Nomogram for predicting gastric cancer recurrence using biomarker gene expression

Machine learning-based integration develops a disulfidptosis-related lncRNA signature for improving outcomes in gastric cancer

Gastric cancer remains one of the deadliest cancers globally due to delayed detection and limited treatment options, underscoring the critical need for innovative prognostic methods. Disulfidptosis, a recently discovered programmed cell death triggered by disulphide stress, presents a fresh avenue for therapeutic exploration. This research examines disulfidptosis-related long noncoding RNAs (DRLs) in gastric cancer, with the goal of leveraging these lncRNAs as potential markers to enhance patient outcomes and treatment approaches. Comprehensive genomic and clinical data from stomach adenocarcinoma (STAD) were obtained from The Cancer Genome Atlas (TCGA). Employing least absolute shrinkage and selection operator (LASSO) regression analysis, a prognostic model was devised incorporating five key DRLs to forecast survival rates. The effectiveness of this model was validated using Kaplan-Meier survival plots, receiver operating characteristic (ROC) curves, and extensive functional enrichment studies. The importance of select lncRNAs and the expression variability of genes tied to disulfidptosis were validated via quantitative real-time PCR (qRT-PCR) and Western blot tests, establishing a solid foundation for their prognostic utility. Analyses of functional enrichment and tumour mutation burden highlighted the biological importance of these DRLs, connecting them to critical cancer pathways and immune responses. These discoveries broaden our comprehension of the molecular framework of gastric cancer and bolster the development of tailored treatment plans, highlighting the substantial role of DRLs in clinical prognosis and therapeutic intervention.

Development and validation of a nomogram for predicting survival in gastric signet ring cell carcinoma patients treated with radiotherapy

There is no effective clinical prediction model to predict the prognosis of gastric signet ring cell carcinoma (GSRC) patients treated with radiotherapy. This study retrospectively analyzed the clinical data of 20-80-year-old patients diagnosed with GSRC between 2004 and 2019 from the Surveillance, Epidemiology, and End Results (SEER) database. Using Cox regression analyses revealed independent prognostic factors, and a nomogram was constructed. The C-index, net reclassification index (NRI) and integrated discrimination improvement (IDI) of the nomogram were greater than those of the TNM staging system for predicting OS, indicating that the nomogram predicted prognosis with greater accuracy. The area under the curve (AUC) values were 0.725, 0.753 and 0.745 for the training group; 0.725, 0.763 and 0.752 for the internal validation group; and 0.795, 0.764 and 0.765 for the external validation group, respectively. Calibration plots demonstrated high agreement between the nomogram's prediction and the actual observations. The risk stratification system was able to accurately stratify patients who underwent radiotherapy for GSRC into high- and low-risk subgroups, with significant differences in prognosis. The Kaplan‒Meier survival analysis according to different treatments indicated that surgery combined with chemoradiotherapy is a more effective treatment strategy for improving OS in for GSRC patients. The nomogram is sufficiently accurate to predict the prognostic factors of GSRC receiving radiotherapy, allowing for clinicians to predict the 1-, 3-, and 5-year OS.

FOXP3 inhibits proliferation and migration by competitively inhibiting YAP1 in nasopharyngeal carcinoma

Hippo signalling is involved in the coordination of extracellular signals that control tissue homeostasis and organ size. Yes-associated protein 1 (YAP1) is regulated primarily by Hippo signalling through coactivation of transcription factors with GATA domains called TEADs. However, small-molecule orthosteric inhibitors of YAP1 are difficult to develop due to its tight binding to TEAD4 via a flat interface. Previous studies have shown that chlorpromazine (CPZ) can inhibit YAP1 expression. MTT, colony formation, wound healing, Transwell migration and Western blot assays were performed to explore how CPZ affects nasopharyngeal carcinoma (NPC) cells through FOXP3. In addition, immunofluorescence and live-cell imaging were used to detect YAP1 intracellular localization after CPZ administration. Through the HDOCK website, we predicted protein binding regions between FOXP3 and TEAD4. Western blot and co-IP experiments were used to verify the relationship between FOXP3 and YAP1. The UCSC Xena database, LinkedOmics database and KM plotter website were used to assess the prognostic value of FOXP3 in head and neck squamous cell carcinoma (HNSCC). Age, sex, pathological tumour-node-metastasis (pTMN) stage, grade, smoking status and FOXP3 expression were included in an overall survival nomogram model. Our findings revealed that FOXP3 has the ability to competitively interacts competitively with TEAD4 to inhibit YAP1 expression. By increasing FOXP3 expression, CPZ induces YAP1 nuclear export and phosphorylation, consequently suppressing NPC cell proliferation and migration. Collectively, our findings indicate that FOXP3 competitively binds TEAD4 to regulate YAP1 localization in the nucleus and cytoplasm to suppress NPC progression. Consequently, FOXP3 may be a prognostic indicator for HNSCC.

The role of the SOX2 gene in cervical cancer: focus on ferroptosis and construction of a predictive model

BACKGROUND

The intricate interplay between stemness markers and cell death pathways significantly influences the pathophysiology of cervical cancer. SOX2, a pivotal regulator of stem cell pluripotency, has recently been implicated in the modulation of ferroptosis, a specialized form of iron-dependent cell death, in cancer dynamics. This study delineates the role of SOX2 in the ferroptotic landscape of cervical carcinoma.

OBJECTIVE

To delineate the association between SOX2 expression and ferroptosis in cervical cancer and develop a robust, SOX2-centric model for predicting prognosis and enhancing personalized treatment.

METHODS

A multidimensional approach integrating advanced bioinformatics, comprehensive molecular profiling, and state-of-the-art machine learning algorithms was employed to assess SOX2 expression patterns and their correlation with ferroptosis marker expression patterns in cervical cancer tissues. A prognostic model incorporating the expression levels of SOX2 and ferroptosis indicators was meticulously constructed.

RESULTS

This investigation revealed a profound and intricate correlation between SOX2 expression and ferroptotic processes in cervical cancer, substantiated by robust molecular evidence. The developed predictive model based on SOX2 expression exhibited superior prognostic accuracy and may guide therapeutic decision-making.

CONCLUSION

This study underscores the critical role of SOX2 in orchestrating the ferroptosis pathway in cervical cancer and presents a novel prognostic framework. The SOX2-centric predictive model represents a significant advancement in prognosis evaluation, offering a gateway to personalized treatment for gynaecologic cancers.

Computed tomography (CT) derived radiomics to predict post-operative disease recurrence in gastric cancer; a systematic review and meta-analysis

INTRODUCTION

Radiomics offers the potential to predict oncological outcomes from pre-operative imaging in order to identify 'high risk' patients at increased risk of recurrence. The application of radiomics in predicting disease recurrence provides tailoring of therapeutic strategies. We aim to comprehensively assess the existing literature regarding the current role of radiomics as a predictor of disease recurrence in gastric cancer.

METHODS

A systematic search was conducted in Medline, EMBASE, and Web of Science databases. Inclusion criteria encompassed retrospective and prospective studies investigating the use of radiomics to predict post-operative recurrence in ovarian cancer. Study quality was assessed using the QUADAS-2 and Radiomics Quality Score tools.

RESULTS

Nine studies met the inclusion criteria, involving a total of 6,662 participants. Radiomic-based nomograms demonstrated consistent performance in predicting disease recurrence, as evidenced by satisfactory area under the receiver operating characteristic curve values (AUC range 0.72 - 1). The pooled AUCs calculated using the inverse-variance method for both the training and validation datasets were 0.819 and 0.789 respectively CONCLUSION: Our review provides good evidence supporting the role of radiomics as a predictor of post-operative disease recurrence in gastric cancer. Included studies noted good performance in predicting their primary outcome. Radiomics may enhance personalised medicine by tailoring treatment decision based on predicted prognosis.

Unlocking the Potential of Disulfidptosis-Related LncRNAs in Lung Adenocarcinoma: A Promising Prognostic LncRNA Model for Survival and Immunotherapy Prediction

OBJECTIVE

Disulfidptosis was stimulated in high SLC7A11 expression cells starving to glucose. We attempted to identify disulfidptosis-related lncRNAs (DRLs), built a prognostic model to predict survival, and analyzed the tumor microenvironment.

METHODS

The TCGA database was utilized to procure the pertinent data. By utilizing both Cox regression and the least absolute shrinkage and selection operator (LASSO) method, a risk model based on DRLs was formulated for prognostic evaluation. The ability of survival prediction was validated by multiple approaches. The biological functions were screened through GO, KEGG, and GSEA. Various methods were employed to evaluate the tumor immune environment, which included ESTIMATE, tumor mutation burden (TMB) score, CIBERSORT algorithm, and tumor immune dysfunction and exclusion (TIDE) score.

RESULTS

Ninety-one DRLs were recognized, and lncRNA AC092718.4, AL365181.2, AL606489.1, EMSLR, and ENTPD3-AS1 were involved in the risk model. The GEO database was used to verify the influence of these lncRNAs on survival. The following analyses showed that survival could be predicted excellently by the DRLs risk model. The results of enrichment analyses pointed toward the involvement of the cell cycle and IgA production pathways. In the low-risk patient group, there was a notable surge in stromal, immune, and ESTIMATE scores, while the TMB scores took a tumble. Conversely, the high-risk patient group displayed a converse trend. Notably, the group of patients with lower risk scores and higher TMB scores showed the most favorable survival outcomes, underscoring the importance of considering both risk score and TMB in predicting the response to immune checkpoint blockade therapy. Furthermore, patients classified as high-risk might display resistance to both chemotherapy and targeted therapy. Cellular biological experiments proved that lncRNA AC092718.4 promoted invasion, migration, and proliferation abilities in vitro. These results provided valuable insights into the role of DRLs in LUAD and presented a possible effective treatment approach for LUAD.

CONCLUSIONS

We developed a disulfidptosis-related risk model with 5 lncRNAs that enables survival prediciton for LUAD patients and aids cilinical decisions by forecasting the TME, TMB, and drug sensitivity, making it a valuable tool for outcomes prediction.

UBE2C as an Immune-Related Biomarker for Breast Cancer: A Study Based on Multiple Databases

OBJECTIVES

To screen the target genes that are associated with survival of breast cancer (BRCA) and explore their prognostic values and immune correlations with BRCA using multiple databases..

METHODS

The microarray expression datasets of BRCA were downloaded from the Gene Expresssion Omnibus database (GEO) and analyzed to obtain differentially expressed genes (DEGs). Hub genes were obtained by constructing and visualizing the protein-protein interaction network of DEGs. The key gene was determined using R language, STRING, and Cytoscape, and the differential expression of the key gene was verified using external datasets The Cancer Genome Atlas (TCGA) and quantitative real-time PCR (qRT-PCR) for BRCA tissues of 37 patients. The prognostic value and immunological correlation of UBE2C in BRCA were explored using R language, TIMER, and Gene Set Enrichment Analysis (GSEA).

