Powerful Prognostic Predictor Research Articles

The role of cholesterol-modified prognostic nutritional index in nutritional status assessment and predicting survival after liver resection for hepatocellular carcinoma.

Malnutrition, which is often underestimated in patients with hepatocellular carcinoma (HCC), has a proven adverse effect on survival rates. The purpose of this study was to verify the effectiveness of the cholesterol-modified prognostic nutritional index (CPNI) in determining the nutritional status and predicting overall survival (OS) and recurrence-free survival (RFS) in patients with HCC by comparing it with several other nutritional indicators. This retrospective single-center study enrolled 1450 consecutive HCC patients who underwent curative liver resection from January 2015 to November 2019. We evaluated the prognostic significance of several nutritional indicators, including CPNI, the controlling nutritional status (CONUT), the nutritional risk index (NRI), and the prognostic nutritional index (PNI), by applying time-dependent receiver operating characteristic (ROC) curves, Kaplan-Meier survival analysis, and Cox proportional hazards regression analysis. Among several objective nutrition evaluations (including CPNI, CONUT, NRI, and PNI), CPNI demonstrated the greatest prognostic predictive power for predicting OS. Meanwhile, CPNI demonstrated marginally higher accuracy in predicting RFS compared to PNI, and significantly outperformed CONUT and NRI. Univariate and multivariate analyses suggested that CPNI was an independent risk factor for the OS and RFS of patients with HCC undergoing curative liver resection. In most subgroups, malnutrition as identified by CPNI demonstrates strong stratification ability in predicting both OS and RFS. CPNI serves as an accurate and stable instrument for evaluating nutritional status and forecasting survival outcomes in HCC patients following liver resection, which has the potential to markedly influence clinical decision-making processes and the management of patient care.

Spatial distribution pattern of immune cells is associated with patient prognosis in colorectal cancer

BackgroundThe spatial context of tumor-infiltrating immune cells (TIICs) is important in predicting colorectal cancer (CRC) patients’ clinical outcomes. However, the prognostic value of the TIIC spatial distribution is unknown. Thus, we aimed to investigate the association between TIICs in situ and patient prognosis in a large CRC sample.MethodsWe implemented multiplex immunohistochemistry staining technology in 190 CRC samples to quantify 14 TIIC subgroups in situ. To delineate the spatial relationship of TIICs to tumor cells, tissue slides were segmented into tumor cell and microenvironment compartments based on image recognition technology, and the distance between immune and tumor cells was calculated by implementing the computational pipeline phenoptr.ResultsMPO+ neutrophils and CD68+IDO1+ tumor-associated macrophages (TAMs) were enriched in the epithelial compartment, and myeloid lineage cells were located nearest to tumor cells. Except for CD68+CD163+ TAMs, other cells were all positively associated with favorable prognosis. The prognostic predictive power of TIICs was highly related to their distance to tumor cells. Unsupervised clustering analysis divided colorectal cancer into three subtypes with distinct prognostic outcomes, and correlation analysis revealed the synergy among B cells, CD68+IDO1+TAMs, and T lineage cells in producing an effective immune response.ConclusionsOur study suggests that the integration of spatial localization with TIIC abundance is important for comprehensive prognostic assessment.

Exploration of the prognostic value of methylation regulators related to m5C in papillary thyroid carcinoma.

The incidence of papillary thyroid carcinoma (PTC) has increased significantly in recent years, and for patients with metastatic and recurrent PTC, the options for treatment currently available are insufficient. To date, the exact molecular mechanism underlying PTC is still not fully understood. 5-Methylcytosine (m5C) RNA methylation is associated with the prognosis of a variety of tumors. However, the molecular mechanisms and biomarkers associated with m5C in the diagnosis, treatment, and prognosis of this disease have not been fully elucidated. Ten m5C regulators with significantly different expression levels were included in this study. Immune infiltration analysis revealed significant negative correlations between most of these regulators and regulatory T cells. TRDMT1, NSUN5, and NSUN6 had high weights and strong correlations in the protein-protein interaction network. Using gene ontology, Kyoto Encyclopedia of Genes and Genomes, and gene set enrichment analysis, 1489 differentially expressed genes were screened from The Cancer Genome Atlas messenger RNA matrix, indicating that these differentially expressed genes were significantly enriched in various pathways and functions related to cancers. Four m5C regulators, NSUN2, NSUN4, NSUN6, and DNMT3B, were screened as prognostic markers by least absolute shrinkage and selection operator regression analysis, and NSUN2 and NSUN6 were identified as risk factors for poor prognosis. We found that the prognostic prediction model constructed using the m5C regulators NSUN2, NSUN4, NSUN6, and DNMT3B showed good prognostic prediction ability and diagnostic ability. This model was applied to predict the survival probability of patients with PTC, the prediction ability of 5-year survival was the best. The multi-factor prognostic prediction model combined with the tumor node metastasis stage and risk score grouping showed better prognostic predictive power.

