Multifactorial Cox Regression Analysis Research Articles

Individualised prediction of chemotherapy benefit in early-onset colorectal cancer

PurposesWhether patients with early-onset colorectal cancer (EOCRC) receive chemotherapy has been controversial, so our study aimed to screen patients with EOCRC who benefit from chemotherapy.MethodsA total of 2166 EOCRC patients were identified from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were divided into chemotherapy and non-chemotherapy groups, propensity score matching (PSM) was performed to balance the differences between the groups, and the Kaplan–Meier method was used to calculate the cancer-specific survival (CSS) of EOCRC patients. Multifactorial COX regression analysis was used to identify independent prognostic factors for CSS and to construct a nomogram for predicting CSS in EOCRC patients. The overall risk score was calculated based on Nomogram, and EOCRC patients were classified into high-risk and low-risk groups to assess further chemotherapy's therapeutic effect on patients with different risk stratification.ResultsBefore PSM, patients in the chemotherapy group had poorer CSS (p < 0.001). After PSM, there was no significant difference in patient CSS between the two groups (p = 0.057). Independent prognostic factors (Race, Grade, Pathology, AJCC.N, AJCC.M, CEA, Marital. Status) were screened according to multifactorial COX regression analyses and included in the Nomogram predicting CSS in EOCRC patients. A risk stratification system for EOCRC patients was further developed, and the results showed that chemotherapy had no significant effect on CSS in the low-risk group of patients, but in the high-risk group, chemotherapy significantly improved CSS in EOCRC patients.ConclusionsWe developed a clinical risk model by combining different risk factors, which can accurately screen those with high-risk EOCRC for benefit from chemotherapy. For low-risk EOCRC patients, our results did not observe a better survival benefit from chemotherapy, and more prospective studies are needed in the future to prove our conclusions.

Machine learning and single-cell RNA sequencing reveal relationship between intratumor CD8+ T cells and uveal melanoma metastasis

PurposeUveal melanoma (UM) is adults’ most common primary intraocular malignant tumor. It has been observed that 40% of patients experience distant metastasis during subsequent treatment. While there exist multigene models developed using machine learning methods to assess metastasis and prognosis, the immune microenvironment’s specific mechanisms influencing the tumor microenvironment have not been clarified. Single-cell transcriptome sequencing can accurately identify different types of cells in a tissue for precise analysis. This study aims to develop a model with fewer genes to evaluate metastasis risk in UM patients and provide a theoretical basis for UM immunotherapy.MethodsRNA-seq data and clinical information from 79 μm patients from TCGA were used to construct prognostic models. Mechanisms were probed using two single-cell datasets derived from the GEO database. After screening for metastasis-related genes, enrichment analysis was performed using GO and KEGG. Prognostic genes were screened using log-rank test and one-way Cox regression, and prognostic models were established using LASSO regression analysis and multifactor Cox regression analysis. The TCGA-UVM dataset was used as internal validation and dataset GSE22138 as external validation data. A time-dependent subject work characteristic curve (time-ROC) was established to assess the predictive ability of the model. Subsequently, dimensionality reduction, clustering, pseudo-temporal analysis and cellular communication analysis were performed on GSE138665 and GSE139829 to explore the underlying mechanisms involved. Cellular experiments were also used to validate the relevant findings.ResultsBased on clinical characteristics and RNA-seq transcriptomic data from 79 samples in the TCGA-UVM cohort, 247 metastasis-related genes were identified. Survival models for three genes (SLC25A38, EDNRB, and LURAP1) were then constructed using lasso regression and multifactorial cox regression. Kaplan-Meier survival analysis showed that the high-risk group was associated with poorer overall survival (OS) and metastasis-free survival (MFS) in UM patients. Time-dependent ROC curves demonstrated high predictive performance in 6 m, 18 m, and 30 m prognostic models. Cell scratch assay showed that the 24 h and 48 h migration rates of cells with reduced expression of the three genes were significantly higher than those of the si-NC group. CD8 + T cells may play an important role in tumour metastasis as revealed by immune infiltration analysis. An increase in the percentage of cytotoxic CD8 + T cells in the metastatic high-risk group was found in the exploration of single-cell transcriptome data. The communication intensity of cytotoxic CD8 was significantly enhanced. It was also found that the CD8 + T cells in the two groups were in different states, although the number of CD8 + T cells in the high-risk group increased, they were mostly in the exhausted and undifferentiated state, while in the low-risk group, the CD8 + T cells were mostly in the functional state.ConclusionsWe developed a precise and stable 3-gene model to predict the metastatic risk and prognosis of patients. CD8 + T cells exhaustion in the tumor microenvironment play a crucial role in UM metastasis.