RESULTS

Of 10 hub genes seleceed from 302 DEGS, UBE2C was identified as the gene associated with BRCA survival. The expression of UBE2C was differentially upregulated in BRCA, as verified by TCGA and qRT-PCR. Prognostic analysis revealed that UBE2C served as an independent prognostic factor. High expression of UBE2C was associated with decreased immune infiltration levels of B cells, CD4+ T cells, CD8+ T cells, macrophages, and myeloid dendritic cells in BRCA tissue. The expression of UBE2C in BRCA showed a significant correlation with immune checkpoints genes PDCD1, CD274, and CTLA4 expressions. There was a positive correlation between the expression of UBE2C and the tumor mutational burden and microsatellite instability. GSEA demonstrated that UBE2C expression significantly enriched 786 immune-related gene sets.

CONCLUSIONS

UBE2C expression in BRCA tissues is closely related to the BRCA immune microenvironment and showes predictive values on the survivals and prognosis of BRCA patients and the effecacy of immunotherapy. UBE2C may be an potential immune-related prognostic biomarker for BRCA.

Development of a prognostic nomogram for esophageal squamous cell carcinoma patients received radiotherapy based on clinical risk factors

Purpose

The goal of the study was to create a nomogram based on clinical risk factors to forecast the rate of locoregional recurrence-free survival (LRFS) in patients with esophageal squamous cell carcinoma (ESCC) who underwent radiotherapy (RT).

Methods

In this study, 574 ESCC patients were selected as participants. Following radiotherapy, subjects were divided into training and validation groups at a 7:3 ratio. The nomogram was established in the training group using Cox regression. Performance validation was conducted in the validation group, assessing predictability through the C-index and AUC curve, calibration via the Hosmer-Lemeshow (H-L) test, and evaluating clinical applicability using decision curve analysis (DCA).

Results

T stage, N stage, gross tumor volume (GTV) dose, location, maximal wall thickness (MWT) after RT, node size (NS) after RT, Δ computer tomography (CT) value, and chemotherapy were found to be independent risk factors that impacted LRFS by multivariate cox analysis, and the findings could be utilized to create a nomogram and forecast LRFS. the area under the receiver operating characteristic (AUC) curve and C-index show that for training and validation groups, the prediction result of LRFS using nomogram was more accurate than that of TNM. The LRFS in both groups was consistent with the nomogram according to the H-L test. The DCA curve demonstrated that the nomogram had a good prediction effect both in the groups for training and validation. The nomogram was used to assign ESCC patients to three risk levels: low, medium, or high. There were substantial variations in LRFS between risk categories in both the training and validation groups (p<0.001, p=0.003).

Conclusions

For ESCC patients who received radiotherapy, the nomogram based on clinical risk factors could reliably predict the LRFS.

Developing and Validating a Nomogram Model for Predicting Ischemic Stroke Risk

Background and purpose: Clinically, the ability to identify individuals at risk of ischemic stroke remains limited. This study aimed to develop a nomogram model for predicting the risk of acute ischemic stroke. Methods: In this study, we conducted a retrospective analysis on patients who visited the Department of Neurology, collecting important information including clinical records, demographic characteristics, and complete hematological tests. Participants were randomly divided into training and internal validation sets in a 7:3 ratio. Based on their diagnosis, patients were categorized as having or not having ischemic stroke (ischemic and non-ischemic stroke groups). Subsequently, in the training set, key predictive variables were identified through multivariate logistic regression and least absolute shrinkage and selection operator (LASSO) regression methods, and a nomogram model was constructed accordingly. The model was then evaluated on the internal validation set and an independent external validation set through area under the receiver operating characteristic curve (AUC-ROC) analysis, a Hosmer-Lemeshow goodness-of-fit test, and decision curve analysis (DCA) to verify its predictive efficacy and clinical applicability. Results: Eight predictors were identified: age, smoking status, hypertension, diabetes, atrial fibrillation, stroke history, white blood cell count, and vitamin B12 levels. Based on these factors, a nomogram with high predictive accuracy was constructed. The model demonstrated good predictive performance, with an AUC-ROC of 0.760 (95% confidence interval [CI]: 0.736-0.784). The AUC-ROC values for internal and external validation were 0.768 (95% CI: 0.732-0.804) and 0.732 (95% CI: 0.688-0.777), respectively, proving the model's capability to predict the risk of ischemic stroke effectively. Calibration and DCA confirmed its clinical value. Conclusions: We constructed a nomogram based on eight variables, effectively quantifying the risk of ischemic stroke.

RRP8, associated with immune infiltration, is a prospective therapeutic target in hepatocellular carcinoma

BACKGROUND

Ribosomal RNA Processing 8 (RRP8) is a nucleolar Rossman fold-like methyltransferase that exhibits increased expression in many malignant tumours. However, the role of RRP8 in hepatocellular carcinoma (HCC) is still uncertain. We explored the relationships between RRP8 and prognosis and immune infiltration, as well as the putative pathological function and mechanism of RRP8 in HCC.

METHODS

Analysis of RRP8 expression across cancers was performed by using multiple databases. Associations between RRP8 expression and clinicopathological factors were further examined. Gene enrichment analysis was used to identify various putative biological activities and regulatory networks of RRP8 in HCC. The relationship between RRP8 expression and immune infiltration was confirmed by single-sample gene set enrichment analysis (ssGSEA). Univariate and multivariate Cox regression analyses were conducted to assess the impact of clinical variables on patient outcomes. Furthermore, a nomogram was constructed to estimate survival probability based on multivariate Cox regression analysis. Functional validation of RRP8 in HCC was performed with two different systems: doxycycline-inducible shRNA knockdown and CRISPR-Cas9 knockout.

RESULTS

RRP8 was markedly overexpressed in HCC clinical specimens compared to adjacent normal tissues. Further analysis demonstrated that RRP8 was directly connected to multiple clinical characteristics and strongly associated with various immune markers in HCC. Moreover, elevated RRP8 expression indicated an unfavourable prognosis. Our functional studies revealed that both knockdown and knockout of RRP8 dramatically attenuated liver cancer cells to proliferate and migrate. Knockout of RRP8 decreased the phosphorylation of MEK1/2 and β-catenin-(Y654) signalling pathway components; downregulated downstream signalling effectors, including Cyclin D1 and N-cadherin; and upregulated E-cadherin.

CONCLUSIONS

RRP8 is strongly implicated in immune infiltration and could be a potential therapeutic target in HCC.

Immunotherapy and drug sensitivity predictive roles of a novel prognostic model in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most significant causes of cancer-related deaths in the worldwide. Currently, predicting the survival of patients with HCC and developing treatment drugs still remain a significant challenge. In this study, we employed prognosis-related genes to develop and externally validate a predictive risk model. Furthermore, the correlation between signaling pathways, immune cell infiltration, immunotherapy response, drug sensitivity, and risk score was investigated using different algorithm platforms in HCC. Our results showed that 11 differentially expressed genes including UBE2C, PTTG1, TOP2A, SPP1, FCN3, SLC22A1, ADH4, CYP2C8, SLC10A1, F9, and FBP1 were identified as being related to prognosis, which were integrated to construct a prediction model. Our model could accurately predict patients' overall survival using both internal and external datasets. Moreover, a strong correlation was revealed between the signaling pathway, immune cell infiltration, immunotherapy response, and risk score. Importantly, a novel potential drug candidate for HCC treatment was discovered based on the risk score and also validated through ex vivo experiments. Our finds offer a novel perspective on prognosis prediction and drug exploration for cancer patients.

ALYREF m5C RNA methylation reader predicts bladder cancer prognosis by regulating the tumor immune microenvironment

BACKGROUND

5-Methylcytidine (m5C) methylation is a recently emerging epigenetic modification that is closely related to tumor proliferation, occurrence, and metastasis. This study aimed to investigate the clinicopathological characteristics and prognostic value of m5C regulators in bladder cancer (BLCA), and their correlation with the tumor immune microenvironment.

METHODS

Thirteen m5C RNA methylation regulators were analyzed using RNA-sequencing and corresponding clinical information obtained from the TCGA database. The Cluster Profiler package was used to analyze the gene ontology function of potential targets and enriched the Kyoto Encyclopedia of Genes and Genomes pathway. Kaplan-Meier survival analysis was used to compare survival differences using the log-rank test and univariate Cox proportional hazards regression. The correlation between signature prognostic m5C regulators and various immune cells was analyzed. Univariate and multivariate Cox regression analyses identified independence of the ALYREF gene signature.

RESULTS

Nine out of the 13 m5C RNA methylation regulators were differentially expressed in BLCA and normal samples and were co-expressed. These 9 regulators were associated with clinicopathological tumor characteristics, particularly high or low tumor risk, pT or pTNM stage, and migration. Consensus clustering analysis divides the BLCA samples into 4 clusters. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment annotation and gene ontology function analysis identified 273 upregulated and 594 downregulated genes in BLCA. Notably, only ALYREF was significantly correlated with OS (P < .05). ALYREF exhibited significant infiltration levels in macrophage cells. Therefore, we constructed a nomogram for ALYREF as an independent prognostic factor. Additionally, we observed that both the mRNA and protein levels of ALYREF were upregulated, and immunofluorescence showed that ALYREF was mainly distributed in nuclear speckles. ALYREF overexpression was significantly associated with poor OS.

CONCLUSION

Our findings demonstrated the potential of ALYREF to predict clinical prognostic risks in BLCA patients and regulate the tumor immune microenvironment. As such, ALYREF may serve as a novel prognostic indicator in BLCA patients.

Predictive modeling for eosinophilic chronic rhinosinusitis: Nomogram and four machine learning approaches

Eosinophilic chronic rhinosinusitis (ECRS) is a distinct subset of chronic rhinosinusitis characterized by heightened eosinophilic infiltration and increased symptom severity, often resisting standard treatments. Traditional diagnosis requires invasive histological evaluation. This study aims to develop predictive models for ECRS based on patient clinical parameters, eliminating the need for invasive biopsy. Utilizing logistic regression with lasso regularization, random forest (RF), gradient-boosted decision tree (GBDT), and deep neural network (DNN), we trained models on common clinical data. The predictive performance was evaluated using metrics such as area under the curve (AUC) for receiver operator characteristics, decision curves, and feature ranking analysis. In a cohort of 437 eligible patients, the models identified peripheral blood eosinophil ratio, absolute peripheral blood eosinophil, and the ethmoidal/maxillary sinus density ratio (E/M) on computed tomography as crucial predictors for ECRS. This predictive model offers a valuable tool for identifying ECRS without resorting to histological biopsy, enhancing clinical decision-making.

