Optimizing Social Distancing Policies: A Dynamic Programming Approach for Coupled High and Low Risk Populations

Background: Decision makers may use social distancing to reduce transmission between risk groups in a pandemic scenario like Covid-19. However, it may result in both financial, mental, and social costs. Given these tradeoffs, it is unclear when and who needs to social distance over the course of a pandemic when policies are allowed to change dynamically over time and vary across different risk groups (e.g., older versus younger individuals face different Covid-19 risks). In this study, we examine the optimal time to implement social distancing to optimize social utility, using Covid-19 as an example. Methodology: We propose using a Markov decision process (MDP) model that incorporates transmission dynamics of an age-stratified SEIR compartmental model to identify the optimal social distancing policy for each risk group over time. We parameterize the model using population-based tracking data on Covid-19 within the US. We compare results of two cases: allowing the social distancing policy to vary only over time, or over both time and population (by risk group). To examine the robustness of our results, we perform sensitivity analysis on patient costs, transmission rates, clearance rates, mortality rates. Results: Our model framework can be used to effectively evaluate dynamic policies while disease transmission and progression occurs. When the policy cannot vary by subpopulation, the optimal policy is to implement social distancing for a limited duration at the beginning of the epidemic; when the policy can vary by subpopulation, our results suggest that some subgroups (older adults) may never need to socially distance. This result may occur because older adults occupy a relatively small proportion of the total population and have less contact with others even without social distancing. Conclusion: Our results show that the additional flexibility of allowing social distancing policies to vary over time and across the population can generate substantial utility gain even when only two patient risk groups are considered. MDP frameworks may help generate helpful insights for policymakers. Our results suggest that social distancing for high-contact but low-risk individuals (e.g., such as younger adults) may be more beneficial in some settings than doing so for low-contact but high-risk individuals (e.g., older adults).

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Recent Changes in the Clinical Outcome of Papillary Thyroid Carcinoma With Cervical Lymph Node Metastasis

The Journal of Clinical Endocrinology & Metabolism ◽

10.1210/jc.2015-2084 ◽

2015 ◽

Vol 100 (9) ◽

pp. 3470-3477 ◽

Cited By ~ 32

Author(s):

Min Ji Jeon ◽

Won Gu Kim ◽

Yun Mi Choi ◽

Hyemi Kwon ◽

Dong Eun Song ◽

...

Keyword(s):

Prognostic Factors ◽

Lymph Node ◽

High Risk ◽

Clinical Outcome ◽

Cervical Lymph Node ◽

Risk Group ◽

Risk Groups ◽

Low Risk ◽

Papillary Thyroid ◽

Over Time

Context: The prognosis of papillary thyroid cancer (PTC) with cervical lymph node (LN) metastasis has changed with increased detection of subclinical metastatic LNs. The number and size of metastatic LNs were proposed as new prognostic factors in PTC with cervical LN metastasis (N1). Objective: The objective of the study was to evaluate changes in N1 PTC characteristics and clinical outcome over time and to confirm the prognostic value of the number and size of metastatic LNs. Design and Patients: This study included 1815 N1 PTC patients diagnosed between 1997 and 2011. Patients were classified into three risk groups according to the number and size of metastatic LNs: very low risk, five or fewer and 0.2 cm or less; low risk, five or fewer and 0.2 cm or greater; and high risk, more than five. Main Outcome Measures: Response to initial therapy and disease-free survival (DFS) was measured. Results: Metastatic LNs became smaller, and the ratio of metastatic LNs, which represents the extent of LN involvement and the completeness of surgery, decreased significantly over time. The proportion of patients with excellent response significantly increased from 33% to 67% over time (P < .001). These improvements were more evident in the low- and high-risk groups than in the very low-risk group. The DFS 5 years after initial surgery was also significantly increased from 73% to 91% over time (P < .001). The new LN classification was strongly associated with outcome. Patients in the very low-risk group had longer DFS than those in the low- and high-risk groups during the study period. Conclusions: The clinical outcome of N1 PTC has significantly changed over time with the earlier detection of thyroid cancers with less extensive LN involvement. More complete surgical neck dissection also might be responsible for these changes. The number and size of metastatic LNs are important prognostic factors of recurrence in N1 PTC.

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Short-term risk stratification using CADILLAC risk score in patients with ST elevation myocardial infarction

European Heart Journal ◽

10.1093/ehjci/ehaa946.1352 ◽

2020 ◽

Vol 41 (Supplement_2) ◽

Author(s):

T Satou ◽

H Kitahara ◽

K Ishikawa ◽

T Nakayama ◽

Y Fujimoto ◽

...