Investigation of TMEM41A's function in breast cancer prognosis and its connection to immune cell infiltration.

Globally, breast cancer is the most common type of malignant tumor. It has been demonstrated that TMEM41A is abnormally expressed in a number of cancers and is linked to a dismal prognosis. TMEM41A's involvement in breast cancer remains unknown, though. Data from databases such as TCGA were used in this study. Expression differences were compared using non-parametric tests. Cox regression analysis was employed, and analyses such as Nomogram were used to assess the significance of TMEM41A in predicting the prognosis of breast cancer. Lastly, it was looked into how immune cell infiltration in breast cancer is related to TMEM41A expression levels. The results suggest that TMEM41A is overexpressed in breast cancer and correlates with poor prognosis (P = 0.01), particularly in early-stage and ductal A breast cancer (P < 0.01). Breast cancer patients' expression of TMEM41A was found to be an independent risk factor (HR = 1.132, 95% CI 1.036-1.237) by multifactorial Cox regression analysis. The Nomogram prediction model's c-index was 0.736 (95% CI 0.684-0.787). The results of GSEA biofunctional enrichment analysis included the B cell receptor signaling pathway (P < 0.05). Ultimately, there was a significant correlation (P < 0.05) between TMEM41A expression in breast cancer and an infiltration of twenty immune cells. Breast cancer tissues overexpress TMEM41A, which is linked to immune cell infiltration and prognosis (particularly in early stage and luminal A breast cancer). Overexpression of TMEM41A is anticipated to serve as a novel prognostic indicator and therapeutic target for breast cancer.

A novel prognostic model based on telomere-related lncRNAs in gastric cancer.

Telomeres are specialized structures at the ends of chromosomes that are important for their protection. Over time, long non-coding RNAs (lncRNAs) have gradually come into the spotlight as essential biomarkers of proliferation, migration, and invasion of human malignant tumors. Nevertheless, the impact of telomere-related lncRNAs (TRLs) in gastric cancer is currently unknown. In the present study, we screen the TRLs and identify a prognostic TRLs signature in gastric cancer. First, telomere-related genes (TRGs) were retrieved from the website, and RNA sequencing (RNA-seq) data and clinical data of stomach adenocarcinoma (STAD) patients were gathered from The Cancer Genome Atlas (TCGA) database. Gastric cancer patients' lncRNAs and overall survival (OS) were found to be related using univariate Cox regression analysis. Next, least absolute shrinkage and selection operator (LASSO) regression analysis and multifactorial Cox regression analysis were used to further screen telomere-related differentially expressed lncRNAs (TRDELs), and finally six lncRNAs were obtained, including LINC01537, CFAP61-AS1, DIRC1, RABGAP1L-IT1, DBH-AS1, and REPIN1-AS1. According to these six TRDELs, a prognostic model for gastric cancer was constructed. The samples were divided into the training group and the testing group at random, and the reliability of prognostic model was validated in both groups and overall samples. In addition, we performed Kaplan-Meier (K-M) survival curve analysis, independent prognostic analysis, and functional enrichment analysis to validate the predictive value and independence of the model, as well as immune cell correlation analysis, clustering analysis, and principal component analysis (PCA) to further explore the relationship between this model and the tumor cells. Finally, we performed the drug sensitivity analysis to identify a few small molecules that may have a therapeutic effect on gastric cancer. Finally, we constructed a prognostic model for gastric cancer consisting of six TRDELs. According to the K-M curve, the prognosis of the low-risk group was noticeably superior than that of the high-risk group. Multivariate Cox regression analysis suggested that risk score was an independent prognostic element. Receiver operating characteristic (ROC) curves, nomogram, and calibration curve indicated that the prognostic model had good predictive ability. Functional enrichment analysis demonstrated major pathways with high- and low-risk groups. Next, both tumor microenvironment (TME) and immune correlation analysis showed discrepancy in the high- and low-risk groups. Through drug sensitivity analysis, we screened four small molecules that might be beneficial for gastric cancer treatment. A prognostic model consisting of these six TRDELs was capable to predict the prognosis of gastric cancer patients.