Development and validation of a nomogram for predicting suicide risk and prognostic factors in bladder cancer patients following diagnosis: A population-based retrospective study

This study sought to identify independent risk factors associated with suicide following a diagnosis of bladder cancer and to develop a predictive model with the potential to contribute to suicide rate reduction. Harnessing data from the Surveillance, Epidemiology, and End Results (SEER) database, we identified bladder cancer patients diagnosed between 2004 and 2015, randomly assigning them to training and validation cohorts. The Cox proportional hazard model was employed to identify relevant predictors, leading to the construction of prediction nomogram models. Validation of prognostic nomograms involved assessing the consistency index (C-index), receiver operating characteristic (ROC) curve, and calibration curve. A total of 109,961 eligible bladder cancer patients were enrolled, randomly divided into training and validation sets. Multivariate Cox regression analysis revealed that sex, marital status, tumor local status (T Stage), and lymph node metastatic conditions (N Stage) were independent predictors for suicide in bladder cancer patients. Evaluation of the nomogram's accuracy through the C-index and ROC curve demonstrated acceptable performance in both training and validation sets. Moreover, the calibration plot indicated moderate accuracy of the nomogram in both datasets. Overall, this study successfully identified risk factors for suicide among bladder cancer patients and developed a nomogram, offering individualized diagnosis, intervention, and risk assessment to mitigate the risk of suicide in this patient population.

Risk Factors and Predictive Nomogram for Carbapenem-Resistant Klebsiella pneumoniae in Children in a Grade 3 First-Class General Hospital in Central China

Background

This study determined risk factors for Carbapenem-resistant Klebsiella pneumoniae (CRKP)in children admitted to a grade 3 first-class general hospital and developed an individualized line graph predictive model.

Methods

The clinical data of 185 children infected with Klebsiella pneumoniae from January 2015 to December 2019 were analyzed retrospectively. Patients were grouped according to carbapenem resistance: CRKP group (50 cases) and CSKP (carbapenem-sensitive Klebsiella pneumoniae) group (135 cases). Risk factors for CRKP in children were screened by logistic regression analysis. The predictive model was established using R software and validated using the Bootstrap method.

Results

Age (odds ratio [OR]=0.104, 95% confidence interval [CI]: 0.026-0.408), intensive care unit admission (OR =2.829, 95% CI: 1.138-7.030), mechanical ventilation (OR =7.510, 95% CI: 3.140-17.961), surgery history (OR =5.005, 95% CI: 1.507-16.618) and glucocorticoid (OR =0.235, 95% CI: 0.099-0.557) were independent risk factors for CRKP in children (P < 0.05), The total risk score of each factor was 362.5, and the risk rate was 0.1-0.9. In receiver-operating characteristic curve analysis, the area under the curve of CRKP predicted by the total risk score was 0.872 (95% CI=0.844-0.901; P < 0.001). The correction curve indicated that the consistency between the observed value and the predicted value was good.

Discussion and Conclusion

This study successfully established a model based on the risk factors, with high accuracy and good predictive value for CRKP in children. Hospitals should take necessary preventive measures against the risk factors for drug-resistant bacteria, such as optimizing the configuration of ICU space, timely isolation of infected children, and adequate disinfection of ICU equipment. Which may reduce CRKP infection rate.

Prediction of injury localization in preoperative patients with gastrointestinal perforation: a multiomics model analysis

BACKGROUND

The location of gastrointestinal perforation is essential for severity evaluation and optimizing the treatment approach. We aimed to retrospectively analyze the clinical characteristics, laboratory parameters, and imaging features of patients with gastrointestinal perforation and construct a predictive model to distinguish the location of upper and lower gastrointestinal perforation.

METHODS

A total of 367 patients with gastrointestinal perforation admitted to the department of emergency surgery in Fujian Medical University Union Hospital between March 2014 and December 2020 were collected. Patients were randomly divided into training set and test set in a ratio of 7:3 to establish and verify the prediction model by logistic regression. The receiver operating characteristic curve, calibration map, and clinical decision curve were used to evaluate the discrimination, calibration, and clinical applicability of the prediction model, respectively. The multiomics model was validated by stratification analysis in the prediction of severity and prognosis of patients with gastrointestinal perforation.

RESULTS

The following variables were identified as independent predictors in lower gastrointestinal perforation: monocyte absolute value, mean platelet volume, albumin, fibrinogen, pain duration, rebound tenderness, free air in peritoneal cavity by univariate logistic regression analysis and stepwise regression analysis. The area under the receiver operating characteristic curve of the prediction model was 0.886 (95% confidence interval, 0.840-0.933). The calibration curve shows that the prediction accuracy and the calibration ability of the prediction model are effective. Meanwhile, the decision curve results show that the net benefits of the training and test sets are greater than those of the two extreme models as the threshold probability is 20-100%. The multiomics model score can be calculated via nomogram. The higher the stratification of risk score array, the higher the number of transferred patients who were admitted to the intensive care unit (P < 0.001).

CONCLUSION

The developed multiomics model including monocyte absolute value, mean platelet volume, albumin, fibrinogen, pain duration, rebound tenderness, and free air in the peritoneal cavity has good discrimination and calibration. This model can assist surgeons in distinguishing between upper and lower gastrointestinal perforation and to assess the severity of the condition.

Mortality patterns of patients with tonsillar squamous cell carcinoma: a population-based study

Objective

Tonsillar squamous cell carcinoma (TSCC) and second primary malignancies (SPMs) are the most common causes of mortality in patients with primary TSCC. However, the competing data on TSCC-specific death (TSD) or SPM-related death in patients with TSCC have not been evaluated. This study aimed to analyze the mortality patterns and formulate prediction models of mortality risk caused by TSCC and SPMs.

Methods

Data on patients with a first diagnosis of TSCC were extracted as the training cohort from the 18 registries comprising the Surveillance, Epidemiology, and End Results (SEER) database. A competing risk approach of cumulation incidence function was used to estimate cumulative incidence curves. Fine and gray proportional sub-distributed hazard model analyses were performed to investigate the risk factors of TSD and SPMs. A nomogram was developed to predict the 5- and 10-year risk probabilities of death caused by TSCC and SPMs. Moreover, data from the 22 registries of the SEER database were also extracted to validate the nomograms.

Results

In the training cohort, we identified 14,530 patients with primary TSCC, with TSCC (46.84%) as the leading cause of death, followed by SPMs (26.86%) among all causes of death. In the proportion of SPMs, the lungs and bronchus (22.64%) were the most common sites for SPM-related deaths, followed by the larynx (9.99%), esophagus (8.46%), and Non-Melanoma skin (6.82%). Multivariate competing risk model showed that age, ethnicity, marital status, primary site, summary stage, radiotherapy, and surgery were independently associated with mortality caused by TSCC and SPMs. Such risk factors were selected to formulate prognostic nomograms. The nomograms showed preferable discrimination and calibration in both the training and validation cohorts.

Conclusion

Patients with primary TSCC have a high mortality risk of SPMs, and the competing risk nomogram has an ideal performance for predicting TSD and SPMs-related mortality. Routine follow-up care for TSCC survivors should be expanded to monitor SPMs.

Prognostic role of long non-coding RNA USP30-AS1 in ovarian cancer: insights into immune cell infiltration in the tumor microenvironment

Ovarian cancer represents a formidable gynecologic malignancy bearing a dismal prognosis owing to the dearth of reliable early detection approaches and a high recurrence rate. Long non-coding RNAs (lncRNAs) have garnered immense attention as key orchestrators involved in diverse biological processes and take part in cancer initiation and progression. The present study investigated the potential significance of lncRNA USP30-AS1 in ovarian cancer prognosis, as well as its putative association with immune cell infiltration in tumor immune microenvironment (TIME). By analyzing publicly available datasets, we identified six lncRNAs with prognostic prediction ability, including USP30-AS1. The results revealed a significant positive correlation of USP30-AS1 expression with the infiltration of immune cells such as Th1 cells, TFH, CD8 T cells, B cells, antigen-presenting dendritic cells (aDC), and plasmacytoid dendritic cells (pDC) in ovarian cancer specimens. These findings provide compelling evidence of the potential involvement of lncRNA in the regulation of the TME in ovarian carcinoma. The outcomes from this study underscore the potential of USP30-AS1 as a promising prognostic biomarker for ovarian cancer. Additionally, the findings offer significant insights into the plausible role of lncRNAs in modulating immune activities, thus adding to our understanding of the disease biology. Additional investigations are necessary to unravel the molecular mechanisms underpinning these connections and validate the results seen in independent cohorts and experimental models.

Pharmacogenomic Analysis of Combined Therapies against Glioblastoma Based on Cell Markers from Single-Cell Sequencing

Glioblastoma is the most common and aggressive form of primary brain cancer and the lack of viable treatment options has created an urgency to develop novel treatments. Personalized or predictive medicine is still in its infancy stage at present. This research aimed to discover biomarkers to inform disease progression and to develop personalized prophylactic and therapeutic strategies by combining state-of-the-art technologies such as single-cell RNA sequencing, systems pharmacology, and a polypharmacological approach. As predicted in the pyroptosis-related gene (PRG) transcription factor (TF) microRNA (miRNA) regulatory network, TP53 was the hub gene in the pyroptosis process in glioblastoma (GBM). A LASSO Cox regression model of pyroptosis-related genes was built to accurately and conveniently predict the one-, two-, and three-year overall survival rates of GBM patients. The top-scoring five natural compounds were parthenolide, rutin, baeomycesic acid, luteolin, and kaempferol, which have NFKB inhibition, antioxidant, lipoxygenase inhibition, glucosidase inhibition, and estrogen receptor agonism properties, respectively. In contrast, the analysis of the cell-type-specific differential expression-related targets of natural compounds showed that the top five subtype cells targeted by natural compounds were endothelial cells, microglia/macrophages, oligodendrocytes, dendritic cells, and neutrophil cells. The current approach-using the pharmacogenomic analysis of combined therapies-serves as a model for novel personalized therapeutic strategies for GBM treatment.