Keyword(s):

Myocardial Infarction ◽

High Risk ◽

Adverse Events ◽

Risk Score ◽

Risk Group ◽

Risk Groups ◽

Low Risk ◽

Short Term ◽

Recurrent Myocardial Infarction ◽

St Elevation

Abstract Background The recent reperfusion therapy for ST-elevation myocardial infarction (STEMI) has made the length of hospital stay shorter without adverse events. CADILLAC risk score is reportedly one of the risk scores predicting the long-term prognosis in STEMI patients. Purpose To invenstigate the usefulness of CADILLAC risk score for predicting short-term outcomes in STEMI patients. Methods Consecutive patients admitted to our university hospital and our medical center with STEMI (excluding shock, arrest case) who underwent primary PCI between January 2012 and April 2018 (n=387) were enrolled in this study. The patients were classified into 3 groups according to the CADILLAC risk score: low risk (n=176), intermediate risk (n=87), and high risk (n=124). Data on adverse events within 30 days after hospitalization, including in-hospital death, sustained ventricular arrhythmia, recurrent myocardial infarction, heart failure requiring intravenous treatment, stroke, or clinical hemorrhage, were collected. Results In the low risk group, adverse events within 30 days were significantly less observed, compared to the intermediate and high risk groups (n=13, 7.4% vs. n=13, 14.9% vs. n=58, 46.8%, p<0.001). In particular, all adverse events occurred within 3 days in the low risk group, although adverse events, such as heart failure (n=4), recurrent myocardial infarction (n=1), stroke (n=1), and gastrointestinal bleeding (n=1), were substantially observed after day 4 of hospitalization in the intermediate and high risk groups. Conclusions In STEMI patients with low CADILLAC risk score, better short-term prognosis was observed compared to the intermediate and high risk groups, and all adverse events occurred within 3 days of hospitalization, suggesting that discharge at day 4 might be safe in this study population. CADILLAC risk score may help stratify patient risk for short-term prognosis and adjust management of STEMI patients. Initial event occurrence timing Funding Acknowledgement Type of funding source: None

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Immune‐related Long Noncoding RNA Signature for Patients with Cervical Cancer

10.21203/rs.3.rs-49217/v1 ◽

2020 ◽

Author(s):

Jianfeng Zheng ◽

Jinyi Tong ◽

Benben Cao ◽

Xia Zhang ◽

Zheng Niu

Keyword(s):

Cervical Cancer ◽

High Risk ◽

Risk Score ◽

Immune Cell ◽

Risk Group ◽

Risk Groups ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Training Set ◽

Score Model

Abstract Background: Cervical cancer (CC) is a common gynecological malignancy for which prognostic and therapeutic biomarkers are urgently needed. The signature based on immune‐related lncRNAs(IRLs) of CC has never been reported. This study aimed to establish an IRL signature for patients with CC.Methods: The RNA-seq dataset was obtained from the TCGA, GEO, and GTEx database. The immune scores（IS）based on single-sample gene set enrichment analysis (ssGSEA) were calculated to identify the IRLs, which were then analyzed using univariate Cox regression analysis to identify significant prognostic IRLs. A risk score model was established to divide patients into low-risk and high-risk groups based on the median risk score of these IRLs. This was then validated by splitting TCGA dataset(n=304) into a training-set(n=152) and a valid-set(n=152). The fraction of 22 immune cell subpopulations was evaluated in each sample to identify the differences between low-risk and high-risk groups. Additionally, a ceRNA network associated with the IRLs was constructed.Results: A cohort of 326 CC and 21 normal tissue samples with corresponding clinical information was included in this study. Twenty-eight IRLs were collected according to the Pearson’s correlation analysis between immune score and lncRNA expression (P < 0.01). Four IRLs (BZRAP1-AS1, EMX2OS, ZNF667-AS1, and CTC-429P9.1) with the most significant prognostic values (P < 0.05) were identified which demonstrated an ability to stratify patients into low-risk and high-risk groups by developing a risk score model. It was observed that patients in the low‐risk group showed longer overall survival (OS) than those in the high‐risk group in the training-set, valid-set, and total-set. The area under the curve (AUC) of the receiver operating characteristic curve (ROC curve) for the four IRLs signature in predicting the one-, two-, and three-year survival rates were larger than 0.65. In addition, the low-risk and high-risk groups displayed different immune statuses in GSEA. These IRLs were also significantly correlated with immune cell infiltration. Conclusions: Our results showed that the IRL signature had a prognostic value for CC. Meanwhile, the specific mechanisms of the four-IRLs in the development of CC were ascertained preliminarily.

