Denoiseit: denoising gene expression data using rank based isolation trees.

BACKGROUND: Selecting informative genes or eliminating uninformative ones before any downstream gene expression analysis is a standard task with great impact on the results. A carefully curated gene set significantly enhances the likelihood of identifying meaningful biomarkers. METHOD: In contrast to the conventional forward gene search methods that focus on selecting highly informative genes, we propose a backward search method, DenoiseIt, that aims to remove potential outlier genes yielding a robust gene set with reduced noise. The gene set constructed by DenoiseIt is expected to capture biologically significant genes while pruning irrelevant ones to the greatest extent possible. Therefore, it also enhances the quality of downstream comparative gene expression analysis. DenoiseIt utilizes non-negative matrix factorization in conjunction with isolation forests to identify outlier rank features and remove their associated genes. RESULTS: DenoiseIt was applied to both bulk and single-cell RNA-seq data collected from TCGA and a COVID-19 cohort to show that it proficiently identified and removed genes exhibiting expression anomalies confined to specific samples rather than a known group. DenoiseIt also showed to reduce the level of technical noise while preserving a higher proportion of biologically relevant genes compared to existing methods. The DenoiseIt Software is publicly available on GitHub at https://github.com/cobi-git/DenoiseIt.

Discovery of high-expressing lncRNA-derived sORFs as potential tumor-associated antigens in hepatocellular carcinoma.

BACKGROUND: This study is based on deep mining of Ribo-seq data for the identification of lncRNAs that have highly expressed sORFs in HCC. In this paper, dynamic prospects associated with sORFs acting as newly defined tumor-specific epitopes are discussed with possible improvement in strategies for tumor immunotherapy. OBJECTIVE: Using ribosome profiling to identify and characterize sORFs within lncRNAs in HCC, identify potential therapeutic targets and tumor-specific epitopes applicable for immunotherapy. METHODS: MetamORF performed the identification of sORFs with deep analysis of the data of ribosome profiling in lncRNAs associated with HCC. The translation efficiency in these molecules was estimated, and epitope prediction was done by pVACbind. Peptide search was done to check the presence of micropeptides translated from these identified sORFs. validated translational activity and identified potential epitopes. RESULTS: Higher translation efficiency was noted in the case of lncRNAs associated with HCC compared to normal tissues. Of particular note is ORF3418981, which results in the highest expression and has supporting experimental evidence at the protein level. Epitope prediction identified a putative epitope at the C-terminus of ORF3418981. CONCLUSIONS: This study uncovers the as-yet-unknown potential of lncRNA-derived sORFs as sources of tumor antigens, shifting the research focus from protein-coding genes to non-coding RNAs also in the HCC context. Moreover, this study highlights the contribution of a subset of lncRNAs, especially LINC00152, to the development of tumors and modulation of the immune response by its sORFs.

Financial Benefits of Renal Dose-Adjusted Dipeptidyl Peptidase-4 Inhibitors for Patients with Type 2 Diabetes and Chronic Kidney Disease.

Background: Dipeptidyl peptidase-4 (DPP4) inhibitors are frequently prescribed for patients with type 2 diabetes; however, their cost can pose a significant barrier for those with impaired kidney function. This study aimed to estimate the economic benefits of substituting non-renal dose-adjusted (NRDA) DPP4 inhibitors with renal dose-adjusted (RDA) DPP4 inhibitors in patients with both impaired kidney function and type 2 diabetes. Methods: This retrospective cohort study was conducted from January 1, 2012 to December 31, 2018, using data obtained from common data models of five medical centers in Korea. Model 1 applied the prescription pattern of participants with preserved kidney function to those with impaired kidney function. In contrast, model 2 replaced all NRDA DPP4 inhibitors with RDA DPP4 inhibitors, adjusting the doses of RDA DPP4 inhibitors based on individual kidney function. The primary outcome was the cost difference between the two models. Results: In total, 67,964,996 prescription records were analyzed. NRDA DPP4 inhibitors were more frequently prescribed to patients with impaired kidney function than in those with preserved kidney function (25.7%, 51.3%, 64.3%, and 71.6% in patients with estimated glomerular filtration rates [eGFRs] of ≥60, <60, <45, and <30 mL/min/1.73 m2, respectively). When model 1 was applied, the cost savings per year were 7.6% for eGFR <60 mL/min/1.73 m2 and 30.4% for eGFR <30 mL/min/1.73 m2. According to model 2, 15.4% to 51.2% per year could be saved depending on kidney impairment severity. Conclusion: Adjusting the doses of RDA DPP4 inhibitors based on individual kidney function could alleviate the economic burden associated with medical expenses.