Prediction model of liver metastasis risk in patients with gastric cancer: A population-based study

Liver was the most common site of distant metastasis in patients with gastric cancer (GC). The prediction model of the risk of liver metastasis was rarely proposed. Therefore, we aimed to establish a prediction model for liver metastasis in patients with GC. In this retrospective cohort study, we extracted demographic and clinical data of all the GC patients from the Surveillance, Epidemiology, and End Results registration database from 2010 to 2015. Patients were divided into training set (n = 1691) for model development and testing set (n = 3943) for validation. Univariable and multivariable logistic regression analyses were carried out on the training set to screen potential predictors of liver metastasis and constructed a prediction model. The receiver operator characteristics curves with the area under curve values were used to assess the predictive performance of the liver metastasis prediction model. And a nomogram of the prediction model was also constructed. Of the total 5634 GC patients, 444 (7.88%) had liver metastasis. Variables including age, gender, N stage, T stage, Lauren classification, tumor size, histological type, and surgery were included in the liver metastasis prediction model. The study results indicated that the model had excellent discriminative ability with an area under curve of 0.851 (95% confidence interval: 0.829-0.873) in the training set, and that of 0.849 (95% confidence interval: 0.813-0.885) in the testing set. We have developed an effective prediction model with 8 easily acquired predictors of liver metastasis. The prediction model could predict the risk of liver metastasis in GC patients and performed well, which would assist clinicians to make individualized prediction of liver metastasis in GC patients and adjust treatment strategies in time to improve the prognosis.

A nomogram prediction model for the TP53mut subtype in endometrial cancer based on preoperative noninvasive parameters

BACKGROUND

The molecular subtypes of endometrial carcinoma are significantly correlated with survival outcomes and can guide surgical methods and postoperative adjuvant therapy. Among them, the TP53mut subtype has the worst prognosis and can only be determined by detection after surgery. Therefore, identifying preoperative noninvasive clinical parameters for early prediction of the TP53mut subtype would provide important guidance in choosing the appropriate surgical method and early warning for clinicians. Our study aimed to establish a model for the early prediction of the TP53mut subtype by using preoperative noninvasive parameters of endometrial cancer and screen out potential TP53mut patients.

METHODS

Information and pathological specimens of 376 patients who underwent surgery for FIGO stage I-IV endometrial cancer in the Department of Gynecology, Peking University Cancer Hospital, from June 2011 to July 2020 were collected, and 178 cases were finally included in the study as the training dataset (part A). Thirty-six cases from January 2022 to March 2023 were collected as the validation dataset (part B). Molecular subtyping was performed using a one-stop next-generation sequencing (NGS) approach. Compared with the TP53mut subtype, the POLE EDM, MSI-H and TP53 wild-type subtypes were defined as non-TP53mut subtypes. Univariate Cox regression analysis and multivariate logistic analysis were performed to determine the preoperative clinical parameters associated with the TP53mut subtype. A nomogram prediction model was established using preoperative noninvasive parameters, and its efficacy in predicting TP53mut subtype and survival outcomes was verified.

RESULTS

The TP53mut subtype was identified in 12.4% of the part A and 13.9% of the part B. Multivariate logistic regression analysis showed that HDL-C/LDL-C level, CA125 level, and cervical or lower uterine involvement were independent influencing factors associated with the TP53mut subtype (p = 0.016, 0.047, <0.001). A TP53mut prognostic model (TPMM) was constructed based on the factors identified in the multivariate analysis, namely, TPMM = -1.385 × HDL-C/LDL-C + 1.068 × CA125 + 1.89 × CI or LUI, with an AUC = 0.768 (95% CI, 0.642 to 0.893) in the part A. The AUC of TPMM for predicting TP53mut subtype in the part B was 0.781(95% CI, 0.581 to 0.980). The progression-free survival (PFS) and overall survival (OS) of patients with the TP53mut subtype were significantly worse than those of patients with the non-TP53mut subtype, as predicted by the model in the part A.

CONCLUSIONS

TP53mut prediction model (TPMM) had good diagnostic accuracy, and survival analysis showed the model can identify patients with different prognostic risk.

Role of the membrane-spanning 4A gene family in lung adenocarcinoma

Lung adenocarcinoma, which is the second most prevalent cancer in the world, has a poor prognosis and a low 5-year survival rate. The MS4A protein family is crucial to disease development and progression, particularly for cancers, allergies, metabolic disorders, autoimmune diseases, infections, and neurodegenerative disorders. However, its involvement in lung adenocarcinoma remains unclear. In this study, we found that 11 MS4A family genes were upregulated or downregulated in lung adenocarcinoma. Furthermore, we described the genetic variation landscape of the MS4A family in lung adenocarcinoma. Notably, through functional enrichment analysis, we discovered that the MS4A family is involved in the immune response regulatory signaling pathway and the immune response regulatory cell surface receptor signaling pathway. According to the Kaplan-Meier curve, patients with lung adenocarcinoma having poor expression of MS4A2, MS4A7, MS4A14, and MS4A15 had a low overall survival rate. These four prognostic genes are substantially associated with immune-infiltrating cells, and a prognosis model incorporating them may more accurately predict the overall survival rate of patients with lung adenocarcinoma than current models. The findings of this study may offer creative suggestions and recommendations for the identification and management of lung adenocarcinoma.

Authentication of a survival nomogram for non-invasive micropapillary breast cancer

Purpose

We aimed at establishing a nomogram to accurately predict the overall survival (OS) of non-metastatic invasive micropapillary breast carcinoma (IMPC).

Methods

In the training cohort, data from 429 patients with non-metastatic IMPC were obtained through the Surveillance, Epidemiology, and End Results (SEER) database. Other 102 patients were enrolled at the Xijing Hospital as validation cohort. Independent risk factors affecting OS were ascertained using univariate and multivariate Cox regression. A nomogram was established to predict OS at 3, 5 and 8 years. The concordance index (C-index), the area under a receiver operating characteristic (ROC) curve and calibration curves were utilized to assess calibration, discrimination and predictive accuracy. Finally, the nomogram was utilized to stratify the risk. The OS between groups was compared through Kaplan-Meier survival curves.

Results

The multivariate analyses revealed that race (p = 0.047), surgery (p = 0.003), positive lymph nodes (p = 0.027), T stage (p = 0.045) and estrogen receptors (p = 0.019) were independent prognostic risk factors. The C-index was 0.766 (95% CI, 0.682-0.850) in the training cohort and 0.694 (95% CI, 0.527-0.861) in the validation cohort. Furthermore, the predicted OS was consistent with actual observation. The AUCs for OS at 3, 5 and 8 years were 0.786 (95% CI: 0.656-0.916), 0.791 (95% CI: 0.669-0.912), and 0.774 (95% CI: 0.688-0.860) in the training cohort, respectively. The area under the curves (AUCs) for OS at 3, 5 and 8 years were 0.653 (95% CI: 0.498-0.808), 0.683 (95% CI: 0.546-0.820), and 0.716 (95% CI: 0.595-0.836) in the validation cohort, respectively. The Kaplan-Meier survival curves revealed a significant different OS between groups in both cohorts (p<0.001).

Conclusion

Our novel prognostic nomogram for non-metastatic IMPC patients achieved a good level of accuracy in both cohorts and could be used to optimize the treatment based on the individual risk factors.

Two web-based dynamic prediction models for the diagnosis and prognosis of gastric cancer with bone metastases: evidence from the SEER database

Purpose

Our aim was to identify the clinical characteristics and develop and validate diagnostic and prognostic web-based dynamic prediction models for gastric cancer (GC) with bone metastasis (BM) using the SEER database.

Method

Our study retrospectively analyzed and extracted the clinical data of patients aged 18-85 years who were diagnosed with gastric cancer between 2010 and 2015 in the SEER database. We randomly divided all patients into a training set and a validation set according to the ratio of 7 to 3. Independent factors were identified using logistic regression and Cox regression analyses. Furthermore, we developed and validated two web-based clinical prediction models. We evaluated the prediction models using the C-index, ROC, calibration curve, and DCA.

Result

A total of 23,156 patients with gastric cancer were included in this study, of whom 975 developed bone metastases. Age, site, grade, T stage, N stage, brain metastasis, liver metastasis, and lung metastasis were identified as independent risk factors for the development of BM in GC patients. T stage, surgery, and chemotherapy were identified as independent prognostic factors for GC with BM. The AUCs of the diagnostic nomogram were 0.79 and 0.81 in the training and test sets, respectively. The AUCs of the prognostic nomogram at 6, 9, and 12 months were 0.93, 0.86, 0.78, and 0.65, 0.69, 0.70 in the training and test sets, respectively. The calibration curve and DCA showed good performance of the nomogram.

Conclusions

We established two web-based dynamic prediction models in our study. It could be used to predict the risk score and overall survival time of developing bone metastasis in patients with gastric cancer. In addition, we also hope that these two web-based applications will help physicians comprehensively manage gastric cancer patients with bone metastases.

The Prognostic Value and Potential Mechanism of Tumor-Nutrition-Inflammation Index and Genes in Patients with Advanced Lung Cancer

Background

Lung cancer (LC) has the highest mortality rate all over the world. It is necessary to search for novel potential biomarkers that are easily accessible and inexpensive in identifying patients with LC at early stage.

Methods

A total of 195 patients with advanced LC who have received first-line chemotherapy were involved in this study. The optimized cut-off values of AGR and SIRI (AGR = albumin/globulin; SIRI = neutrophil ∗ monocyte/lymphocyte) were determined by survival function analysis based on R software. COX regression analysis was performed to obtain the independent factors for establishing the nomogram model. A nomogram model comprising these independent prognostic parameters was built for the TNI (tumor-nutrition-inflammation index) score calculation. The predictive accuracy was demonstrated through ROC curve and calibration curves after index concordance.

Results

The optimized cut-off values of AGR and SIRI were 1.22 and 1.60, respectively. It was revealed that liver metastasis, SCC, AGR, and SIRI were independent prognostic factors in advanced lung cancer by Cox analysis. Afterwards, the nomogram model comprised of these independent prognostic parameters was built for TNI scores calculation. Based on the TNI quartile values, patients were divided into four groups. And it was indicated that higher TNI had worse OS (P < 0.05) via Kaplan-Meier analysis and log-rank test. Moreover, the C-index and 1-year AUC area were 0.756 (0.723-0.788) and 75.62, respectively. There was high consistency shown in the calibration curves between predicted and actual survival proportions in the TNI model. In addition, tumor-nutrition-inflammation index and genes play an important role in LC development that might affect some pathways related to tumor development including cell cycle, homologous recombination, and P53 signaling pathway from a molecular level.

Conclusion

TNI might be an analytical tool which was practical and precise for survival prediction of patients with advanced LC. Tumor-nutrition-inflammation index and genes play an important role in LC development. A preprint has previously been published [1].

Comprehensive analysis of autophagy-related clusters and individual risk model for immunotherapy response prediction in gastric cancer

Introduction

Autophagy can be triggered by oxidative stress and is a double-edged sword involved in the progression of multiple malignancies. However, the precise roles of autophagy on immune response in gastric cancer (GC) remain clarified.