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Abstract 18787: Hypertension Treatment and Control Rates in United States Adults by Risk Group

Circulation ◽

10.1161/circ.130.suppl_2.18787 ◽

2014 ◽

Vol 130 (suppl_2) ◽

Author(s):

Kunal N Karmali ◽

Hongyan Ning ◽

Donald M Lloyd-Jones

Keyword(s):

Older Adults ◽

High Risk ◽

Kidney Disease ◽

Risk Group ◽

Intensive Therapy ◽

Risk Groups ◽

Middle Aged ◽

Ascvd Risk ◽

Middle Aged Adults ◽

And Control

Introduction: Ten-year cardiovascular disease (CVD) risk and absolute benefit from antihypertensive therapy vary at any given BP based on associated risk factor levels. Thus, implications of treatment and control rates at a particular BP vary substantially in different risk groups. Objectives: We examined the prevalence, treatment, and control of hypertension (HTN) by risk group in US adults without prevalent CVD. Methods: We used data from the National Health and Nutrition Examination Survey 2005 to 2010 for adults age 40-79 years without prevalent CVD (n=4,066). We estimated 10-year risk for an atherosclerotic CVD (ASCVD) event using the ACC/AHA 2013 Pooled Cohort risk equations. We examined HTN treatment and control rates according to current guidelines in middle-aged (40-59 years) and older (60-79 years) adults with: 10-year ASCVD risk <7.5% (no diabetes/kidney disease); 10-year ASCVD risk ≥7.5% (no diabetes/kidney disease); and either diabetes or kidney disease. Results: The proportion of adults with treatment-eligible HTN was 39.3% for those with 10-year ASCVD risk <7.5%, 32.2% for those with 10-year ASCVD risk ≥7.5%, and 28.4% for those with either diabetes or kidney disease (see Table 1). Treatment rates across the risk groups varied from 51.5% to 79.0% for middle-aged adults and 81.8% to 90.2% for older adults. HTN control rates were highest (87.7%) in older adults with 10-year ASCVD risk <7.5% but were lowest (29.3%) in middle-aged individuals with 10-year ASCVD risk ≥7.5%. Conclusions: US HTN guidelines, based solely on BP thresholds, identify a higher proportion of low-risk adults and a lower proportion of high-risk adults as being eligible for treatment. Control rates remain suboptimal in high-risk individuals, particularly middle-aged adults. Future guidelines should consider pre-treatment risk stratification to identify those at increased pretreatment ASCVD risk who would benefit most from more intensive therapy.

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A Novel Risk Classification Model Predicts Overall Survival and Locoregional Surgery Benefit in Colorectal Patients with Distant Metastasis at the Initial Diagnosis

10.21203/rs.3.rs-117490/v1 ◽

2020 ◽

Author(s):

Mo Chen ◽

Tian-en Li ◽

Pei-zhun Du ◽

Junjie Pan ◽

Zheng Wang ◽

...

Keyword(s):