Fc-enhanced anti-CTLA-4, anti-PD-1, doxorubicin, and ultrasound-mediated BBB opening: A novel combinatorial immunotherapy regimen for gliomas.

BACKGROUND: Glioblastoma is a highly aggressive brain cancer that is resistant to conventional immunotherapy strategies. Botensilimab, an Fc-enhanced anti-CTLA-4 antibody (FcE-aCTLA-4), has shown durable activity in "cold" and immunotherapy-refractory cancers. METHOD: We evaluated the efficacy and immune microenvironment phenotype of a mouse analogue of FcE-aCTLA-4 in treatment-refractory preclinical models of glioblastoma, both as a monotherapy and in combination with doxorubicin delivered via low-intensity pulsed ultrasound and microbubbles (LIPU/MB). Additionally, we studied 4 glioblastoma patients treated with doxorubicin, anti-PD-1 with concomitant LIPU/MB to investigate the novel effect of doxorubicin modulating FcγR expressions in tumor associated macrophages/microglia (TAMs). RESULTS: FcE-aCTLA-4 demonstrated high-affinity binding to FcγRIV, the mouse ortholog of human FcγRIIIA, which was highly expressed in TAMs in human glioblastoma, most robustly at diagnosis. Notably, FcE-aCTLA-4 mediated selective depletion of intra-tumoral regulatory T cells (Tregs) via TAM-mediated phagocytosis, while sparing peripheral Tregs. Doxorubicin, a chemotherapeutic drug with immunomodulatory functions, was found to upregulate FcγRIIIA on TAMs in glioblastoma patients who received doxorubicin and anti-PD-1 with concomitant LIPU/MB. In murine models of immunotherapy-resistant gliomas, a combinatorial regimen of FcE-aCTLA-4, anti-PD-1, and doxorubicin with LIPU/MB, achieved a 90% cure rate, that was associated robust infiltration of activated CD8+ T cells and establishment of immunological memory as evidenced by rejection upon tumor rechallenge. CONCLUSION: Our findings demonstrate that FcE-aCTLA-4 promotes robust immunomodulatory and anti-tumor effects in murine gliomas and is significantly enhanced when combined with anti-PD-1, doxorubicin, and LIPU/MB. We are currently investigating this combinatory strategy in a clinical trial (clinicaltrials.gov NCT05864534).

Time Series AI Model for Acute Kidney Injury Detection Based on a Multicenter Distributed Research Network: Development and Verification Study.

Background: Acute kidney injury (AKI) is a marker of clinical deterioration and renal toxicity. While there are many studies offering prediction models for the early detection of AKI, those predicting AKI occurrence using distributed research network (DRN)-based time series data are rare. Objective: In this study, we aimed to detect the early occurrence of AKI by applying an interpretable long short-term memory (LSTM)-based model to hospital electronic health record (EHR)-based time series data in patients who took nephrotoxic drugs using a DRN. Methods: We conducted a multi-institutional retrospective cohort study of data from 6 hospitals using a DRN. For each institution, a patient-based data set was constructed using 5 drugs for AKI, and an interpretable multivariable LSTM (IMV-LSTM) model was used for training. This study used propensity score matching to mitigate differences in demographics and clinical characteristics. Additionally, the temporal attention values of the AKI prediction model's contribution variables were demonstrated for each institution and drug, with differences in highly important feature distributions between the case and control data confirmed using 1-way ANOVA. Results: This study analyzed 8643 and 31,012 patients with and without AKI, respectively, across 6 hospitals. When analyzing the distribution of AKI onset, vancomycin showed an earlier onset (median 12, IQR 5-25 days), and acyclovir was the slowest compared to the other drugs (median 23, IQR 10-41 days). Our temporal deep learning model for AKI prediction performed well for most drugs. Acyclovir had the highest average area under the receiver operating characteristic curve score per drug (0.94), followed by acetaminophen (0.93), vancomycin (0.92), naproxen (0.90), and celecoxib (0.89). Based on the temporal attention values of the variables in the AKI prediction model, verified lymphocytes and calcvancomycin ium had the highest attention, whereas lymphocytes, albumin, and hemoglobin tended to decrease over time, and urine pH and prothrombin time tended to increase. Conclusions: Early surveillance of AKI outbreaks can be achieved by applying an IMV-LSTM based on time series data through an EHR-based DRN. This approach can help identify risk factors and enable early detection of adverse drug reactions when prescribing drugs that cause renal toxicity before AKI occurs.