Methods

We endeavor to explore the novel autophagy-related clusters and develop a multi-gene signature for predicting the prognosis and the response to immunotherapy in GC. A total of 1505 patients from eight GC cohorts were categorized into two subtypes using consensus clustering. We compare the differences between clusters by the multi-omics approach. Cox and LASSO regression models were used to construct the prognostic signature.

Results

Two distinct clusters were identified. Compared with cluster 2, the patients in cluster 1 have favorable survival outcomes and lower scores for epithelial-mesenchymal transition (EMT). The two subtypes are further characterized by high heterogeneity concerning immune cell infiltration, somatic mutation pattern, and pathway activity by gene set enrichment analysis (GSEA). We obtained 21 autophagy-related differential expression genes (DEGs), in which PTK6 amplification and BCL2/CDKN2A deletion were highly prevalent. The four-gene (PEA15, HSPB8, BNIP3, and GABARAPL1) risk signature was further constructed with good predictive performance and validated in 3 independent datasets including our local Tianjin cohort. The risk score was proved to be independent prognostic factor. A prognostic nomogram showed robust validity of GC survival. The risk score was significantly associated with immune cell infiltration status, tumor mutation burden (TMB), microsatellite instability (MSI), and immune checkpoint molecules. Furthermore, the model was efficient for predicting the response to tumor-targeted agent and immunotherapy and verified by the IMvigor210 cohort. This model is also capable of discriminating between low and high-risk patients receiving chemotherapy.

Conclusion

Altogether, our exploratory research on the landscape of autophagy-related patterns may shed light on individualized therapies and prognosis in GC.

SLC2As as diagnostic markers and therapeutic targets in LUAD patients through bioinformatic analysis

Facilitative glucose transporters (GLUTs), which are encoded by solute carrier 2A (SLC2A) genes, are responsible for mediating glucose absorption. In order to meet their higher energy demands, cancer cells are more likely than normal tissue cells to have elevated glucose transporters. Multiple pathogenic processes, such as cancer and immunological disorders, have been linked to GLUTs. Few studies, meanwhile, have been conducted on individuals with lung adenocarcinoma (LUAD) to evaluate all 14 SLC2A genes. We first identified increased protein levels of SLC2A1, SLC2A5, SLC2A6, and SLC2A9 via HPA database and downregulated mRNA levels of SLC2A3, SLC2A6, SLC2A9, and SLC2A14 by ONCOMINE and UALCAN databases in patients with LUAD. Additionally, lower levels of SLC2A3, SLC2A6, SLC2A9, SLC2A12, and SLC2A14 and higher levels of SLC2A1, SLC2A5, SLC2A10, and SLC2A11 had an association with advanced tumor stage. SLC2A1, SLC2A7, and SLC2A11 were identified as prognostic signatures for LUAD. Kaplan-Meier analysis, Univariate Cox regression, multivariate Cox regression and ROC analyses further revealed that these three genes signature was a novel and important prognostic factor. Mechanistically, the aberrant expression of these molecules was caused, in part, by the hypomethylation of SLC2A3, SLC2A10, and SLC2A14 and by the hypermethylation of SLC2A1, SLC2A2, SLC2A5, SLC2A6, SLC2A7, and SLC2A11. Additionally, SLC2A3, SLC2A5, SLC2A6, SLC2A9, and SLC2A14 contributed to LUAD by positively modulating M2 macrophage and T cell exhaustion. Finally, pathways involving SLC2A1/BUB1B/mitotic cell cycle, SLC2A5/CD86/negative regulation of immune system process, SLC2A6/PLEK/lymphocyte activation, SLC2A9/CD4/regulation of cytokine production might participate in the pathogenesis of LUAD. In summary, our results will provide the theoretical basis on SLC2As as diagnostic markers and therapeutic targets in LUAD.

A Nomogram with the Keloid Activity Assessment Scale for Predicting the Recurrence of Chest Keloid after Surgery and Radiotherapy

BACKGROUND

Patients with chest keloids undergoing surgery and adjuvant radiotherapy still have a high recurrence rate, which is a critical problem. The level of keloid activity has not been studied, and a nomogram model for predicting keloid recurrence has not been established in previous studies.

METHODS

A total of 145 patients with chest keloids who underwent surgery and radiotherapy between January 2015 and January 2019 at Peking Union Medical College Hospital were included in our study. Demographic and clinical features and the score of KAAS were analyzed. We compared the area under the curve (AUC) and decision curve analysis (DCA) between KAAS and the Vancouver scar scale (VSS) and established a nomogram model for predicting the risk of recurrence. We used bootstrap and calibration plots to evaluate the performance of the nomogram.

RESULTS

The KAAS can predict recurrence in patients with chest keloids after surgery and radiotherapy. Areas under the curve (AUCs) of KAAS and VSS were 0.858 and 0.711, respectively (p < 0.001). Decision curve analysis (DCA) demonstrated that the KAAS was better than the VSS. Complications after treatment may be risk factors for keloid recurrence. We created a nomogram by using complications and KAAS. The AUC was 0.871 (95% CI 0.812-0.930). The ROC of the model's bootstrap was 0.865 and was well calibrated.

CONCLUSIONS

The KAAS can be used to predict the recurrence and we developed a nomogram for predicting the recurrence of chest keloids after surgery and adjuvant radiotherapy.

LEVEL OF EVIDENCE IV

This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

A nomogram combining clinical factors and biomarkers for predicting the recurrence of high-risk cutaneous squamous cell carcinoma

Background

Although determining the recurrence of cutaneous squamous cell carcinoma (cSCC) is important, currently suggested systems and single biomarkers have limited power for predicting recurrence.

Objective

In this study, combinations of clinical factors and biomarkers were adapted into a nomogram to construct a powerful risk prediction model.

Methods

The study included 145 cSCC patients treated with Mohs micrographic surgery. Clinical factors were reviewed, and immunohistochemistry was performed using tumor tissue samples. A nomogram was constructed by combining meaningful clinical factors and protein markers.

Results

Among the various factors, four clinical factors (tumor size, organ transplantation history, poor differentiation, and invasion into subcutaneous fat) and two biomarkers (Axin2 and p53) were selected and combined into a nomogram. The concordance index (C-index) of the nomogram for predicting recurrence was 0.809, which was higher than that for the American Joint Committee on Cancer (AJCC) 7th, AJCC 8th, Brigham and Women’s Hospital, and Breuninger staging systems in the patient data set.

Conclusion

A nomogram model that included both clinical factors and biomarkers was much more powerful than previous systems for predicting cSCC recurrence.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-10213-2.

Nomograms for predicting the prognosis of patients with penoscrotal extramammary Paget’s disease: A retrospective study in the SEER database and two medical centers

Background

Extramammary Paget’ s disease (EMPD) is a rare cutaneous malignant tumor, and the prognostic factors associated with penoscrotal EMPD remains unclear. The purpose of this study is to investigate prognostic factors and construct nomograms to predict the outcome of patients with EMPD located in the penis or scrotum.

Methods

From the Surveillance, Epidemiology and End Results (SEER) database, we extracted 95 patients with primary EMPD located in the penis or scrotum as the training cohort. Forty-nine penoscrotal EMPD patients were included from two medical centers as the external validation cohort. Univariate and multivariate Cox regression model were applied to investigating risk factors of cancer-specific survival (CSS) and overall survival (OS). Based on the results of multivariate Cox regression analysis, the nomograms were constructed for predicting CSS and OS of patients with penoscrotal EMPD. The concordance index (C-index), receiver operating characteristic (ROC) curves and calibration curves were applied to evaluate the practicability and accuracy of the nomograms.

Results

In the training cohort, multivariate Cox regression analysis showed that marital status and tumor stage were independent factors of CSS, and marital status, tumor stage and surgery are associated with OS independently in patients with penoscrotal EMPD. Based on these results, we developed nomograms to predict CSS and OS respectively. The C-index values were 0.778 for CSS, and 0.668 for OS in the training set, which displayed the good discriminations. In the external validation set, the C-index values were 0.945 for CSS, and 0.703 for OS. The areas under the curve (AUC) values of nomogram predicting 1-, 3-, and 5-year CSS were 0.815, 0.833, and 0.861 respectively, and 0.839, 0.654, and 0.667 for nomogram predicting 1-, 3-, and 5-year OS respectively. In the validation set, the AUC values of nomogram predicting 1-, 3-, and 5-year CSS were 0.944, 0.896, and 0.896 respectively, and 0.777, 0.762 and 0.692 for nomogram predicting 1-, 3-, and 5-year OS respectively. Additionally, the internal calibration curves also proved that our nomograms have good accuracy.

Conclusions

By incorporating marital status, tumor stage and/or surgery, our nomograms can efficiently predict CSS and OS of patients with penoscrotal EMPD.

Identification of a necroptosis-related prognostic gene signature associated with tumor immune microenvironment in cervical carcinoma and experimental verification

Cervical carcinoma (CC) has been associated with high morbidity, poor prognosis, and high intratumor heterogeneity. Necroptosis is the significant cellular signal pathway in tumors which may overcome tumor cells’ apoptosis resistance. To investigate the relationship between CC and necroptosis, we established a prognostic model based on necroptosis-related genes for predicting the overall survival (OS) of CC patients. The gene expression data and clinical information of cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) patients were obtained from The Cancer Genome Atlas (TCGA). We identified 43 differentially expressed necroptosis-related genes (NRGs) in CESC by examining differential gene expression between CESC tumors and normal tissues, and 159 NRGs from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Gene ontology (GO) and KEGG enrichment analysis illustrated that the genes identified were mainly related to cell necrosis, extrinsic apoptosis, Influenza A, I − kappaB kinase/NF − kappaB, NOD − like receptor, and other signaling pathways. Subsequently, least absolute shrinkage and selection operator (LASSO) regression and univariate and multivariate Cox regression analyses were used to screen for NRGs that were correlated with patient prognosis. A prognostic signature that includes CAMK2A, CYBB, IL1A, IL1B, SLC25A5, and TICAM2 was established. Based on the prognostic model, patients were stratified into either the high-risk or low-risk subgroups with distinct survival. Receiver operating characteristic (ROC) curve analysis was used to identify the predictive accuracy of the model. In relation to different clinical variables, stratification analyses were performed to demonstrate the associations between the expression levels of the six identified NRGs and the clinical variables in CESC. Immunohistochemical (IHC) validation experiments explored abnormal expressions of these six NRGs in CESC. We also explored the relationship between risk score of this necroptosis signature and expression levels of some driver genes in TCGA CESC database and Gene Expression Omnibus (GEO) datasets. Significant relationships between the six prognostic NRGs and immune-cell infiltration, chemokines, tumor mutation burden (TMB), microsatellite instability (MSI), and immune checkpoints in CESC were discovered. In conclusion, we successfully constructed and validated a novel NRG signature for predicting the prognosis of CC patients and might also play a crucial role in the progression and immune microenvironment in CC.

