors for predicting the prognosis of alpha-fetoprotein (AFP)-negative (≤ 20ng/ml) hepatocellular carcinoma (HCC) patients, and to compare with other traditional staging systems. We retrospectively enrolled 169 patients with AFP-negative HCC who received preoperative MRI and hepatectomy between January 2015 and August 2020 (derivation dataset:validation dataset = 118:51). A prognostic model was constructed using the risk factors identified via Cox regression analysis. Predictive performance and discrimination capability were evaluated and compared with those of two traditional staging systems. Six risk factors, namely the LI-RADS category, blood products in mass, microvascular invasion, tumor size, cirrhosis, and albumin-bilirubin grade, were associated with recurrence-free survival. The prognostic model constructed using these factors achieved C-index of 0.705 and 0.674 in the derivation and validation datasets, respectively. Furthermore, the model performed better in predicting patient prognosis than traditional staging systems. The model effectively stratified patients with AFP-negative HCC into high- and low-risk groups with significantly different outcomes (p < 0.05). A prognostic model integrating the LI-RADS category, blood products in mass, microvascular invasion, tumor size, cirrhosis, and albumin-bilirubin grade may serve as a valuable tool for refining risk stratification in patients with AFP-negative HCC.

Multi-omics analysis of immune-related microbiome and prognostic model in head and neck squamous cell carcinoma

ObjectivesThe aim of our study is to explore the transcriptional and microbial characteristics of head and neck cancer’s immune phenotypes using a multi-omics approach.Materials and methodsEmploying TCGA data, we analyzed head and neck squamous cell carcinoma (HNSCC) immune cells with CIBERSORT and identified differentially expressed genes using DESeq2. Microbial profiles, obtained from the TCMA database, were analyzed using LEfSe algorithm to identify differential microbes in immune cell infiltration (ICI) subgroups. Random Forest algorithm and deep neural network (DNN) were employed to select microbial features and developed a prognosis model.ResultsWe categorized HNSCC into three immune subtypes, finding ICI-2 with the worst prognosis and distinct microbial diversity. Our immune-related microbiome (IRM) model outperformed the TNM staging model in predicting survival, linking higher IRM model scores with poorer prognosis, and demonstrating clinical utility over TNM staging. Patients categorized as low-risk by the IRM model showed higher sensitivity to cisplatin and sorafenib treatments.ConclusionsThis study offers a comprehensive exploration of the ICI landscape in HNSCC. We provide a detailed scenario of immune regulation in HNSCC and report a correlation between differing ICI patterns, intratumor microbiome, and prognosis. This research aids in identifying prime candidates for optimizing treatment strategies in HNSCC.Clinical relevanceThis study revealed the microbial signatures associated with immunophenotyping of HNSCC and further found the microbial signatures associated with prognosis. The prognostic model based on IRM microbes is helpful for early prediction of patient prognosis and assisting clinical decision-making.

Monitoring hepatocellular carcinoma using tumor content in circulating cell-free DNA.

To evaluate the utility of tumor content in circulating cell-free DNA (ccfDNA) for monitoring hepatocellular carcinoma (HCC) throughout its natural history. We included 67 hepatitis B virus (HBV)-related HCC patients, of whom 17 had paired pre- and post-treatment samples, and 90 controls. Additionally, in a prospective cohort with HBV surface antigen-positive participants recruited in 2012 and followed up biannually with blood sample collections until 2019, we included 270 repeated samples before diagnosis from 63 participants who later developed HCC (pre-HCC samples). Shallow whole-genome sequencing and the ichorCNA method were used to analyze genome-wide copy number and tumor content in ccfDNA. High tumor content was associated with advanced tumor stage (P < 0.001) and a poor survival after HCC diagnosis (HR=12.35; 95% confidence interval [CI]=1.413-107.9; P = 0.023). Tumor content turned negative after surgery (P = 0.027), while remained positive after transarterial chemoembolization treatment (P = 0.578). In non-HCC samples, the mean tumor content (±SD) was 0.011 (±0.007) and had a specificity of 97.8% (95%CI=92.2%-99.7%). In pre-HCC samples, tumor content increased from 0.014 in 4 years before diagnosis to 0.026 in 1 year before diagnosis. The sensitivity of tumor content in detecting HCC increased from 22.7% (95%CI=11.5%-37.8%) within one year before diagnosis to 30.4% (95%CI=13.2%-52.9%) at BCLC stage 0/A, 81.8% (95%CI=59.7%-94.8%) at stage B, and 95.5% (95%CI=77.2%-99.9%) at stage C. The tumor content in ccfDNA is correlated with tumor burden and may help in monitoring HCC one year earlier than clinical diagnosis and in predicting patient prognosis.