Ultrasound-mediated delivery of doxorubicin to the brain results in immune modulation and improved responses to PD-1 blockade in gliomas.

Given the marginal penetration of most drugs across the blood-brain barrier, the efficacy of various agents remains limited for glioblastoma (GBM). Here we employ low-intensity pulsed ultrasound (LIPU) and intravenously administered microbubbles (MB) to open the blood-brain barrier and increase the concentration of liposomal doxorubicin and PD-1 blocking antibodies (aPD-1). We report results on a cohort of 4 GBM patients and preclinical models treated with this approach. LIPU/MB increases the concentration of doxorubicin by 2-fold and 3.9-fold in the human and murine brains two days after sonication, respectively. Similarly, LIPU/MB-mediated blood-brain barrier disruption leads to a 6-fold and a 2-fold increase in aPD-1 concentrations in murine brains and peritumoral brain regions from GBM patients treated with pembrolizumab, respectively. Doxorubicin and aPD-1 delivered with LIPU/MB upregulate major histocompatibility complex (MHC) class I and II in tumor cells. Increased brain concentrations of doxorubicin achieved by LIPU/MB elicit IFN-γ and MHC class I expression in microglia and macrophages. Doxorubicin and aPD-1 delivered with LIPU/MB results in the long-term survival of most glioma-bearing mice, which rely on myeloid cells and lymphocytes for their efficacy. Overall, this translational study supports the utility of LIPU/MB to potentiate the antitumoral activities of doxorubicin and aPD-1 for GBM.

Non-invasive prediction of massive transfusion during surgery using intraoperative hemodynamic monitoring data.

OBJECTIVE: Failure to receive prompt blood transfusion leads to severe complications if massive bleeding occurs during surgery. For the timely preparation of blood products, predicting the possibility of massive transfusion (MT) is essential to decrease morbidity and mortality. This study aimed to develop a model for predicting MT 10 min in advance using non-invasive bio-signal waveforms that change in real-time. METHODS: In this retrospective study, we developed a deep learning-based algorithm (DLA) to predict intraoperative MT within 10 min. MT was defined as the transfusion of 3 or more units of red blood cells within an hour. The datasets consisted of 18,135 patients who underwent surgery at Seoul National University Hospital (SNUH) for model development and internal validation and 621 patients who underwent surgery at the Boramae Medical Center (BMC) for external validation. We constructed the DLA by using features extracted from plethysmography (collected at 500 Hz) and hematocrit measured during surgery. RESULTS: Among 18,135 patients in SNUH and 621 patients in BMC, 265 patients (1.46%) and 14 patients (2.25%) received MT during surgery, respectively. The area under the receiver operating characteristic curve (AUROC) of DLA predicting intraoperative MT before 10 min was 0.962 (95% confidence interval [CI], 0.948-0.974) in internal validation and 0.922 (95% CI, 0.882-0.959) in external validation, respectively. CONCLUSION: The DLA can successfully predict intraoperative MT using non-invasive bio-signal waveforms.

Healthcare burden changes by restricted physical activities in lumbar spinal stenosis and spondylolisthesis: a retrospective large cohort study during the COVID-19 pandemic.

BACKGROUND: Lumbar spinal stenosis (LSS) and spondylolisthesis (SPL) are characterized as degenerative spinal pathologies and share considerable similarities. However, opinions vary on whether to recommend exercise or restrict it for these diseases. Few studies have objectively compared the effects of daily physical activity on LSS and SPL because it is impossible to restrict activities ethnically and practically. We investigated the effect of restricting physical activity due to social distancing (SoD) on LSS and SPL, focusing on the aspect of healthcare burden changes during the pandemic period. METHODS: We included first-visit patients diagnosed exclusively with LSS and SPL in 2017 and followed them up for two years before and after the implementation of the SoD policy. As controls, patients who first visited in 2015 and were followed for four years without SoD were analyzed. The common data model was employed to analyze each patient's diagnostic codes and treatments. Hospital visits and medical costs were analyzed by regression discontinuity in time to control for temporal effects on dependent variables. RESULTS: Among 33,484 patients, 2,615 with LSS and 446 with SPL were included. A significant decrease in hospital visits was observed in the LSS (difference, -3.94 times/month·100 patients; p = 0.023) and SPL (difference, -3.44 times/month·100 patients; p = 0.026) groups after SoD. This decrease was not observed in the data from the control group. Concerning medical costs, the LSS group showed a statistically significant reduction in median copayment (difference, -$45/month·patient; p < 0.001) after SoD, whereas a significant change was not observed in the SPL group (difference, -$19/month·patient; p = 0.160). CONCLUSION: Restricted physical activity during the SoD period decreased the healthcare burden for patients with LSS or, conversely, it did not significantly affect patients with SPL. Under circumstances of physical inactivity, patients with LSS may underrate their symptoms, while maintaining an appropriate activity level may be beneficial for patients with SPL.