m6A/ m1A /m5C/m7G-related methylation modification patterns and immune characterization in prostate cancer

Methylation has a close relationship with immune reactions, metastasis, and cancer cell growth. Additionally, RNA methylation-related proteins have emerged as potential cancer therapeutic targets. The connection between the tumor microenvironment (TME) and methylation-related genes (MRGs) remains unclear. We explored the expression patterns of the MRGs in the genome and transcriptional fields of 796 prostate cancer (PCa) samples using two separate data sets. We identified a relationship between patient clinicopathological characteristics, prognosis, TME cell infiltrating qualities, and different MRG changes, as well as the identification of two distinct molecular groupings. Then, we formed an MRGs model to predict overall survival (OS), and we tested the accuracy of the model in patients with PCa. In addition, we developed a very accurate nomogram to improve the MRG model’s clinical applicability. The low-risk group had fewer tumor mutational burden (TMB), greater tumor immune dysfunction and exclusion (TIDE) ratings, fewer mutant genes, and better OS prospects. We discuss how MGRs may affect the prognosis, clinically important traits, TME, and immunotherapy responsiveness in PCa. In order to get a better understanding of MRGs in PCa, we could further explore the prognosis and create more effective immunotherapy regimens to open new avenues.

A multifaceted and feasible prognostic model of amino acid metabolism-related genes in the immune response and tumor microenvironment of head and neck squamous cell carcinomas

We investigated the role of amino acid metabolism (AAM) in head and neck squamous cell carcinoma (HNSCC) tissues to explore its prognostic value and potential therapeutic strategies. A risk score based on four AAM-related genes (AMG) was constructed that could predict the prognosis of HNSCC. These four genes were up-regulated in HNSCC tissues and might act as oncogenes. Internal validation in The Cancer Genome Atlas (TCGA) by bootstrapping showed that patients with high-risk scores had a poorer prognosis than patients with low-risk scores, and this was confirmed in the Gene Expression Omnibus (GEO) cohort. There were also differences between the high-risk and low-risk groups in clinical information and different anatomical sites such as age, sex, TNM stage, grade stage, surgery or no surgery, chemotherapy, radiotherapy, no radiotherapy, neck lymph node dissection or not, and neck lymphovascular invasion, larynx, overlapping lesion of lip, and oral cavity and pharynx tonsil of overall survival (OS). Immune-related characteristics, tumor microenvironment (TME) characteristics, and immunotherapy response were significantly different between high- and low-risk groups. The four AMGs were also found to be associated with the expression of markers of various immune cell subpopulations. Therefore, our comprehensive approach revealed the characterization of AAM in HNSCC to predict prognosis and guide clinical therapy.

A nine–consensus–prognostic –gene–based prognostic signature, recognizing the dichotomized subgroups of gastric cancer patients with different clinical outcomes and therapeutic strategies

Background: The increasing prevalence and mortality of gastric cancer (GC) has promoted the urgent need for prognostic signatures to predict the long-term risk and search for therapeutic biomarkers.

Methods and materials: A total of 921 GC patients from three GEO cohorts were enrolled in the current study. The GSE15459 and GSE62254 cohorts were used to select the top prognostic gene via the evaluation of the area under the receiver operating characteristic (ROC) curve (AUC) values. The GSE84437 cohort was used as the external validation cohort. Least absolute shrinkage and selector operation (LASSO) regression analysis was applied to reduce the feature dimension and construct the prognostic signature. Furthermore, a nomogram was constructed by integrating the independent prognostic analysis and validated by calibration plot, decision curve analysis and clinical impact curve. The molecular features and response to chemo-/immunotherapy among risk subgroups were evaluated by the “MOVICS” and “ESTAMATE” R packages and the SubMap algorithm. Lauren classification and ACRG molecular subtype were obtained to compare with the risk model.

Results: Forty-four prognosis-associated genes were identified with a preset cutoff AUC value of 0.65 in both the GSE62254 and GSE15459 cohorts. With the 10-fold cross validation analysis of LASSO, nine genes were selected to construct the nine-consensus-prognostic-gene signature. The signature showed good prognostic value in the GSE62254 (p < 0.001, HR: 3.81, 95% CI: 2.44–5.956) and GSE15459 (p < 0.001, HR: 2.65, 95% CI: 1.892–3.709) cohorts and the external validation GSE84437 cohort (p < 0.001, HR: 2.06, 95% CI: 1.554–2.735). The nomogram constructed based on two independent predictive factors, tumor stage and the signature, predicted events tightly consistent with the actual (Hosmer–Lemeshow p value: 1-year, 0.624; 3-years, 0.795; 5-years, 0.824). For the molecular features, we observed the activation of apical junction, epithelial mesenchymal transition, and immune pathways in the high-risk group, while in the low-risk group, cell cycle associated G2M, E2F and MYC target pathways were activated. Based on the results we obtained, we indicated that gastric patients in the low-risk group are more suitable for 5-fluorouracil therapy, while high-risk group patients are more suitable for anti-CTLA4 immunotherapy, these results need more support in the further studies. After compare with proposed molecular subtypes, we realized that the nine-consensus prognostic gene signature is a powerful addition to identify the gastric patients with poor prognosis.

Conclusion: In summary, we constructed a robust nine-consensus-prognostic-gene signature for the prediction of GC prognosis, which can also predict the personalized treatment of GC patients.

A predictive model of bowel resection for incarcerated inguinal hernia based on the systemic immune-inflammation index

Background and Purpose

An inguinal hernia is a common surgical disease. Once incarcerated or strangulated, it may endanger the life of the patient. Therefore, it is essential to study the risk factors of incarcerated inguinal hernia (IIH) and strangulated inguinal hernia (SIH). One of the serious complications of IIH and SIH is intestinal necrosis, which occurs owing to blood supply disorder. The study explores the risk factors of intestinal resection and establishes a simple model to assess the incidence of intestinal resection to provide significant assistance and limited guidance for clinical work.

Patients and Methods

Our research team collected and retrospectively analysed the clinical data of 338 patients with IIH who were hospitalized in the First Affiliated Hospital of Wenzhou Medical University between September 2008 and December 2016. According to the surgical plan, we divided the included cases into two groups, non-intestinal and intestinal resection groups, and the clinical case characteristics of these groups were statistically analysed.

Results

Based on multivariable logistic regression analysis, we found that increased risk of bowel resection was highly correlated among the elderly (≥70 years), and for people with high temperature (≥37.3°C), high systemic immune-inflammation index(SII) values (≥1230.13), presence of bowel obstruction, and signs of peritonitis. Further, we processed the five independent risk factors using special software to obtain a simple model called a nomogram. To verify the nomogram’s accuracy and predictive ability, we calculate the C-index: 0.806 and use the calibration curve to evaluate its stability and predictive performance. We constructed the ROC curve nomogram and other sub-variables, and calculated the area under the curve (AUC) corresponding to the nomogram (AUC = 0.808, 95% CI = 0.762 to 0.848), SII (AUC = 0.752, 95% CI = 0.703 to 0.797), age (AUC = 0.641, 95% CI = 0.587 to 0.692), temperature (AUC = 0.579, 95% CI = 0.524 to 0.632), bowel obstruction (AUC = 0.685, 95% CI = 0.633 to 0.734), and signs of peritonitis (AUC = 0.580, 95% CI = 0.525 to 0.633).

Conclusion

It can be said that we found for the first time that clinical variables such as SII are independent risk factors for enterectomy for IIH. The nomogram based on SII and other variables can accurately and easily predict the probability of IIH requiring bowel resection.

Ornithine aminotransferase and carbamoyl phosphate synthetase 1 involved in ammonia metabolism serve as novel targets for early stages of gastric cancer

Objective

The sensitivity and specificity of current biomarkers for gastric cancer were insufficient. The aim of the present study was to screen novel biomarkers and determine the diagnostic values of ornithine aminotransferase (OAT) and carbamoyl phosphate synthetase 1 (CPS1) for detecting gastric cancer.

Methods

With stable isotope tags, we labelled an initial discovery group of four paired gastric cancer tissue samples and identified with LC‐ESI‐MS/MS. A validation group of 159 gastric cancer samples and 30 healthy controls were used to validate the candidate targets. GSEA was used to explore the pathways activated in gastric cancer.

Results

Four hundred and thirty one proteins were found differentially expressed in gastric cancer tissues. Of these proteins, OAT and CPS1 were found over‐expressed in gastric cancer patients, with sensitivity of 70.4% (95% CI: 63.3%–77.6%) and specificity of 80.5% (95% CI: 74.3%–86.7%) for ornithine aminotransferase, and with sensitivity of 68.6% (95% CI: 61.3%–75.8%) and specificity of 73% (95% CI: 66%–79.9%) for carbamoyl phosphate synthetase 1. The co‐expression of OAT and CPS1 in gastric cancer tissues has a sensitivity of 81% (95% CI: 73.2%–88.8%) and specificity of 89% (95% CI: 83%–95%). Furthermore, both OAT and CPS1 were overexpressed in patients with local invasion T3 and T4 stages than those in patients with T1 and T2 stages. The co‐expression of OAT and CPS1 was strongly correlated with histological grade I 68% (95% CI: 58.7%–77.3%) and TNM stage I/II 52% (95% CI: 42%–62%). The areas under ROC curves were up to 0.758 for the co‐expression of OAT and CPS1 in gastric cancer. GSEA results showed that two gene sets and 30 gene sets were activated in OAT high‐ and CPS1 high‐expression patients with gastric cancer, respectively.

Conclusions

The present findings indicated a tight correlation between the co‐expression of OAT and CPS1 and the histological grade, local invasion, and TNM stages of gastric cancer. Therefore, OAT and CPS1 might be predictors for gastric cancer invasion and potential targets for anticancer drug design for gastric cancer.