Nomogram for predicting outcomes in elderly women with mucinous breast cancer: A retrospective study combined with external validation in southwest China.

Mucinous breast cancer (MBC) is a kind of breast cancer (BC), which is rare in clinic, mainly for women, because of the low incidence rate, so there is no unified standard treatment protocol. Elderly patients have a poor prognosis due to their combined comorbidities. This study aims to investigate the effect of surgery and chemoradiotherapy on the prognosis of elderly female MBC patients and construct nomograms for predicting the OS and CSS in elderly female MBC patients. Data for female MBC patients over 65 years are obtained from the Surveillance, Epidemiology and End Results (SEER) database, patients were divided into two groups: the training set and the validation set. External validation data of the prediction model were provided by Kunming Hospital of Traditional Chinese Medicine. We used Cox regression modeling, which was used to identify independent risk factors affecting patient prognosis. After avoiding confounding bias according to the multifactorial Cox regression model, we used these screened statistically significant results to construct column-line plots. The performance of the model was tested using the consistency index (c-index), the calibration curve, and the area under the operating characteristic curve of the receiver (AUC). Subsequently, we used decision curve analysis (DCA) to examine the potential clinical value of our nomograms. A total of 8103 elderly MBC female patients were extracted from the database SEER and were assigned to the training and validation set, randomly. A total of 83 patients from Kunming Hospital of Traditional Chinese Medicine were used in the external verification set. After multifactorial Cox regression analysis, we found that age, race, T-stage, M-stage, surgical approach, radiotherapy, and tumor size were independent risk factors for OS in elderly MBC patients. Similarly, independent risk factors of CSS included age, marital status, N stage, M stage, surgical approach, chemotherapy, and tumor size. The C-index for the OS training, validation, and external verification set were 0.731 (95%CI 0.715-0.747), 0.738 (95%CI 0.724-0.752), and 0.809 (95%CI 0.731-0.8874). The C-index of the training set, the validation set, and external verification set for CSS were 0.786 (95%CI 0.747-0.825), 0.776 (95%CI 0.737-0.815), and 0.84 (95%CI0.754-0.926), respectively. The AUC, calibration curves and DCA also showed good accuracy. In this study, we construct a new nomogram to predict the prognosis of elderly patients with MBC. The nomograms have undergone internal and external validation and have been confirmed to have good clinical applicability. At the same time, we found that for elderly female MBC patients, surgery and radiotherapy significantly benefit their survival, but chemotherapy is not conducive to patient survival.

Development and validation of a model for predicting the early occurrence of RF in ICU-admitted AECOPD patients: a retrospective analysis based on the MIMIC-IV database

BackgroundThis study aims to construct a model predicting the probability of RF in AECOPD patients upon hospital admission.MethodsThis study retrospectively extracted data from MIMIC-IV database, ultimately including 3776 AECOPD patients. The patients were randomly divided into a training set (n = 2643) and a validation set (n = 1133) in a 7:3 ratio. First, LASSO regression analysis was used to optimize variable selection by running a tenfold k-cyclic coordinate descent. Subsequently, a multifactorial Cox regression analysis was employed to establish a predictive model. Thirdly, the model was validated using ROC curves, Harrell’s C-index, calibration plots, DCA, and K-M curve.ResultEight predictive indicators were selected, including blood urea nitrogen, prothrombin time, white blood cell count, heart rate, the presence of comorbid interstitial lung disease, heart failure, and the use of antibiotics and bronchodilators. The model constructed with these 8 predictors demonstrated good predictive capabilities, with ROC curve areas under the curve (AUC) of 0.858 (0.836–0.881), 0.773 (0.746–0.799), 0.736 (0.701–0.771) within 3, 7, and 14 days in the training set, respectively and the C-index was 0.743 (0.723–0.763). Additionally, calibration plots indicated strong consistency between predicted and observed values. DCA analysis demonstrated favorable clinical utility. The K-M curve indicated the model’s good reliability, revealed a significantly higher RF occurrence probability in the high-risk group than that in the low-risk group (P < 0.0001).ConclusionThe nomogram can provide valuable guidance for clinical practitioners to early predict the probability of RF occurrence in AECOPD patients, take relevant measures, prevent RF, and improve patient outcomes.