Prognostic utility of paraspinal muscle index in elderly patients with community-acquired pneumonia.

This study investigated the associations between paraspinal muscle measurements on chest computed tomography and clinical outcomes of elderly patients with community-acquired pneumonia (CAP). This single-center, retrospective, observational study analyzed elderly patients (≥65 years) with CAP hospitalized through an emergency department between March 2020 and December 2022. We collected their baseline characteristics and laboratory data at the time of admission. The paraspinal muscle index and attenuation were calculated at the level of the 12th thoracic vertebra using chest computed tomography taken within 48 hours before or after admission. Univariable and multivariable logistic regression analyses were conducted to evaluate the association between paraspinal muscle measurements and 28-day mortality. Receiver operating characteristic (ROC) curve and area under the curve (AUC) analyses were used to evaluate the prognostic predictive power. Of the 338 enrolled patients, 60 (17.8%) died within 28 days after admission. A high paraspinal muscle index was associated with low 28-day mortality in elderly patients with CAP (adjusted odds ratio, 0.994; 95% confidence interval, 0.992-0.997). The area under the ROC curve for the muscle index was 0.75, which outperformed the pneumonia severity index and the CURB-65 (confusion, urea, respiratory rate, blood pressure, age ≥65 years) metric, both of which showed an AUC of 0.64 in predicting mortality. A high paraspinal muscle index was associated with low 28-day mortality in patients aged 65 years or older with CAP.

Revolutionizing T3-4N0-2M0 gastric cancer staging with an innovative pathologic N classification system

BackgroundThe current pathologic N (pN) classification exhibits limitations in the prognostic stratification of patients with pT3-4N0-2M0 gastric cancer (GC). Therefore, this study aimed to develop and validate a new lymph nodal staging method based on the number of examined lymph nodes (ELNs) and lymph node ratio (LNR). MethodsData from 7883 patients with pT3-4N0-2M0 GC were collected from the Surveillance, Epidemiology, and End Results (SEER) database and Zhejiang Cancer Provincial Hospital. Optimal cutoff values for ELNs and LNR were determined using X-tile software. Kaplan-Meier methods, Log-rank tests, and Cox regression analyses were employed in this study. Patients were categorized into 3 new pN stages: new pN0 (pN0 with ELNs of >16), new pN1 (pN0 with ELNs of ≤16 or pN1-2 with LNR of ≤0.15), and new pN2 (pN1-2 with LNR of >0.15). The prognostic predictive power of both current and new pN staging was evaluated using the Akaike information criterion (AIC), Bayesian information criterion, concordance index (C-index), and receiver operating characteristic curve. ResultsThe new pN classification exhibited excellent performance in Kaplan-Meier survival analysis. After adjusting for confounding factors, the new pN staging emerged as an independent prognostic indicator in patients with GC. In the SEER cohort, the new pN staging demonstrated enhanced prognostic prediction accuracy over the American Joint Committee on Cancer pN staging (AIC: 75578.85 vs 75755.06; C-index: 0.642 vs 0.630; P < .001). Similar findings were validated in the Chinese cohort. ConclusionThis study developed and validated an improved pN classification for patients with pT3-4N0-2M0 GC. Surgeons should consider ELNs and LNR when assessing postoperative prognosis in patients with GC.

Abstract 5873: ERα coordinates with squalene synthase to confer statin resistance in lung cancer

Abstract Statins are widely used to treat hypercholesterolemia by inhibiting hydroxymethylglutaryl coenzyme A reductase (HMGCR). Accumulating evidence suggests that statins also have protective effects against cancer recurrence. However, the role of statins in preventing lung cancer and biomarkers for predicting statin treatment response remains lacking. This study analyzes molecular features that dictate statin sensitivity in lung cancer by correlating large-scale statin sensitivity data and basal gene expression in a collection of 125 lung cancer patients. We identified a gene signature associated with lovastatin sensitivity and showed that gene expression in mesenchymal molecular features, the cholesterol biosynthesis pathway, and estrogen receptor alpha (ERα) signaling were key determinants of lovastatin sensitivity in lung cancer cells. To this end, we further discovered that overexpression of a committed step enzyme in cholesterol biosynthesis farnesyl-diphosphate farnesyltransferase 1 (FDFT1), also known as squalene synthase (SQS), is capable of activating ERα and is associated with increased statin tolerance. In clinical specimens, overexpression of SQS positively correlated with nuclear expression of ERα in lung cancer patients, and the combination of SQS and ERα is a powerful prognostic predictor for lung cancer. Activation of the SQS-ERα signaling axis and epithelial-to-mesenchymal transition (EMT) status represents two important features in determining statin sensitivity in lung cancer. Keywords: Statin, cholesterol biosynthesis, squalene synthase, estrogen receptor α, Epithelial-mesenchymal transition Citation Format: Yi-Fang Yang. ERα coordinates with squalene synthase to confer statin resistance in lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5873.