Overall Survival ◽

High Risk ◽

Distant Metastasis ◽

Cox Regression ◽

Risk Group ◽

Risk Groups ◽

Risk Classification ◽

Low Risk ◽

Classification Model ◽

Intermediate Risk

Abstract Background and aims: In this research, we aimed to construct a risk classification model to predict overall survival (OS) and locoregional surgery benefit in colorectal cancer (CRC) patients with distant metastasis.Methods: We selected a cohort consisting of 12741 CRC patients diagnosed with distant metastasis between 2010 and 2014, from the Surveillance, Epidemiology and End Results (SEER) database. Patients were randomly assigned into training group and validation group at the ratio of 2:1. Univariable and multivariable Cox regression models were applied to screen independent prognostic factors. A nomogram was constructed and assessed by the Harrell’s concordance index (C-index) and calibration plots. A novel risk classification model was further established based on the nomogram.Results: Ultimately 12 independent risk factors including race, age, marriage, tumor site, tumor size, grade, T stage, N stage, bone metastasis, brain metastasis, lung metastasis and liver metastasis were identified and adopted in the nomogram. The C-indexes of training and validation groups were 0.77 (95% confidence interval [CI] 0.73-0.81) and 0.75 (95% CI 0.72-0.78), respectively. The risk classification model stratified patients into three risk groups (low-, intermediate- and high-risk) with divergent median OS (low-risk: 36.0 months, 95% CI 34.1-37.9; intermediate-risk: 18.0 months, 95% CI 17.4-18.6; high-risk: 6.0 months, 95% CI 5.3-6.7). Locoregional therapies including surgery and radiotherapy could prognostically benefit patients in the low-risk group (surgery: hazard ratio [HR] 0.59, 95% CI 0.50-0.71; radiotherapy: HR 0.84, 95% CI 0.72-0.98) and intermediate risk group (surgery: HR 0.61, 95% CI 0.54-0.68; radiotherapy: HR 0.86, 95% CI 0.77-0.95), but not in the high-risk group (surgery: HR 1.03, 95% CI 0.82-1.29; radiotherapy: HR 1.03, 95% CI 0.81-1.31). And all risk groups could benefit from systemic therapy (low-risk: HR 0.68, 95% CI 0.58-0.80; intermediate-risk: HR 0.50, 95% CI 0.47-0.54; high-risk: HR 0.46, 95% CI 0.40-0.53).Conclusion: A novel risk classification model predicting prognosis and locoregional surgery benefit of CRC patients with distant metastasis was established and validated. This predictive model could be further utilized by physicians and be of great significance for medical practice.

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MO048MULTICENTRE PROSPECTIVE VALIDATION OF THE URINARY PEPTIDOME-BASED CLASSIFIER CKD273 AS A PREDICTOR OF RENAL FUNCTION DECLINE IN SUBJECTS WITH TYPE 2 DIABETES

Nephrology Dialysis Transplantation ◽

10.1093/ndt/gfaa140.mo048 ◽

2020 ◽

Vol 35 (Supplement_3) ◽

Author(s):

Morten Lindhardt ◽

Nete Tofte ◽

Gemma Currie ◽

Marie Frimodt-Moeller ◽

Heiko Von der Leyen ◽

...

Keyword(s):

Type 2 Diabetes ◽

Renal Function ◽

High Risk ◽

Risk Group ◽

Cox Model ◽

Risk Groups ◽

High Risk Group ◽

Low Risk ◽

Renal Function Decline

Abstract Background and Aims In the PRIORITY study, it was recently demonstrated that the urinary peptidome-based classifier CKD273 was associated with increased risk for progression to microalbuminuria. As a prespecified secondary outcome, we aim to evaluate the classifier CKD273 as a determinant of relative reductions in eGFR (CKD-EPI) of 30% and 40% from baseline, at one timepoint without requirements of confirmation. Method The ‘Proteomic prediction and Renin angiotensin aldosterone system Inhibition prevention Of early diabetic nephRopathy In TYpe 2 diabetic patients with normoalbuminuria trial’ (PRIORITY) is the first prospective observational study to evaluate the early detection of diabetic kidney disease in subjects with type 2 diabetes (T2D) and normoalbuminuria using the CKD273 classifier. Setting 1775 subjects from 15 European sites with a mean follow-up time of 2.6 years (minimum of 7 days and a maximum of 4.3 years). Patients Subjects with T2D, normoalbuminuria and estimated glomerular filtration rate (eGFR) ≥ 45 ml/min/1.73m2. Participants were stratified into high- or low-risk groups based on their CKD273 score in a urine sample at screening (high-risk defined as score > 0.154). Results In total, 12 % (n = 216) of the subjects had a high-risk proteomic pattern. Mean (SD) baseline eGFR was 88 (15) ml/min/1.73m2 in the low-risk group and 81 (17) ml/min/1.73m2 in the high-risk group (p < 0.01). Baseline median (interquartile range) urinary albumin to creatinine ratio (UACR) was 5 (3-8) mg/g and 7 (4-12) mg/g in the low-risk and high-risk groups, respectively (p < 0.01). A 30 % reduction in eGFR from baseline was seen in 42 (19.4 %) subjects in the high-risk group as compared to 62 (3.9 %) in the low-risk group (p < 0.0001). In an unadjusted Cox-model the hazard ratio (HR) for the high-risk group was 5.7, 95 % confidence interval (CI) (3.9 to 8.5; p<0.0001). After adjustment for baseline eGFR and UACR, the HR was 5.2, 95 % CI (3.4 to 7.8; p<0.0001). A 40 % reduction in eGFR was seen in 15 (6.9 %) subjects in the high-risk group whereas 22 (1.4 %) in the low-risk group developed this endpoint (p<0.0001). In an unadjusted Cox-model the HR for the high-risk group was 5.0, 95 % CI (2.6 to 9.6; p<0.0001). After adjustment for baseline eGFR and UACR, the HR was 4.8, 95 % CI (2.4 to 9.7; p<0.0001). Conclusion In normoalbuminuric subjects with T2D, the urinary proteomic classifier CKD273 predicts renal function decline of 30 % and 40 %, independent of baseline eGFR and albuminuria.