Exploring GZMK as a prognostic marker and predictor of immunotherapy response in breast cancer: unveiling novel insights into treatment outcomes

BackgroundGranzyme K (GZMK) is a crucial mediator released by immune cells to eliminate tumor cells, playing significant roles in inflammation and tumorigenesis. Despite its importance, the specific role of GZMK in breast cancer and its mechanisms are not well understood.MethodsWe utilized data from the TCGA and GEO databases and employed a range of analytical methods including GO, KEGG, GSEA, ssGSEA, and PPI to investigate the impact of GZMK on breast cancer. In vitro studies, including RT-qPCR, CCK-8 assay, cell cycle experiments, apoptosis assays, Celigo scratch assays, Transwell assays, and immunohistochemical methods, were conducted to validate the effects of GZMK on breast cancer cells. Additionally, Cox regression analysis integrating TCGA and our clinical data was used to develop an overall survival (OS) prediction model.ResultsAnalysis of clinical pathological features revealed significant correlations between GZMK expression and lymph node staging, differentiation grade, and molecular breast cancer subtypes. High GZMK expression was associated with improved OS, progression-free survival (PFS), and recurrence-free survival (RFS), as confirmed by multifactorial Cox regression analysis. Functional and pathway enrichment analyses of genes positively correlated with GZMK highlighted involvement in lymphocyte differentiation, T cell differentiation, and T cell receptor signaling pathways. A robust association between GZMK expression and T cell presence was noted in the breast cancer tumor microenvironment (TME), with strong correlations with ESTIMATEScore (Cor = 0.743, P < 0.001), ImmuneScore (Cor = 0.802, P < 0.001), and StromalScore (Cor = 0.516, P < 0.001). GZMK also showed significant correlations with immune checkpoint molecules, including CTLA4 (Cor = 0.856, P < 0.001), PD-1 (Cor = 0.82, P < 0.001), PD-L1 (Cor = 0.56, P < 0.001), CD48 (Cor = 0.75, P < 0.001), and CCR7 (Cor = 0.856, P < 0.001). Studies indicated that high GZMK expression enhances patient responsiveness to immunotherapy, with higher levels observed in responsive patients compared to non-responsive ones. In vitro experiments confirmed that GZMK promotes cell proliferation, cell division, apoptosis, cell migration, and invasiveness (P < 0.05).ConclusionOur study provides insights into the differential expression of GZMK in breast cancer and its potential mechanisms in breast cancer pathogenesis. Elevated GZMK expression is associated with improved OS and RFS, suggesting its potential as a prognostic marker for breast cancer survival and as a predictor of the efficacy of immunotherapy.

Associations between pinch strength, cardiovascular events and all-cause mortality in patients undergoing maintenance hemodialysis