Establishment and verification of the first prognostic nomograms in locally advanced thyroid cancer based on the analysis of clinical and follow-up information on 2396 patients

Background and objectivesThe purpose of this research was to develop and validate the first prognostic nomograms for 3-, 5-, and 10-year cancer-specific survival (CSS) and overall survival (OS) in patients diagnosed with locally advanced thyroid cancer (LATC) by evaluating independent predictors of prognosis in a population of LATC patients. MethodsDemographics, clinicopathologic characteristics, treatment, and follow-up of 2396 LATC patients in the surveillance, epidemiology, and end results database from 2004 to 2015 were retrospectively analyzed and compared with patients with LATC according to staging. We randomized all LATC patients into training and validation groups in a 7:3 ratio. Cox regression analyses helped us to derive independent prognostic factors for LATC patients. According to these results, we established and validated the first prognostic nomograms and risk stratification. ResultsIn our research, the clinical information of LATC patients was compared and significant differences were found in the relevant variables including CSS and OS (P < 0.05), with CSS of 82.0 % and 49.0 %, and OS of 70.6 % and 40.0 %, respectively. Cox regression analyses showed that age at diagnosis, tumor diameter, presence of DM, extrathyroidal extension sites, histological type, thyroidectomy scope, radiotherapy status, and chronological sequence of radiotherapy and surgery were observably correlated with CSS in LATC patients, and in addition to the above factors, gender, marital status, and chemotherapy status were also observably correlated with OS in LATC patients. The prognostic predictive power of the above factors is visualized by the Kaplan-Meier survival curve. The concordance index of nomograms for CSS and OS were 0.933, 0.925, and 0.926 (CSS), 0.918, 0.909, and 0.906 (OS), respectively, and the time-dependent receiver operating characteristic curve, area under curve, calibration curve and decision curve analysis curve indicate that the nomograms have good discriminatory ability, accuracy and clinical applicability in both the training and validation groups. ConclusionsIn these findings, we drawed a conclusion that there were significant differences in clinical information between patients with T4a and T4b LATC, and we established and validated the first prognostic nomograms and risk stratification of CSS and OS for LATC patients at 3, 5, and 10 years, which will help clinicians to individualize their postoperative treatment and individualized follow-up.

Exploring the relationship between metabolism and immune microenvironment in osteosarcoma based on metabolic pathways

BackgroundMetabolic remodeling and changes in tumor immune microenvironment (TIME) in osteosarcoma are important factors affecting prognosis and treatment. However, the relationship between metabolism and TIME needs to be further explored.MethodsRNA-Seq data and clinical information of 84 patients with osteosarcoma from the TARGET database and an independent cohort from the GEO database were included in this study. The activity of seven metabolic super-pathways and immune infiltration levels were inferred in osteosarcoma patients. Metabolism-related genes (MRGs) were identified and different metabolic clusters and MRG-related gene clusters were identified using unsupervised clustering. Then the TIME differences between the different clusters were compared. In addition, an MRGs-based risk model was constructed and the role of a key risk gene, ST3GAL4, in osteosarcoma cells was explored using molecular biological experiments.ResultsThis study revealed four key metabolic pathways in osteosarcoma, with vitamin and cofactor metabolism being the most relevant to prognosis and to TIME. Two metabolic pathway-related clusters (C1 and C2) were identified, with some differences in immune activating cell infiltration between the two clusters, and C2 was more likely to respond to two chemotherapeutic agents than C1. Three MRG-related gene clusters (GC1-3) were also identified, with significant differences in prognosis among the three clusters. GC2 and GC3 had higher immune cell infiltration than GC1. GC3 is most likely to respond to immune checkpoint blockade and to three commonly used clinical drugs. A metabolism-related risk model was developed and validated. The risk model has strong prognostic predictive power and the low-risk group has a higher level of immune infiltration than the high-risk group. Knockdown of ST3GAL4 significantly inhibited proliferation, migration, invasion and glycolysis of osteosarcoma cells and inhibited the M2 polarization of macrophages.ConclusionThe metabolism of vitamins and cofactors is an important prognostic regulator of TIME in osteosarcoma, MRG-related gene clusters can well reflect changes in osteosarcoma TIME and predict chemotherapy and immunotherapy response. The metabolism-related risk model may serve as a useful prognostic predictor. ST3GAL4 plays a critical role in the progression, glycolysis, and TIME of osteosarcoma cells.