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Immune-Related Four-lncRNA Signature for Patients with Cervical Cancer

BioMed Research International ◽

10.1155/2020/3641231 ◽

2020 ◽

Vol 2020 ◽

pp. 1-15

Author(s):

Jianfeng Zheng ◽

Benben Cao ◽

Xia Zhang ◽

Zheng Niu ◽

Jinyi Tong

Keyword(s):

Cervical Cancer ◽

High Risk ◽

Immune Cell ◽

Risk Group ◽

Pearson Correlation ◽

Characteristic Curve ◽

Risk Groups ◽

High Risk Group ◽

Low Risk ◽

Gynecological Malignancy

Cervical cancer (CC) is a common gynecological malignancy for which prognostic and therapeutic biomarkers are urgently needed. The signature based on immune-related lncRNAs (IRLs) of CC has never been reported. This study is aimed at establishing an IRL signature for patients with CC. A cohort of 326 CC and 21 normal tissue samples with corresponding clinical information was included in this study. Twenty-eight IRLs were collected according to the Pearson correlation analysis between the immune score and lncRNA expression ( p < 0.01 ). Four IRLs (BZRAP1-AS1, EMX2OS, ZNF667-AS1, and CTC-429P9.1) with the most significant prognostic values ( p < 0.05 ) were identified which demonstrated an ability to stratify patients into the low-risk and high-risk groups by developing a risk score model. It was observed that patients in the low-risk group showed longer overall survival (OS) than those in the high-risk group in the training set, valid set, and total set. The area under the curve (AUC) of the receiver operating characteristic curve (ROC curve) for the four-IRL signature in predicting the one-, two-, and three-year survival rates was larger than 0.65. In addition, the low-risk and high-risk groups displayed different immune statuses in GSEA. These IRLs were also significantly correlated with immune cell infiltration. Our results showed that the IRL signature had a prognostic value for CC. Meanwhile, the specific mechanisms of the four IRLs in the development of CC were ascertained preliminarily.

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Validation of Postpartum Hemorrhage Admission Risk Factor Stratification in a Large Obstetrics Population

American Journal of Perinatology ◽

10.1055/s-0040-1712166 ◽

2020 ◽

Author(s):

Halley Ruppel ◽

Vincent X. Liu ◽

Neeru R. Gupta ◽

Lauren Soltesz ◽

Gabriel J. Escobar

Keyword(s):

Risk Factors ◽

Risk Assessment ◽

High Risk ◽

Blood Loss ◽

Postpartum Hemorrhage ◽

Risk Group ◽

Risk Groups ◽

Low Risk ◽

Hemorrhage Risk ◽

Present On Admission

Abstract Objective This study aimed to evaluate the performance of the California Maternal Quality Care Collaborative (CMQCC) admission risk criteria for stratifying postpartum hemorrhage risk in a large obstetrics population. Study Design Using detailed electronic health record data, we classified 261,964 delivery hospitalizations from Kaiser Permanente Northern California hospitals between 2010 and 2017 into high-, medium-, and low-risk groups based on CMQCC criteria. We used logistic regression to assess associations between CMQCC risk groups and postpartum hemorrhage using two different postpartum hemorrhage definitions, standard postpartum hemorrhage (blood loss ≥1,000 mL) and severe postpartum hemorrhage (based on transfusion, laboratory, and blood loss data). Among the low-risk group, we also evaluated associations between additional present-on-admission factors and severe postpartum hemorrhage. Results Using the standard definition, postpartum hemorrhage occurred in approximately 5% of hospitalizations (n = 13,479), with a rate of 3.2, 10.5, and 10.2% in the low-, medium-, and high-risk groups. Severe postpartum hemorrhage occurred in 824 hospitalizations (0.3%), with a rate of 0.2, 0.5, and 1.3% in the low-, medium-, and high-risk groups. For either definition, the odds of postpartum hemorrhage were significantly higher in medium- and high-risk groups compared with the low-risk group. Over 40% of postpartum hemorrhages occurred in hospitalizations that were classified as low risk. Among the low-risk group, risk factors including hypertension and diabetes were associated with higher odds of severe postpartum hemorrhage. Conclusion We found that the CMQCC admission risk assessment criteria stratified women by increasing rates of severe postpartum hemorrhage in our sample, which enables early preparation for many postpartum hemorrhages. However, the CMQCC risk factors missed a substantial proportion of postpartum hemorrhages. Efforts to improve postpartum hemorrhage risk assessment using present-on-admission risk factors should consider inclusion of other nonobstetrical factors.