Past, present, and future of cell replacement therapy for parkinson's disease: a novel emphasis on host immune responses.

Parkinson's disease (PD) stands as the second most common neurodegenerative disorder after Alzheimer's disease, and its prevalence continues to rise with the aging global population. Central to the pathophysiology of PD is the specific degeneration of midbrain dopamine neurons (mDANs) in the substantia nigra. Consequently, cell replacement therapy (CRT) has emerged as a promising treatment approach, initially supported by various open-label clinical studies employing fetal ventral mesencephalic (fVM) cells. Despite the initial favorable results, fVM cell therapy has intrinsic and logistical limitations that hinder its transition to a standard treatment for PD. Recent efforts in the field of cell therapy have shifted its focus towards the utilization of human pluripotent stem cells, including human embryonic stem cells and induced pluripotent stem cells, to surmount existing challenges. However, regardless of the transplantable cell sources (e.g., xenogeneic, allogeneic, or autologous), the poor and variable survival of implanted dopamine cells remains a major obstacle. Emerging evidence highlights the pivotal role of host immune responses following transplantation in influencing the survival of implanted mDANs, underscoring an important area for further research. In this comprehensive review, building upon insights derived from previous fVM transplantation studies, we delve into the functional ramifications of host immune responses on the survival and efficacy of grafted dopamine cells. Furthermore, we explore potential strategic approaches to modulate the host immune response, ultimately aiming for optimal outcomes in future clinical applications of CRT for PD.

Associations of MRI-derived kidney volume, kidney function, body composition and physical performance in ≈38 000 UK Biobank participants: a population-based observational study.

Background: Kidney volume is used as a predictive and therapeutic marker for several clinical conditions. However, there is a lack of large-scale studies examining the relationship between kidney volume and various clinicodemographic factors, including kidney function, body composition and physical performance. Methods: In this observational study, MRI-derived kidney volume measurements from 38 526 UK Biobank participants were analysed. Major kidney volume-related measures included body surface area (BSA)-adjusted total kidney volume (TKV) and the difference in bilateral kidneys. Multivariable-adjusted linear regression and cubic spline analyses were used to explore the association between kidney volume-related measures and clinicodemographic factors. Cox or logistic regression was used to identify the risks of death, non-kidney cancer, myocardial infarction, ischaemic stroke and chronic kidney disease (CKD). Results: The median of BSA-adjusted TKV and the difference in kidney volume were 141.9 ml/m2 [interquartile range (IQR) 128.1-156.9] and 1.08-fold (IQR 1.04-1.15), respectively. Higher BSA-adjusted TKV was significantly associated with higher estimated glomerular filtration rate {eGFR; ß = 0.43 [95% confidence interval (CI) 0.42-0.44]; P < .001}, greater muscle volume [ß = 0.50 (95% CI 0.48-0.51); P < .001] and greater mean handgrip strength [ß = 0.15 (95% CI 0.13-0.16); P < .001] but lower visceral adipose tissue volume [VAT; ß = -0.09 (95% CI -0.11 to -0.07); P < .001] in adjusted models. A greater difference in bilateral kidney volumes was associated with lower eGFR, muscle volume and physical performance but with higher proteinuria and VAT. Higher BSA-adjusted TKV was significantly associated with a reduced risk of CKD [odds ratio (OR) 0.7 (95% CI 0.63-0.77); P < .001], while a greater difference in kidney volume was significantly associated with an increased risk of CKD [OR 1.13 (95% CI 1.07-1.20); P < .001]. Conclusion: Higher BSA-adjusted TKV and lower differences in bilateral kidney volumes are associated with higher kidney function, muscle volume and physical performance and a reduced risk of CKD.

The complete mitochondrial genome of Hyperhalosydna striata (Kinberg, 1856) (Annelida: Polynoidae) collected from Jejudo Island, Korea.