Triglyceride-glucose body mass index predicts prognosis in patients with ST-elevation myocardial infarction

Triglyceride glycemic-body mass index (TyG-BMI) is a simple and reliable surrogate for insulin resistance (IR). However, it is still unclear if TyG-BMI has any predictive value in patients having percutaneous coronary intervention (PCI) for ST-segment elevation myocardial infarction (STEMI). The purpose of this study was to examine the TyG-BMI index's prognostic significance and predictive power in patients with STEMI. The study comprised a total of 2648 consecutive STEMI patients who underwent PCI. The primary endpoint was the occurrence of major adverse cardiovascular events (MACE), defined as the combination of all-cause death, nonfatal myocardial infarction, nonfatal stroke, and coronary revascularization. The TyG-BMI index was formulated as ln [fasting triglycerides (mg/dL) × fasting plasma glucose (mg/dL)/2] × BMI. 193 patients in all experienced MACE over a median follow-up of 14.7 months. There was a statistically significant difference between the Kaplan–Meier survival curves for the TyG-BMI index tertiles (log-rank test, p = 0.019) for the cumulative incidence of MACE. The adjusted HRs for the incidence of MACE in the middle and highest quartiles of the TyG-BMI index compared with the lowest quartile were 1.37 (95% CI 0.92, 2.03) and 1.53 (95% CI 1.02, 2.29), respectively, in the fully adjusted Cox regression model. At six months, one year, and three years, the TyG-BMI area under the curve (AUC) for predicting MACE was 0.691, 0.666, and 0.637, respectively. Additionally, adding the TyG-BMI index to the risk prediction model enhanced outcome prediction. In STEMI patients undergoing PCI, TyG-BMI was independently linked to MACE. TyG-BMI could be a simple and solid way to assess MACE risk and prognosis.

CD8 + T cell-based molecular subtypes with heterogeneous immune landscapes and clinical significance in acute myeloid leukemia.

Acute myeloid leukemia (AML) is a heterogeneous hematological malignancy. Although high-dose chemotherapy is the primary treatment option, it cannot cure the disease, and new approaches need to be developed. The tumor microenvironment (TME) plays a crucial role in tumor biology and immunotherapy. CD8 + T cells are the main anti-tumor immune effector cells, and it is essential to understand their relationship with the TME and the clinicopathological characteristics of AML. In this study, we conducted a systematic analysis of CD8 + T cell infiltration through multi-omics data and identified molecular subtypes with significant differences in CD8 + T cell infiltration and prognosis. We aimed to provide a comprehensive evaluation of the pathological factors affecting the prognosis of AML patients and to offer theoretical support for the precise treatment of AML. Our results indicate that CD8 + T cell infiltration is accompanied by immunosuppression, and that there are two molecular subtypes, with the C2 subtype having a significantly worse prognosis than the C1 subtype, as well as less CD8 + T cell infiltration. We developed a signature to distinguish molecular subtypes using multiple machine learning algorithms and validated the prognostic predictive power of molecular subtypes in nine AML cohorts including 2059 AML patients. Our findings suggest that there are different immunosuppressive characteristics between the two subtypes. The C1 subtype has up-regulated expression of immune checkpoints such as CTLA4, PD-1, LAG3, and TIGITD, while the C2 subtype infiltrates more immunosuppressive cells such as Tregs and M2 macrophages. The C1 subtype is more responsive to anti-PD-1 immunotherapy and induction chemotherapy, as well as having higher immune and cancer-promoting variant-related pathway activity. Patients with the C2 subtype had a higher FLT3 mutation rate, higher WBC counts, and a higher percentage of blasts, as indicated by increased activity of signaling pathways involved in energy metabolism and cell proliferation. Analysis of data from ex vivo AML cell drug assays has identified a group of drugs that differ in therapeutic sensitivity between molecular subtypes. Our results suggest that the molecular subtypes we constructed have potential application value in the prognosis evaluation and treatment guidance of AML patients.

Identification of efferocytosis-related subtypes in gliomas and elucidating their characteristics and clinical significance.