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Computational identification of mutator-derived lncRNA signatures of genome instability for improving the clinical outcome of cancers: a case study in breast cancer

Briefings in Bioinformatics ◽

10.1093/bib/bbz118 ◽

2019 ◽

Vol 21 (5) ◽

pp. 1742-1755 ◽

Cited By ~ 26

Author(s):

Siqi Bao ◽

Hengqiang Zhao ◽

Jian Yuan ◽

Dandan Fan ◽

Zicheng Zhang ◽

...

Keyword(s):

Breast Cancer ◽

Genomic Instability ◽

Genome Instability ◽

Risk Group ◽

Expression Profiles ◽

Risk Groups ◽

Low Risk ◽

A Genome ◽

Significant Difference

Abstract Emerging evidence revealed the critical roles of long non-coding RNAs (lncRNAs) in maintaining genomic instability. However, identification of genome instability-associated lncRNAs and their clinical significance in cancers remain largely unexplored. Here, we developed a mutator hypothesis-derived computational frame combining lncRNA expression profiles and somatic mutation profiles in a tumor genome and identified 128 novel genomic instability-associated lncRNAs in breast cancer as a case study. We then identified a genome instability-derived two lncRNA-based gene signature (GILncSig) that stratified patients into high- and low-risk groups with significantly different outcome and was further validated in multiple independent patient cohorts. Furthermore, the GILncSig correlated with genomic mutation rate in both ovarian cancer and breast cancer, indicating its potential as a measurement of the degree of genome instability. The GILncSig was able to divide TP53 wide-type patients into two risk groups, with the low-risk group showing significantly improved outcome and the high-risk group showing no significant difference compared with those with TP53 mutation. In summary, this study provided a critical approach and resource for further studies examining the role of lncRNAs in genome instability and introduced a potential new avenue for identifying genomic instability-associated cancer biomarkers.

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The ferroptosis and iron-metabolism signature robustly predicts clinical diagnosis, prognosis and immune microenvironment for hepatocellular carcinoma

Cell Communication and Signaling ◽

10.1186/s12964-020-00663-1 ◽

2020 ◽

Vol 18 (1) ◽

Cited By ~ 1

Author(s):

Bufu Tang ◽

Jinyu Zhu ◽

Jie Li ◽

Kai Fan ◽

Yang Gao ◽

...

Keyword(s):

High Risk ◽

Iron Metabolism ◽

Cox Regression ◽

Risk Group ◽

Risk Groups ◽

Low Risk ◽

Prognostic Models ◽

Integrated Analysis ◽

Immune Microenvironment ◽

Diagnostic Models

Abstract Background In this study, we comprehensively analyzed genes related to ferroptosis and iron metabolism to construct diagnostic and prognostic models and explore the relationship with the immune microenvironment in HCC. Methods Integrated analysis, cox regression and the least absolute shrinkage and selection operator (LASSO) method of 104 ferroptosis- and iron metabolism-related genes and HCC-related RNA sequencing were performed to identify HCC-related ferroptosis and iron metabolism genes. Results Four genes (ABCB6, FLVCR1, SLC48A1 and SLC7A11) were identified to construct prognostic and diagnostic models. Poorer overall survival (OS) was exhibited in the high-risk group than that in the low-risk group in both the training cohort (P < 0.001, HR = 0.27) and test cohort (P < 0.001, HR = 0.27). The diagnostic models successfully distinguished HCC from normal samples and proliferative nodule samples. Compared with low-risk groups, high-risk groups had higher TMB; higher fractions of macrophages, follicular helper T cells, memory B cells, and neutrophils; and exhibited higher expression of CD83, B7H3, OX40 and CD134L. As an inducer of ferroptosis, erastin inhibited HCC cell proliferation and progression, and it was showed to affect Th17 cell differentiation and IL-17 signaling pathway through bioinformatics analysis, indicating it a potential agent of cancer immunotherapy. Conclusions The prognostic and diagnostic models based on the four genes indicated superior diagnostic and predictive performance, indicating new possibilities for individualized treatment of HCC patients. Graphical abstract

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