Background and aimsPatients undergoing maintenance hemodialysis (MHD) experience increased mortality and cardiovascular disease (CVD) risks; however, the potential connection between pinch strength (PS) and the prognosis of these patients remains unknown. Consequently, this study aimed to comprehensively assess the influence of PS and handgrip strength (HGS) on both survival and cardiovascular events (CVE) in patients undergoing MHD.MethodsData were gathered from patients undergoing MHD at the Hemodialysis Center of Guangzhou Red Cross Hospital in March 2021. We performed a retrospective follow-up spanning 24 months, with death serving as the primary endpoint for observation and CVE as the secondary endpoint. Multifactorial Cox regression analysis, Kaplan–Meier survival curves, trend tests, and restricted cubic spline were applied to explore the association.ResultsDuring a 24-month follow-up, data were collected from 140 patients undergoing MHD with an average age of 66.71 ± 12.61 years. Among them, 52 (37.14%) experienced mortality, whereas 36 (40.00%) had CVE without baseline CVD. Kaplan–Meier survival curves demonstrated better survival rates and reduced CVE risk for patients in the second, third, and fourth quartiles compared with those in the first quartile for PS. Adjusted analyses in different models revealed higher PS levels were independently associated with all-cause mortality (major model, model 4, HR, 0.78; 95% CI, 0.64–0.95) but not with CVE risk (unadjusted HR, 0.90; 95% CI, 0.77–1.05). Compared with lower quartile PS levels, higher PS levels significantly reduced all-cause mortality (HR, 0.31; 95% CI, 0.10–1.02), and this trend remained consistent (P for trend = 0.021). Finally, the restricted cubic spline method using different models showed a linear relationship between PS and all-cause mortality (P > 0.05), when PS exceeded 4.99 kg, the all-cause mortality of MHD patients significantly decreased.ConclusionsPS was independently associated with all-cause mortality but not with CVE in patients undergoing MHD.

Identifying lncRNAs and mRNAs related to survival of NSCLC based on bioinformatic analysis and machine learning.

Non-small cell lung cancer (NSCLC) is the most common histopathological type, and it is purposeful for screening potential prognostic biomarkers for NSCLC. This study aims to identify the lncRNAs and mRNAs related to survival of non-small cell lung cancer (NSCLC). The expression profile data of lung adenocarcinoma and lung squamous cell carcinoma were downloaded in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) dataset. A total of eight survival related long non-coding RNAs (lncRNAs) and 262 survival related mRNAs were filtered. By gene set enrichment analysis, 17 significantly correlated Gene Ontology signal pathways and 14 Kyoto Encyclopedia of Genes and Genomes signal pathways were screened. Based on the clinical survival and prognosis information of the samples, we screened eight lncRNAs and 193 mRNAs by single factor Cox regression analysis. Further single and multifactor Cox regression analysis were performed, 30 independent prognostication-related mRNAs were obtained. The PPI network was further constructed. We then performed the machine learning algorithms (Least absolute shrinkage and selection operator, Recursive feature elimination, and Random forest) to screen the optimized DEGs combination, and a total of 17 overlapping mRNAs were obtained. Based on the 17 characteristic mRNAs obtained, we firstly built a Nomogram prediction model, and the ROC values of training set and testing set were 0.835 and 0.767, respectively. By overlapping the 17 characteristic mRNAs and PPI network hub genes, three genes were obtained: CDC6, CEP55, TYMS, which were considered as key factors associated with survival of NSCLC. The in vitro experiments were performed to examine the effect of CDC6, CEP55, and TYMS on NSCLC cells. Finally, the lncRNAs-mRNAs networks were constructed.

Clinical outcome of non-curative endoscopic submucosal dissection for early gastric cancer.