Introduction: Gliomas, the most prevalent tumors of the central nervous system, are known for their aggressive nature and poor prognosis. The heterogeneity among gliomas leads to varying responses to the same treatments, even among similar glioma types. In our study, we efferocytosis-related subtypes and explored their characteristics in terms of immune landscape, intercellular communication, and metabolic processes, ultimately elucidating their potential clinical implications. Methods and Results: We first identified efferocytosis-related subtypes in Bulk RNA-seq using the NMF algorithm. We then preliminarily demonstrated the correlation of these subtypes with efferocytosis by examining enrichment scores of cell death pathways, macrophage infiltration, and the expression of immune ligands. Our analysis of single-cell RNA-seq data further supported the association of these subtypes with efferocytosis. Through enrichment analysis, we found that efferocytosis-related subtypes differ from other types of gliomas in terms of immune landscape, intercellular communication, and substance metabolism. Moreover, we found that the efferocytosis-related classification is a prognostic factor with robust predictive performance by calculating the AUC values. We also found that efferocytosis-related subtypes, when compared with other gliomas in drug sensitivity, survival, and TIDE scores, show a clear link to the effectiveness of chemotherapy, radiotherapy, and immunotherapy in glioma patients. Discussion: We identified efferocytosis-related subtypes in gliomas by analyzing the expression of 137 efferocytosis-associated genes, exploring their characteristics in immune landscape, intercellular communication, metabolic processes, and genomic variations. Moreover, we discovered that the classification of efferocytosis-related subtypes has a strong prognostic predictive power and holds potential significance in guiding clinical treatment.

Clinical Application of the IPSS-M Prognostic Score in Myelodysplastic Syndrome

Objectives: Patients with myelodysplastic syndrome (MDS) currently get therapy in accordance with their prognosis scores. The newest MDS risk-stratified score, the IPSS-M, isn't still being extensively used in clinical practice and is based on clinical and molecular data. Comparing the IPSS-M score to the current IPSS-R score will enable us to assess the clinical relevance of the IPSS-M score, especially for low-risk MDS patients. Methods: In order to determine the IPSS-R and IPSS-M scores, we gathered clinical, cytogenetic, and molecular information from 210 MDS patients who were diagnosed between 2016 and 2023 and followed up until April 2023. Harrell's c index and Kaplan-Meier survival analysis were used to examine the impact of the two scores on overall survival (OS).The lower-risk group included patients with IPSS-R≤3.5 and IPSS-M≤0, whereas the higher-risk group included the remaining patients. Analysis was done on the differences in gene mutations between the two risk groups based on the two scoring standards, as well as the impact of IPSS-M reclassification on the choice of therapeutic treatments. Results: At least one genetic mutation was identified in 77.1% of patients. The genes ASXL1 (21.4%), TET2 (19.0%), TP53 (14.8%), and DNMT3A (13.3%) have the highest frequency of mutations. According to IPSS-R score, the higher-risk group's TP53 gene mutation rate was significantly higher than that of the lower-risk group; According to IPSS-M score, the higher-risk group had more instances of TP53 and ASXL1 gene mutations than the lower-risk group did, while the lower-risk group had a relatively higher incidence of SF3B1 gene mutations than the higher-risk group did. The difference in survival between IPSS-M groups was more prominent than that between IPSS-R groups under Kaplan-Meier survival analysis, which was used to assess the prognostic prediction power of IPSS-R and IPSS-M. When t 50 months, the Harrell's c index of OS predicted by IPSS-M exceeded that of IPSS-R (IPSS-R vs IPSS-M c-index: 0.710vs0.747, 0.692vs0.707, 0.705vs0.726, 0.752vs0.759, 0.743vs0.773, t=10, 20, 30, 40, 50 months), demonstrating that IPSS-M enhanced the outcome prediction.87 patients (41.4%) were re-stratified by IPSS-M, with 65 (30.9%) being upgraded and 22 (10.5%) being degraded. 64 patients (30.5%) who had changes in their IPSS-M scores qualified for reduced therapy, whereas 48 (22.9%) qualified for intensive treatment, including 32 patients who were reclassified as being in the lower risk category based on their IPSS-R scores. Conclusions: The research shows that IPSS-M offers a more thorough prognostic evaluation than IPSS-R. Certain patients' treatment modalities may need to be changed as a result of modifying IPSS-M in clinical settings, especially those who were previously classified as low-risk.