Family history of malignant tumor is a predictor of gastric cancer prognosis: Incorporation into a nomogram

The purpose of this study was to investigate the impact of a family history of malignant tumor on the prognosis of patients with gastric cancer and develop a nomogram that incorporates a family history of malignant tumor to predict overall survival (OS) in patients with gastric cancer to aid clinicians and patients in decision making. Four hundred eighty-eight patients with gastric cancer undergoing radical gastrectomy in our center were included and randomly split into a training set (n = 350) and a validation set (n = 138) at a ratio of 7:3. Cox univariate regression analysis was used to evaluate the influence of clinicopathological characteristics and family history of malignant tumors on their prognosis, and variables were screened by multivariate Cox regression analysis and consensus on clinical evidence. A nomogram was constructed for OS based on the filtered variables, and the C-index, receiver operating characteristic curve (ROC curve), and calibration curve were used to validate the nomogram and decision curve analysis curve (DCA curve) was used for clinical practicality assessment. Six variables related to OS, including the pathological differentiation degree, Lauren type, infiltration depth, lymph node metastasis, tumor deposit, and family history of malignant tumor, were screened to construct a nomogram. The nomogram developed in this study performed well in the training set and the validation set, with C-index of 0.776 and 0.757, and the area under the ROC curve(AUC) for predicting 1-, 3-, and 5-year survival rates are 0.838, 0.850, 0.820 and 0.754, 0.789, 0.808, respectively. The calibration curve shows that the estimated death risk of the nomogram in the 2 data sets is very close to the actual death risk. The net benefits of nomogram-guided prediction of patient survival at 1-, 3-, and 5 years were demonstrated by the DCA curves, which showed high clinical practicability. Family history of malignant tumors is an independent risk factor affecting the prognosis of patients with gastric cancer. The nomogram developed in this research can be used as an important tool to predict the prognosis of gastric cancer patients with family history data.

Effects of Radiotherapy on Survival of Esophageal Cancer Patients Receiving Immunotherapy: Propensity Score Analysis and Nomogram Construction

Purpose

The present study assessed the effects of radiotherapy on overall survival (OS) and progression-free survival time (PFS) in patients with stage II or higher esophageal cancer receiving immunotherapy; evaluated factors independently prognostic of OS and PFS in these patients; and utilized these factors to establish a prognostic nomogram.

Patients and Methods

This study enrolled 134 patients with stage II or higher esophageal cancer treated with chemotherapy (platinum-based agents plus paclitaxel or fluorouracil) and immunotherapy. These patients were divided into two groups, a radiotherapy (RT) group (n = 55) and a non-radiotherapy (non-RT) group (n = 79). Following 1:1 propensity score matching, OS and PFS were compared by the Kaplan-Meier method, and factors associated with survival were determined by univariate and multifactorial Cox regression analyses. These factors were used to construct a prognostic nomogram.

Results

After propensity matching, all covariates were well balanced in the two groups (all P > 0.05). After matching, both median PFS (15.70 months [95% confidence interval (CI) 8.68-22.72 months] vs 5.70 months [95% CI 3.38-8.02 months], P = 0.002) and median OS (15.72 months [95% CI 12.94-18.46 months] vs 12.06 months [95% CI 9.91-14.20 months], P = 0.036) were significantly longer in the RT than in the non-RT group. Univariate and multifactorial analyses showed that RT, neutrophil-lymphocyte ratios, and tumor differentiation were independently prognostic of OS, with all hazard ratios (HRs) <1 and all P-values <0.05. A nomogram based on these factors was constructed, and its accuracy was verified.

Conclusion

Immunotherapy plus RT resulted in better survival outcomes than immunotherapy alone. A nomogram based on prognostic factors can guide personalized treatment and monitor prognosis.

Overexpression of LIMA1 Indicates Poor Prognosis and Promotes Epithelial-Mesenchymal Transition in Head and Neck Squamous Cell Carcinoma

Background:

LIMA1 encodes LIM domain and actin binding 1, a cytoskeleton-associated protein whose loss has been linked to migration and invasion behavior of cancer cells. However, the roles of LIMA1 underlying the malignant behavior of tumors in head and neck squamous cell carcinoma (HNSC) are not fully understood.

Methods:

We conducted a multi-omics study on the role of LIMA1 in HNSC based on The Cancer Genome Atlas data. Subsequent in vitro experiments were performed to validate the results of bioinformatic analysis. We first identified the correlation between LIMA1 and tumor cell functional states according to single-cell sequencing data in HNSC. The potential downstream effects of LIMA1 were explored for gene ontology and Kyoto Encyclopedia of Genes and Genomes pathways through functional enrichment analysis of the gene sets that correlated with LIMA1 in HNSC. The prognostic role of LIMA1 was assessed using the log rank test to compare difference in survival between LIMA1^High and LIMA1^Low patients. Univariate Cox regression and multivariate Cox regression were further carried out to identify the prognostic value of LIMA1 in HNSC.

Results:

LIMA1 was identified as a prognostic biomarker and is associated with epithelial-mesenchymal transition (EMT) progress in HNSC. In vitro silencing of LIMA1 suppressed EMT and related pathways in HNSC.

Conclusions:

LIMA1 promotes EMT and further leads to tumor invasion and metastasis. Increased expression of LIMA1 indicates poor survival, identifying it as a prognostic biomarker in HNSC.

Indications for Adjuvant Chemotherapy in Stage II Gastric Cancer After D2 Gastrectomy-A Chinese Multicenter Study

BACKGROUND

The benefit of adjuvant chemotherapy (AC) for patients with stage II gastric cancer remains controversial. This study aimed to explore the indications for adjuvant chemotherapy in patients with stage II gastric cancer by constructing an individual prediction model.

PATIENTS AND METHODS

In this Chinese multicenter study, a total of 1012 patients with stage II gastric cancer after D2 radical gastrectomy were retrospectively analyzed. All patients were randomly assigned to a training cohort (n = 674) or a validation cohort (n = 338). A nomogram was constructed according to the training cohort. Concordance index (C-index), the area under the receiver operating characteristic (ROC) curves (AUC), calibration curves, and decision curve analysis (DCA) were applied to evaluate the performance of the nomogram. ROC curves and stratified survival were used to determine the patients' cutoff score for a benefit from adjuvant chemotherapy. An additional 338 patients were used as a validation cohort to validate the feasibility of using this nomogram to guide individualized therapy for patients with stage II gastric cancer.

RESULTS

Univariate and multivariate analyses illustrated that age, sex, tumor location, size, carcinoembryonic antigen (CEA), hemoglobin (HB), and T stage were independent prognostic factors for overall survival (OS), and they were used to establish a nomogram. The cutoff value was determined by ROC curve analysis, and patients were divided into a high-risk group (< 239 points) and a low-risk group (≥ 239 points). There was no significant difference in the OS of low-risk patients in either the training cohort or the validation cohort. However, the OS of high-risk patients in the AC group was better than that of patients in the surgery-only group.

CONCLUSIONS

This prediction model can be applied to guide treatment of patients with stage II gastric cancer. High-risk patients (< 239 points) are likely to benefit from AC after D2 radical gastrectomy.

Risk factor analysis and nomogram construction for predicting suicidal ideation in patients with cancer

BACKGROUND

Suicidal ideation in cancer patients is a critical challenge. At present, few studies focus on factors associated with suicidal ideation, and predictive models are still lacking. This study aimed at investigating the risk factors for suicidal ideation among cancer patients, and developed a predictive nomogram to screen high risk cancer patients for early prevention and intervention.

METHODS

A questionnaire survey was conducted among cancer patients between May 2021 and January 2022. The factors associated with suicidal ideation were used to construct a multivariate logistic regression model, which was visualized as a predictive nomogram to evaluate the risk of suicidal ideation. Areas under the curve, calibration plot, decision curve analysis, and internal and external validation were used to validate the discrimination, calibration and clinical usefulness of the model.

RESULTS

A total of 820 patients with cancer were recruited for this study and 213 (25.98%) developed suicidal ideation. Levels of demoralization, depression and cancer staging, marital status, residence, medical financial burden, and living condition were influence factors for suicidal ideation. Comparing nomogram with Self-rating Idea of Suicide Scale (SIOSS), the nomogram had a satisfactory discrimination ability with an AUC of 0.859 (95% CI: 0.827-0.890) and 0.818 (95% CI: 0.764-0.873) in the training and validation sets, respectively. The calibration plot and decision curve analysis revealed that this nomogram was in good fitness and could be beneficial in clinical applications.

CONCLUSIONS

Suicidal ideation is common in cancer patients. Levels of demoralization, depression and cancer staging were independent predictors of suicidal ideation. The nomogram is an effective and simple tool for predictive suicidal ideation in cancer patients.

Ferroptosis-based molecular prognostic model for adrenocortical carcinoma based on least absolute shrinkage and selection operator regression

Background

This study aimed to find ferroptosis‐related genes linked to clinical outcomes of adrenocortical carcinoma (ACC) and assess the prognostic value of the model.

Methods

We downloaded the mRNA sequencing data and patient clinical data of 78 ACC patients from the TCGA data portal. Candidate ferroptosis‐related genes were screened by univariate regression analysis, machine‐learning least absolute shrinkage, and selection operator (LASSO). A ferroptosis‐related gene‐based prognostic model was constructed. The effectiveness of the prediction model was accessed by KM and ROC analysis. External validation was done using the GSE19750 cohort. A nomogram was generated. The prognostic accuracy was measured and compared with conventional staging systems (TNM stage). Functional analysis was conducted to identify biological characterization of survival‐associated ferroptosis‐related genes.

Results

Seventy genes were identified as survival‐associated ferroptosis‐related genes. The prognostic model was constructed with 17 ferroptosis‐related genes including STMN1, RRM2, HELLS, FANCD2, AURKA, GABARAPL2, SLC7A11, KRAS, ACSL4, MAPK3, HMGB1, CXCL2, ATG7, DDIT4, NOX1, PLIN4, and STEAP3. A RiskScore was calculated for each patient. KM curve indicated good prognostic performance. The AUC of the ROC curve for predicting 1‐, 3‐, and 5‐ year(s) survival time was 0.975, 0.913, and 0.915 respectively. The nomogram prognostic evaluation model showed better predictive ability than conventional staging systems.

Conclusion

We constructed a prognosis model of ACC based on ferroptosis‐related genes with better predictive value than the conventional staging system. These efforts provided candidate targets for revealing the molecular basis of ACC, as well as novel targets for drug development.

Nomogram and Machine Learning Models Predict 1-Year Mortality Risk in Patients With Sepsis-Induced Cardiorenal Syndrome

Objective

Early prediction of long-term outcomes in patients with sepsis-induced cardiorenal syndrome (CRS) remains a great challenge in clinical practice. Herein, we aimed to construct a nomogram and machine learning model for predicting the 1-year mortality risk in patients with sepsis-induced CRS.