Rim Enhancement on Contrast-Enhanced CT as a Predictor of Prognosis in Patients with Pancreatic Ductal Adenocarcinoma.

This study investigated the utility of imaging features, such as rim enhancement on contrast-enhanced CT (CECT), in predicting the prognosis of pancreatic ductal adenocarcinoma (PDAC). This retrospective study included 158 patients (84 men; mean age, 68 years) with pathologically confirmed PDAC. The following imaging features were evaluated on CECT by two radiologists: tumor size, tumor attenuation, and the presence of rim enhancement. Cox proportional hazards analysis was performed to identify the imaging and clinicopathological features for predicting disease-free survival (DFS) and overall survival (OS). Pathological features were compared with the presence of rim enhancement. Among the 158 patients, 106 (67%) underwent curative surgery (surgery group) and 52 (33%) received conservative treatment (non-surgery group). Rim enhancement was observed more frequently in the non-surgery group than in the surgery group (44% vs. 20%; p < 0.001). Rim enhancement showed significant associations with shorter DFS and OS in the surgery group (hazard ratios (HRs), 3.03 and 2.99; p < 0.001 and p = 0.003, respectively), whereas tumor size showed significant associations with shorter OS (HR per 1 mm increase, 1.08; p < 0.001). PDACs with rim enhancement showed significant associations with higher histological tumor grades (p < 0.001). PDAC with rim enhancement on CECT could predict poorer prognosis and more aggressive tumor grades.

Stress-induced phosphoprotein 1: how does this co-chaperone influence the metastasis steps?

In several cancer types, metastasis is associated with poor prognosis, survival, and quality of life, representing a life risk more significant than the primary tumor itself. Metastasis is a multi-step process that spreads tumor cells from primary sites to surrounding or distant organs, originating secondary tumors. The interconnected steps that drive metastasis depend of several capabilities that enable cells to detach from the primary tumor, acquire motility and migrate through the basal membrane; invade and spread through the vascular system, and finally settle and originate a new tumor. Recently, stress-induced phosphoprotein 1 (STIP1) has emerged as a protein capable of driving tumor cells through these metastasis steps by mediating several biological processes and signaling pathways. This protein is mainly known for its function as a co-chaperone, acting as a scaffold for the interaction of its client heat-shock proteins Hsp70/90 chaperones; however, it is also known that STIP1 can act independently of chaperones to activate downstream phosphorylation pathways. The over-expression of STIP1 has been reported across various cancer types, identifying it as a potential biomarker for predicting patient prognosis and monitoring the progression of metastasis. Here, we present a discussion on how this co-chaperone mediates the initial steps of metastasis (cell adhesion loss, epithelial-to-mesenchymal transition, and angiogenesis), highlighting the biological mechanisms in which STIP1 plays a vital role, also presenting an overview of the current knowledge regarding its clinical relevance.

Assessment of transabdominal fat volumes as a predictor of prognosis in patients undergoing transcatheter aortic valve replacement.

Transcatheter aortic valve replacement (TAVR) has emerged as a well-established treatment option for eligible patients with severe aortic stenosis. This study aimed to investigate the correlation between abdominal fat tissue volumes, measured using computed tomography (CT), and all-cause mortality in patients undergoing TAVR. The study included 258 consecutive patients who underwent TAVR at a single center between September 2017 and November 2020. During the preoperative preparation, CT scans were used to perform a semi-quantitative measurement of abdominal fat components. Body mass index (BMI) for each participant was calculated. The relationship between fat parameters and overall survival was determined using multivariable Cox proportional hazards models. Participants had a mean age of 76.8 ± 7.8years, of whom 32.9% were male. The median follow-up period was 12months, during which 38 patients (14.7%) died. Both the survivor and non-survivor groups showed comparable risk factors. Regarding transabdominal fat volume parameters, deceased individuals exhibited significantly lower values. However, no significant differences were observed in BMI and transabdominal area measurements. Among transabdominal fat parameters, only subcutaneous fat volume [adjusted Hazard Ratio (aHR) = 0.83, p = 0.045] and total fat volume (TFV) [aHR = 0.82, p = 0.007] were identified as significant predictors of reduced all-cause mortality. Furthermore, TFV demonstrated the highest discriminative performance with a threshold of ≤ 9.1 L (AUC = 0.751, p < 0.001, sensitivity 71.1%, specificity 70.9%). Preoperative CT-based abdominal fat volume parameters, particularly TFV, can serve as potential predictors of survival in patients undergoing TAVR.