Lymph node ratio and hematological parameters predict relapse-free survival in patients with high grade rectal neuroendocrine neoplasms after radical resection: a multicenter prognostic study

BackgroundThe prognostic nutritional index (PNI), alkaline phosphatase (ALP), and lymph node ratio (LNR) are reportedly related to prognosis. The aim of this study was to elucidate the clinical importance of the LNR and hematological parameters in patients with high grade rectal neuroendocrine neoplasms (HG-RNENs) who were undergoing radical resection.MethodsWe reviewed the medical records of patients with HG-RNENs from 17 large-scale medical centers in China (January 1, 2010–April 30, 2022). A nomogram was constructed by using a proportional hazard model. Bootstrap method was used to draw calibration plots to validate the reproducibility of the model. Concordance index (C-Index), decision curve analysis (DCA), and time-dependent area under the receiver operating characteristic curve (TD-AUC) analysis were used to compare the prognostic predictive power of the new model with American Joint Committee on Cancer (AJCC) TNM staging and European Neuroendocrine Tumor Society (ENETS) TNM staging.ResultsA total of 85 patients with HG-RNENs were enrolled in this study. In the 45 patients with HG-RNENs who underwent radical resection, PNI ≤ 49.13 (HR: 3.997, 95% CI: 1.379–11.581, P = 0.011), ALP > 100.0 U/L (HR: 3.051, 95% CI: 1.011–9.205, P = 0.048), and LNR > 0.40 (HR: 6.639, 95% CI: 2.224–19.817, P = 0.0007) were independent predictors of relapse-free survival. The calibration plots suggested that the nomogram constructed based on the three aforementioned factors had good reproducibility. The novel nomogram revealed a C-index superior to AJCC TNM staging (0.782 vs 0.712) and ENETS TNM staging (0.782 vs 0.657). Also, the new model performed better compared to AJCC TNM staging and ENETS TNM staging in DCA and TD-AUC analyses.ConclusionsLNR, ALP, and PNI were independent prognostic factors in patients with HG-RNENs after radical resection, and the combined indicator had better predictive efficacy compared with AJCC TNM staging and ENETS TNM staging.

Development and external validation of a nomogram predicting overall survival for Gastric adenocarcinoma patients with radical gastrectomy

Purpose The aim of this study was to develop and externally validate a nomogram to accurately predict the overall survival (OS) of patients with gastric adenocarcinoma who underwent radical gastrectomy. Materials and Methods A total of 3492 patients with gastric adenocarcinoma who underwent radical gastrectomy from 2012 to 2017 were included as the training cohort. Survival analysis was performed via Kaplan Meier method and log-rank test. Independent postoperative prognostic factors in patients with gastric adenocarcinoma were analyzed using univariate and multifactorial COX analysis methods. The prognosis nomogram was established in the training cohort and verified externally in the Surveillance, Epidemiology and End Results (SEER) database. Results According to the univariate and multifactorial COX analyses, metastatic lymph node ratio (MLNR) and five other independent prognostic factors (age at surgery, type of gastrectomy, tumor size, T stage, and pathological grade) were included in the prognostic nomogram. The nomogram had better prognostic predictive ability than the American Joint Committee on Cancer (AJCC) TNM staging in both the training (C-index: 0.736 VS. 0.668) and external validation cohort (C-index: 0.712 VS. 0.627). The calibration plots showed that the predicted survival rate was in good agreement with the actual survival rate. And the decision curve analysis (DCA) curves revealed that nomogram showed stronger ability in predicting 1-year, 3-year, and 5-year OS. Conclusion This study estimated the excellent prognostic predictive power and clinical application potential of the MLNR-based nomogram, which may be used to facilitate postoperative clinical treatment decisions and potentially improve patient survival outcomes.

Collagen-related gene expression level predicts the prognosis and immune therapy response.

Gastric cancer patients responded differently to the same treatment strategy and had various prognoses for the lack of biomarkers to guide the therapy choice. RNA data of a local gastric cancer cohort with 103 patients were processed and used to explore potential treatment guiding factors. Cluster analysis was performed by non-negative matrix factorization. The expression level of collagen-related genes was evaluated by ssGSEA named collagen score (CS). Data from TCGA, ACRG, and an immune therapy cohort were utilized to explore prognosis and efficacy. Prognostic predictive power of CS was assessed using the nomogram. In our study, local RNA data were processed by cluster analysis, and it was found that cluster 2 contained a worse tumor infiltration status. The GSEA result showed that collagen-related pathways were differentially activated in two clusters. In TCGA and ACRG cohorts, the CS can be used as an independent prognostic factor (TCGA OS: p = 0.018, HR = 3.5; ACRG OS: p = 0.014, HR = 4.88). An immunotherapy cohort showed that the patients with higher CS had a significantly worse ORR (p = 0.0025). The high CS group contained several cell death pathways down-regulated and contained the worse tumor microenvironment. The nomogram demonstrated the survival prediction capability of collagen score. CS was verified as an independent prognostic factor and potentially reflected the therapeutic effect of immunotherapy. The CS could provide a new way to evaluate the clinical prognosis and response information helping develop the collagen-targeted treatment.