The mitogenome sequence of Hyperhalosydna striata was determined for the first time in the present study. The genome is 15,226 bp long and contains 13 protein-coding genes (PCGs), two ribosomal RNA genes (rRNAs), and 22 transfer RNA genes (tRNA). The overall base composition was 28.0% A, 21.9% C, 13.0% G, and 37.1% T. A phylogenetic tree was constructed to infer the phylogenetic position of H. striata among polynoid species whose mitochondrial genome sequences are available in GenBank. Hyperhalosydna striata was closely related to the species of subfamily Lepidonotinae.

Continual learning framework for a multicenter study with an application to electrocardiogram.

Deep learning has been increasingly utilized in the medical field and achieved many goals. Since the size of data dominates the performance of deep learning, several medical institutions are conducting joint research to obtain as much data as possible. However, sharing data is usually prohibited owing to the risk of privacy invasion. Federated learning is a reasonable idea to train distributed multicenter data without direct access; however, a central server to merge and distribute models is needed, which is expensive and hardly approved due to various legal regulations. This paper proposes a continual learning framework for a multicenter study, which does not require a central server and can prevent catastrophic forgetting of previously trained knowledge. The proposed framework contains the continual learning method selection process, assuming that a single method is not omnipotent for all involved datasets in a real-world setting and that there could be a proper method to be selected for specific data. We utilized the fake data based on a generative adversarial network to evaluate methods prospectively, not ex post facto. We used four independent electrocardiogram datasets for a multicenter study and trained the arrhythmia detection model. Our proposed framework was evaluated against supervised and federated learning methods, as well as finetuning approaches that do not include any regulation to preserve previous knowledge. Even without a central server and access to the past data, our framework achieved stable performance (AUROC 0.897) across all involved datasets, achieving comparable performance to federated learning (AUROC 0.901).

Deep learning-based long-term risk evaluation of incident type 2 diabetes using electrocardiogram in a non-diabetic population: a retrospective, multicentre study.

Background: Diabetes is a major public health concern. We aimed to evaluate the long-term risk of incident type 2 diabetes in a non-diabetic population using a deep learning model (DLM) detecting prevalent type 2 diabetes using electrocardiogram (ECG). Methods: In this retrospective study, participants who underwent health checkups at two tertiary hospitals in Seoul, South Korea, between Jan 1, 2001 and Dec 31, 2022 were included. Type 2 diabetes was defined as glucose ≥126 mg/dL or glycated haemoglobin (HbA1c) ≥ 6.5%. For survival analysis on incident type 2 diabetes, we introduced an additional variable, diabetic ECG, which is determined by the DLM trained on ECG and corresponding prevalent diabetes. It was assumed that non-diabetic individuals with diabetic ECG had a higher risk of incident type 2 diabetes than those with non-diabetic ECG. The one-dimensional ResNet-based model was adopted for the DLM, and the Guided Grad-CAM was used to localise important regions of ECG. We divided the non-diabetic group into the diabetic ECG group (false positive) and the non-diabetic ECG (true negative) group according to the DLM decision, and performed a Cox proportional hazard model, considering the occurrence of type 2 diabetes more than six months after the visit. Findings: 190,581 individuals were included in the study with a median follow-up period of 11.84 years. The areas under the receiver operating characteristic curve for prevalent type 2 diabetes detection were 0.816 (0.807-0.825) and 0.762 (0.754-0.770) for the internal and external validations, respectively. The model primarily focused on the QRS duration and, occasionally, P or T waves. The diabetic ECG group exhibited an increased risk of incident type 2 diabetes compared with the non-diabetic ECG group, with hazard ratios of 2.15 (1.82-2.53) and 1.92 (1.74-2.11) for internal and external validation, respectively. Interpretation: In the non-diabetic group, those whose ECG was classified as diabetes by the DLM were at a higher risk of incident type 2 diabetes than those whose ECG was not. Additional clinical research on the relationship between the phenotype of ECG and diabetes to support the results and further investigation with tracked data and various ECG recording systems are suggested for future works. Funding: National Research Foundation of Korea.

Paclitaxel and Carboplatin in Combination with Low-intensity Pulsed Ultrasound for Glioblastoma.