Methods

This retrospective study enrolled 340 patients diagnosed with sepsis-induced CRS in Shanghai Tongji Hospital between January 2015 and May 2019, as a discovery cohort. Two predictive models, the nomogram and machine learning model, were used to predict 1-year mortality. The prognostic variables used to develop the nomogram were identified based on a forward stepwise binary logistic regression, and the predictive ability of the nomogram was evaluated by the areas under the receiver operating characteristic curve (AUC) and the calibration curve. Meanwhile, machine learning (ML) techniques, such as support vector machine, random forest (RF), and gradient boosted decision tree, were assessed mainly by accuracy and AUC. Feature ranking analysis was performed using the ML algorithm. Both nomogram and ML models were externally validated by an independent cohort of 103 patients diagnosed with sepsis-induced CRS between June 2019 and December 2020.

Results

Age, sequential sepsis-related organ failure score (SOFA), serum myoglobin (MYO), vasopressor use, and mechanical ventilation were identified as independent risk factors for 1-year mortality in the nomogram predictive model. In the discovery cohort, the nomogram yielded higher AUC for predicting mortality than did the SOFA score (0.855 [95% CI: 0.815–0.895] vs. 0.756 [95% CI: 0.705–0.808]). For ML, the model developed by RF showed the highest accuracy (0.765) and AUC (0.854). In feature ranking analysis, factors such as age, MYO, SOFA score, vasopressor use, and baseline serum creatinine were identified as important features affecting 1-year prognosis. Moreover, the nomogram and RF model both performed well in external validation, with an AUC of 0.877 and 0.863, respectively.

Conclusion

Our nomogram and ML models showed that age, SOFA score, serum MYO levels, and the use of vasopressors during hospitalization were the main factors influencing the risk of long-term mortality. Our models may serve as useful tools for assessing long-term prognosis in patients with sepsis-induced CRS.

Development and External Validation of a Nomogram for Predicting Acute Kidney Injury in Cardiogenic Shock Patients in Intensive Care Unit

Background

The aim of this study was to construct and external validate a nomogram for predicting cardiogenic shock acute kidney injury (CS-AKI) in patients in intensive care unit (ICU).

Methods

All patients diagnosed with CS from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and the eICU Collaborative Research Database (eICU-CRD) were included in this study. Least absolute shrinkage and selection operator (LASSO) regression and recursive feature elimination for support vector machine (SVM-RFE) were used to determine the overlapping clinical features associated with CS-AKI. The predictive nomogram was established based on the significant clinical parameters and externally verified in this study.

Results

LASSO regression and SVM-RFE demonstrated that Charlson Comorbidity Index (CCI), usage of mechanical ventilation, SOFA score, white blood cell, albumin, eGFR, anion gap, and positive fluid balance were closely associated with CS-AKI in the training cohort. The predictive nomogram based on the eight parameters showed good predictive performance as calculated by C-index were 0.823 (95% confidence index, 95% CI 0.798-0.849), 0.819 (95% CI 0.769-0.849), and 0.733 (95% CI 0.704-0.763) in the training set, in the internal validation set and in the external validation sets, respectively. Moreover, the nomogram exhibited not only encouraging calibration ability but also great clinical utility in the training set and in the validation sets.

Conclusion

CCI, usage of mechanical ventilation, SOFA score, white blood cell, albumin, eGFR, anion gap, and positive fluid balance were closely associated with CS-AKI. The predictive nomogram for CS-AKI manifested well-predictive ability for the identification of ICU patients with CS-AKI.

B3GNT3 as a prognostic biomarker and correlation with immune cell infiltration in lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is the most common malignant cancer in humans and because of low long-term survival rates, exploration of the molecular mechanisms underlying its progression, as well as novel prognostic predictors, is urgently needed. B3GNT3, a type II transmembrane protein located in the Golgi apparatus, is essential for forming extended core 1 oligosaccharides and is reportedly involved in malignant transformation.

Methods

The Cancer Genome Atlas (TCGA) and GSE68465 were used to analyze the expression of B3GNT3 in LUAD and normal tissues and overall survival. Real time quantitative polymerase chain reaction (qPCR) and western blot were conducted to measure the mRNA and protein levels of B3GNT3, respectively. Functional enrichment of differentially expressed genes was explored using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses. We performed univariate and multivariate Cox regression analyses and a meta-analysis to reveal an independent factor for LUAD. We evaluated the correlation between immune infiltration levels and cumulative survival in the TIMER database. The correlation between B3GNT3 and immune cell infiltration was assessed via Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT). The association of DNA methylation of B3GNT3 and prognosis was determined. A nomogram that incorporated expression and clinical features was additionally built for prognostic prediction. Cell proliferation, cloning, and invasion were conducted to validate the roles of B3GNT3 in LUAD.

Results

B3GNT3 was more highly expressed in LUAD tissues than in normal lung tissues, consistent with the mRNA and protein levels in LUAD cells. B3GNT3 was an independent factor for LUAD. Moreover, the levels of B3GNT3 were related to immune cell infiltration in LUAD microenvironments. DNA methylation of B3GNT3 correlated with the mRNA of B3GNT and overall survival of LUAD patients. The expression of B3GNT3 was highly valuable for the prediction of diagnosis. Knockdown of B3GNT3 inhibited LUAD cell viability and cloning ability, and hindered invasion.

Conclusions

B3GNT3 was highly associated with immune cell infiltration, acting as an important biomarker for the prognosis and diagnosis of LUAD.

ELP6 and PLIN5 Mutations Were Probably Prognostic Biomarkers for Patients With Gastric Cancer

Purpose

Gastric cancer (GC) is the fifth leading cancer around world. And prognosis of patients with GC is still undesirable. Our study aimed to explore potential prognostic biomarkers for patients with GC.

Methods

The clinical samples were collected from the Qinghai University Affiliated Hospital, which were subjected to the whole exome sequencing (WES). The other GC-related data were obtained from The Cancer Genome Atlas (TCGA) database. Cross analyses were done to determine the candidate genes. And the final mutated genes were determined by survival analyses, univariate and multivariate Cox regression analyses. CIBERSORT and GSEA were used for immune cell infiltration analysis and functional enrichment, respectively.

Results

After cross analyses, 160 candidate-mutated genes were identified. And mutated ELP6 and PLIN5 were significantly independently correlated with the overall survival (OS) of patients with GC. Patients with GC with ELP6 and PLIN5 mutations had worse and better prognosis, respectively. Totally 5 types of immune cells were significantly differentially infiltrated in wild-type and mutated ELP6 and PLIN5 GC samples. In mutated ELP6 and PLIN5 GC samples, totally 7 and 11 pathways were significantly enriched, respectively.

Conclusions

The ELP6 and PLIN5 mutations were probably prognostic biomarkers for patients with GC.

Integrated Analysis of miR-7-5p-Related ceRNA Network Reveals Potential Biomarkers for the Clinical Outcome of Gastric Cancer

Gastric cancer (GC) is the second leading cause of tumor-associated death and the fourth most commonly seen tumor across the world. Abnormal ncRNAs have been verified to be involved in potential metastasis via modulating epithelial-to-mesenchymal transition progression and are vital for the progression of cancers. Tumor-infiltrating immune cells (TICs) are a vital indicator of whether cancer patients will benefit from immunotherapy. Nonetheless, the association between ceRNAs and immune cells remained largely unclear. We used the ceRNA network combined with TICs for the prediction of the clinical outcome of GC patients based on TCGA datasets. The percentage of immunocytes in GC was speculated by the use of CIBERSORT. Via Lasso and multivariate assays, prognostic models were established applying survival-related genes and immune cells. Nomograms were developed, and the accuracy of the nomograms was determined using calibration curves. The association between ceRNAs and TICs was validated by the use of integration analysis. In this study, there were 2219 mRNAs (1308 increased and 911 decreased), 171 lncRNAs (51 decreased and 120 increased), and 123 miRNAs (55 decreased and 68 increased) differentially expressed between tumor groups and nontumor groups. Five lncRNAs, six miRNAs, and 64 mRNAs were used for ceRNA network construction. Eight genes including LOX, SPARC, MASTL, PI15, BMPR1B, ANKRD13B, PVT1, and miR-7-5p were applied for the development of the prognostic model. Survival assays suggested that tumor cases with high risk exhibited a shorter overall survival. In addition, we included T-cell CD4 memory activated, monocytes, and neutrophils for the development of a prognosis model. Eventually, our team demonstrated the possible associations between the ceRNA prognosis model and prognostic model based on immune cells. To sum up, the ceRNA network could be used for gene regulation and predict clinical outcomes of GC patients.

CENPF/CDK1 signaling pathway enhances the progression of adrenocortical carcinoma by regulating the G2/M-phase cell cycle

Background

Adrenocortical carcinoma (ACC) is an aggressive and rare malignant tumor and is prone to local invasion and metastasis. And, overexpressed Centromere Protein F (CENPF) is closely related to the oncogenesis of various neoplasms, including ACC. However, the prognosis and exact biological function of CENPF in ACC remains largely unclear.

Methods

In the present essay, the expression patterns and prognostic value of CENPF in ACC were investigated in clinical specimens and public cancer databases, including GEO and TCGA. The potential signaling mechanism of CENPF in ACC was studied based on gene-set enrichment analysis (GSEA). Furthermore, a small RNA interference experiment was conducted to probe the underlying biological function of CENPF in the human ACC cell line, SW13 cells. Lastly, two available therapeutic strategies, including immunotherapy and chemotherapy, have been further explored.

Results

The expression of CENPF in human ACC samples, GEO, and TCGA databases depicted that CENPF was overtly hyper-expressed in ACC patients and positively correlated with tumor stage. The aberrant expression of CENPF was significantly correlated with unfavorable overall survival (OS) in ACC patients. Then, the GSEA analysis declared that CENPF was mainly involved in the G2/M-phase mediated cell cycle and p53 signaling pathway. Further, the in vitro experiment demonstrated that the interaction between CENPF and CDK1 augmented the G2/M-phase transition of mitosis, cell proliferation and might induce p53 mediated anti-tumor effect in human ACC cell line, SW13 cells. Lastly, immune infiltration analysis highlighted that ACC patients with high CENPF expression harbored significantly different immune cell populations, and high TMB/MSI score. The gene-drug interaction network stated that CENPF inhibitors, such as Cisplatin, Sunitinib, and Etoposide, might serve as potential drugs for the therapy of ACC.

Conclusion

The result points out that CENPF is significantly overexpressed in ACC patients. The overexpressed CENPF predicts a poor prognosis of ACC and might augment the progress of ACC. Thus, CENPF and related genes might serve as a novel prognostic biomarker or latent therapeutic target for ACC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03277-y.

CDK3, CDK5 and CDK8 Proteins as Prognostic and Potential Biomarkers in Colorectal Cancer Patients

Introduction

Methods

Results

Conclusion