Prediction of Surgical Outcome by Tumor Volume Doubling Time via Stereo Imaging Software in Early Non-Small Cell Lung Cancer.

Volume doubling time (VDT) has been proven to be a powerful predictor of lung cancer progression. In non-small cell lung cancer patients receiving sublobar resection, the discussion of correlation between VDT and surgery was absent. We proposed to investigate the surgical outcomes according to VDT. We retrospectively studied 96 cases post sublobar resection from 2012 to 2018, collecting two chest CT scans preoperatively of each case and calculating the VDT. The receiver operating characteristic curve was constructed to identify the optimal cut-off point of VDTs as 133 days. We divided patients into two groups: VDT < 133 days and VDT ≥ 133 days. Univariable and multivariable analyses were performed for comparative purposes. Univariable and multivariable analyses revealed that the consolidation and tumor diameter ratio was the factor of overall survival (OS), and VDT was the only factor of disease-free survival (DFS). The five year OS rates of patients with VDTs ≥ 133 days and VDTs < 133 days, respectively, were 89.9% and 71.9%, and the five year DFS rates were 95.9% and 61.5%. As VDT serves as a powerful prognostic predictor and provides an essential role in planning surgical procedures, the evaluation of VDT preoperatively is highly suggested.

An amino acid metabolism-based seventeen-gene signature correlates with the clinical outcome and immune features in pancreatic cancer.

Background: Pancreatic cancer is an aggressive tumor with a low 5-year survival rate and primary resistance to most therapy. Amino acid (AA) metabolism is highly correlated with tumor growth, crucial to the aggressive biological behavior of pancreatic cancer; nevertheless, the comprehensive predictive significance of genes that regulate AA metabolism in pancreatic cancer remains unknown. Methods: The mRNA expression data downloaded from The Cancer Genome Atlas (TCGA) were derived as the training cohort, and the GSE57495 cohort from Gene Expression Omnibus (GEO) database was applied as the validation cohort. Random survival forest (RSF) and the least absolute shrinkage and selection operator (LASSO) regression analysis were employed to screen genes and construct an AA metabolism-related risk signature (AMRS). Kaplan-Meier analysis and receiver operating characteristic (ROC) curve were performed to assess the prognostic value of AMRS. We performed genomic alteration analysis and explored the difference in tumor microenvironment (TME) landscape associated with KRAS and TP53 mutation in both high- and low-AMRS groups. Subsequently, the relationships between AMRS and immunotherapy and chemotherapy sensitivity were evaluated. Results: A 17-gene AA metabolism-related risk model in the TCGA cohort was constructed according to RSF and LASSO. After stratifying patients into high- and low-AMRS groups based on the optimal cut-off value, we found that high-AMRS patients had worse overall survival (OS) in the training cohort (a median OS: 13.1months vs. 50.1 months, p < 0.0001) and validation cohort (a median OS: 16.2 vs. 30.5 months, p = 1e-04). Genetic mutation analysis revealed that KRAS and TP53 were significantly more mutated in high-AMRS group, and patients with KRAS and TP53 alterations had significantly higher risk scores than those without. Based on the analysis of TME, low-AMRS group displayed significantly higher immune score and more enrichment of T Cell CD8+ cells. In addition, high-AMRS-group exhibited higher TMB and significantly lower tumor immune dysfunction and exclusion (TIDE) score and T Cells dysfunction score, which suggested a higher sensitive to immunotherapy. Moreover, high-AMRS group was also more sensitive to paclitaxel, cisplatin, and docetaxel. Conclusion: Overall, we constructed an AA-metabolism prognostic model, which provided a powerful prognostic predictor for the clinical treatment of pancreatic cancer.

A novel anoikis-related gene prognostic signature and its correlation with the immune microenvironment in colorectal cancer.