PURPOSE: We recently reported on clinical trials for patients with recurrent glioblastoma where low-intensity pulsed ultrasound and microbubbles (LIPU/MB) improved paclitaxel or carboplatin delivery into the brain. Here, we report variable local tumor control with paclitaxel at the maximal/target dose in our phase I trial (NCT04528680). To address this, we investigated the combination of paclitaxel with carboplatin in preclinical glioma models. EXPERIMENTAL DESIGN: We performed MRI-based analysis to evaluate disease control in patients from our trial. We studied the cytotoxicity of paclitaxel and carboplatin against 11 human glioma lines as monotherapy and in combination at concentrations derived from human intraoperative studies. Synergy was assessed with the Loewe model and the survival benefit evaluated in two xenografts. We examined the effects on cell cycle progression, DNA damage, and apoptosis. RESULTS: Patients treated with paclitaxel and LIPU/MB exhibited variable local tumor control, which correlated with overall survival. We observed limited cross-resistance to paclitaxel and carboplatin in glioma lines, with almost a third of them being exclusively susceptible to one drug. This combination led to susceptibility of 81% of lines and synergy in 55% of them. The combination proved more efficacious in two intracranial xenografts when administered with LIPU/MB, leading to complementary effects on cell cycle arrest. CONCLUSIONS: Combining paclitaxel and carboplatin in gliomas may be more efficacious than monotherapy, as in other cancers, due to synergy and independent susceptibility to each drug. These results form the basis for an ongoing phase II trial (NCT04528680) where we investigate this combination with LIPU/MB.

The Risk of COVID-19 and Its Outcomes in Korean Patients With Gout: A Multicenter, Retrospective, Observational Study.

This retrospective cohort study aimed to compare coronavirus disease 2019 (COVID-19)-related clinical outcomes between patients with and without gout. Electronic health record-based data from two centers (Seoul National University Hospital [SNUH] and Boramae Medical Center [BMC]), from January 2021 to April 2022, were mapped to a common data model. Patients with and without gout were matched using a large-scale propensity-score algorithm based on population-level estimation methods. At the SNUH, the risk for COVID-19 diagnosis was not significantly different between patients with and without gout (hazard ratio [HR], 1.07; 95% confidence interval [CI], 0.59-1.84). Within 30 days after COVID-19 diagnosis, no significant difference was observed in terms of hospitalization (HR, 0.57; 95% CI, 0.03-3.90), severe outcomes (HR, 2.90; 95% CI, 0.54-13.71), or mortality (HR, 1.35; 95% CI, 0.06-16.24). Similar results were obtained from the BMC database, suggesting that gout does not increase the risk for COVID-19 diagnosis or severe outcomes.

Impregnation of Activated Carbon with Organic Phase-Change Material.

In this study, we developed a thermal storage medium comprising porous activated carbon filled with organic phase-change materials (PCMs) that utilizes the latent heat of phase-change to absorb heat during heating and release heat during cooling. For the activated carbon, we used both charcoal-based powdered activated carbon (250-350 mesh) and granular activated carbon. The organic phase-change materials used in the experiments were dodecane, tridecane, tetradecane, and pentadecane. Material properties such as thermal conductivity, latent heat, and melting temperature range were evaluated experimentally and theoretically, with the results observed to be consistent. The cyclic thermal performance of the proposed medium was also evaluated. Notably, filling the activated carbon with a mixture of organic PCMs resulted in the highest temperature-moderating effect. The procedure and results presented in this study are expected to aid in further improvement in the performance of thermal storage media containing PCM where stable temperatures are required, including building heating and cooling.

Expression Profiles of Hypoxia-Related Genes of Cancers Originating from Anatomically Similar Locations Using TCGA Database Analysis.

Background: Hypoxia is a well-recognized characteristic of the tumor microenvironment of solid cancers. This study aimed to analyze hypoxia-related genes shared by groups based on tumor location. Methods: A total of 9 hypoxia-related pathways from the Kyoto Encyclopedia of Genes and Genomes database or the Reactome database were selected, and 850 hypoxia-related genes were analyzed. Based on their anatomical locations, 14 tumor types were categorized into 6 groups. The group-specific genetic risk score was classified as high- or low-risk based on mRNA expression, and survival outcomes were evaluated. Results: The risk scores in the Female Reproductive group and the Lung group were internally and externally validated. In the Female Reproductive group, CDKN2A, FN1, and ITGA5 were identified as hub genes associated with poor prognosis, while IL2RB and LEF1 were associated with favorable prognosis. In the Lung group, ITGB1 and LDHA were associated with poor prognosis, and GLS2 was associated with favorable prognosis. Functional enrichment analysis showed that the Female Reproductive group was enriched in relation to cilia and skin, while the Lung group was enriched in relation to cytokines and defense. Conclusions: This analysis may lead to better understanding of the mechanisms of cancer progression and facilitate establishing new biomarkers for prognosis prediction.