Background: Anoikis is a type of apoptosis associated with cell detachment. Resistance to anoikis is a focal point of tumor metastasis. This study aimed to explore the relationship among anoikis-related genes (ARGs), immune infiltration, and prognosis in colorectal cancer (CRC). Methods: The transcriptome profile and clinical data on patients with CRC were retrieved from The Cancer Genome Atlas and Gene Expression Omnibus databases. Patients were divided into two clusters based on the expression of ARGs. Differences between the two ARG molecular subtypes were analyzed in terms of prognosis, functional enrichment, gene mutation frequency, and immune cell infiltration. An ARG-related prognostic signature for predicting overall survival in patients with CRC was developed and validated using absolute value convergence and selection operator (LASSO) regression analysis. The correlation between the signature risk score and clinicopathological features, immune cell infiltration, immune typing, and immunotherapy response was analyzed. The risk score combined with clinicopathological characteristics was used to construct a nomogram to assess CRC patients' prognosis. Results: Overall, 151 ARGs were differentially expressed in CRC. Two ARG subtypes, namely, ARG-high and ARG-low groups, were identified and correlated with CRC prognosis. The gene mutation frequency and immune, stromal, and ESTIMATE scores of the ARG-high group were higher than those of the ARG-low group. Moreover, CD8, natural killer cells, M1 macrophages, human leukocyte antigen (HLA), and immune checkpoint-related genes were significantly increased in the ARG-high group. An optimized 25-gene CRC prognostic signature was successfully constructed, and its prognostic predictive ability was validated. The high-risk score was correlated with T, N, M, and TNM stages. Risk scores were negatively correlated with dendritic cells, eosinophils, and CD4 cells, and significantly positively correlated with regulatory T cells. Patients in the high-risk group were more likely to exhibit immune unresponsiveness. Finally, the nomogram model was constructed and showed good prognostic predictive power. Conclusion: ARGs are associated with clinicopathological features and the prognosis of CRC, and play important roles in the immune microenvironment. Herein, we underpinned the usefulness of ARGs in CRC to develop more effective immunotherapy techniques.

A Machine Learning Approach for Predicting Capsular Contracture after Postmastectomy Radiotherapy in Breast Cancer Patients

In recent years, immediate breast reconstruction after mastectomy surgery has steadily increased in the treatment pathway of breast cancer (BC) patients due to its potential impact on both the morpho-functional and aesthetic type of the breast and the quality of life. Although recent studies have demonstrated how recent radiotherapy techniques have allowed a reduction of adverse events related to breast reconstruction, capsular contracture (CC) remains the main complication after post-mastectomy radio-therapy (PMRT). In this study, we evaluated the association of the occurrence of CC with some clinical, histological and therapeutic parameters related to BC patients. We firstly performed bivariate statistical tests and we then evaluated the prognostic predictive power of the collected data by using machine learning techniques. Out of a sample of 59 patients referred to our institute, 28 patients (i.e., 47%) showed contracture after PMRT. As a result, only estrogen receptor status (ER) and molecular subtypes were significantly associated with the occurrence of CC after PMRT. Different machine learning models were trained on a subset of clinical features selected by a feature importance approach. Experimental results have shown that collected features have a non-negligible predictive power. The extreme gradient boosting classifier achieved an area under the curve (AUC) value of 68% and accuracy, sensitivity, and specificity values of 68%, 64%, and 74%, respectively. Such a support tool, after further suitable optimization and validation, would allow clinicians to identify the best therapeutic strategy and reconstructive timing.

Identification and Experimental Validation of the Prognostic Significance and Immunological Correlation of Glycosylation-Related Signature and ST6GALNAC4 in Hepatocellular Carcinoma.

Glycosylation has been demonstrated to be involved in tumorigenesis, progression, and immunoregulation, and to present specific profiles in different tumors. In this study, we aimed to explore the specific glycosylation-related gene (GRG) signature and its potential immunological roles and prognostic implications in hepatocellular carcinoma (HCC). The GRG expression profile was defined using the transcriptome data from The Cancer Genome Atlas and Gene Expression Omnibus. Univariate and the least absolute shrinkage and selection operator Cox analyses were performed to develop a GRG-based risk score model. A nomogram was subsequently established and validated. Its correlation with cancer immune microenvironment and drug susceptibility was further analyzed. The role and immunological correlation of ST6GALNAC4 were further experimentally validated at the tissue and cellular levels in HCC. A total of 87 GRGs were identified to be significantly dysregulated in HCC, and a novel risk score model was constructed using eight critical GRGs, which demonstrated superior prognostic discrimination and predictive power in both training and validation groups. High risk scores in HCC patients were associated with lower OS. The model was also identified as an independent risk factor for HCC, and a novel nomogram was subsequently constructed and validated. Notably, significant correlations were found in risk scores with immune cells infiltration, tumor immunophenotyping, immune checkpoint genes' expression, and sensitivities to multiple drugs. Furthermore, we validated in local HCC samples that ST6GALNAC4 was significantly upregulated and its knockdown significantly inhibited the tumor proliferation, migration and invasion ability and affected the expression of immune checkpoints on hepatoma cells. We identified a novel GRG-based model which showed significant prognostic and immunological correlations in HCC, and the oncogenic role of ST6GALNAC4 has been validated and may serve as a potential drug target.