Early gastric cancer (EGC) is defined as cancer cells confined to the mucosal or submucosal layer, irrespective of size or presence of lymph node metastasis. The recent EGC endoscopic submucosal dissection (ESD) and endoscopic mucosal resection (EMR) guidelines (2021 Japan Gastroenterological Endoscopy Society (JGES) guidelines, 2nd edition) revised the concept from "endoscopic curative/non-curative resection" (NCR) to "endoscopic curability (eCura)". Under this, eCuraA and eCuraB signify curative resections (CRs), while eCuraC (including eCuraC-1 and eCura-C2) indicate NCRs. This study retrospectively analyzes clinical and pathological data from EGC patients who underwent endoscopic resection, assessing the long-term clinical outcomes in a substantial cohort after undergoing NCR. We retrospectively analyzed clinical and pathological data from 443 EGC patients, encompassing 478 lesions, who received endoscopic treatment. The long-term clinical outcomes of patients who underwent NCR were statistically evaluated. Characteristics of the NCR group were compared with those of the surgical group, employing single- and multi-factor logistic regression analyses to identify risk factors that necessitate further surgical intervention. Prognostically, the Kaplan-Meier method and Log-Rank test determined the impact of risk factors on recurrence-free survival post-surgery in NCR patients. Differences were assessed using a method incorporating statistically significant differences in the multi-factor Cox regression analysis, evaluating the hazard ratio (HR) for disease recurrence following NCR. In this study, 443 EGC cases were pathologically diagnosed, comprising a total of 478 lesions. Of these, 127 cases underwent non-curative endoscopic resection, resulting in a NCR rate of 24.4%. Long-term follow-up was achieved for 117 (92.12%) patients. The metastasis/recurrence rate at 6 months stood at 23.1%. Multivariate Cox regression analysis identified lesion size ≥2.0 and <3 cm [P=0.02, HR =0.12, 95% confidence interval (CI): 0.02-0.67], presence of ulceration (P=0.03, HR =5.48, 95% CI: 1.23-24.33), lymphatic invasion (P=0.05, HR =17.51, 95% CI: 1.07-286.23), positive vertical margins (P=0.09, HR =3.77, 95% CI: 0.81-17.53), and flat macroscopic morphology (P=0.048, HR =4.8, 95% CI: 1.01-22.73) as independent risk factors for recurrence-free survival post non-curative endoscopic resection in EGC patients. The recurrence/metastasis rate in patients who underwent NCR is notably higher compared to the control group. Significant prognostic risk factors include tumor size ≥2.0 and <3 cm, positive vertical margins, lymphatic invasion, and flat type (one of pathological gross classification). Patients in the eCuraC-2 category of NCR should consider further surgical intervention. The necessity for additional surgical intervention in these patients warrants further investigation.

Identification and validation of ferroptosis-related gene SLC2A1 as a novel prognostic biomarker in AKI.

Emerging evidence reveals the key role of ferroptosis in the pathophysiological process of acute kidney injury (AKI). Our study aimed to investigate the potential ferroptosis-related gene in AKI through bioinformatics and experimental validation. The AKI single-cell sequencing dataset was retrieved from the GEO database and ferroptosis-related genes were extracted from the GENECARD website. The potential differentially expressed ferroptosis-related genes of AKI were selected. Functional enrichment analysis was performed. Machine learning algorithms were used to identify key ferroptosis-related genes associated with AKI. A multi-factor Cox regression analysis was used to construct a risk score model. The accuracy of the risk score model was validated using receiver operating characteristic (ROC) curve analysis. We extensively explored the immune landscape of AKI using CIBERSORT tool. Finally, expressions of ferroptosis DEGs were validated in vivo and in vitro by Western blot, ICH and transfection experiments. Three hub genes (BAP1, MDM4, SLC2A1) were identified and validated by constructing drug regulatory network and subsequent screening using experimentally determined interactions. The risk mode showed the low-risk group had significantly better prognosis compared to high-risk group. The risk score was independently associated with overall survival. The ROC curve analysis showed that the prognosis model had good predictive ability. Additionally, CIBERSORT immune infiltration analysis suggest that the hub gene may influence cell recruitment and infiltration in AKI. Validation experiments revealed that SLC2A1 functions by regulating ferroptosis. In summary, our study not only identifies SLC2A1 as diagnostic biomarker for AKI, but also sheds light on the role of it in AKI progression, providing novel insights for the clinical diagnosis and treatment of AKI.

Development and validation of a machine learning model to predict prognosis in liver failure patients treated with non-bioartificial liver support system.

The prognosis of liver failure treated with non-bioartificial liver support systems is poor. Detecting its risk factors and developing relevant prognostic models still represent the top priority to lower its death risk. All 215 patients with liver failure treated with non-bioartificial liver support system were retrospectively analyzed. Potential prognostic factors were investigated, and the Nomogram and the Random Survival Forests (RSF) models were constructed, respectively. Notably, we evaluated the performance of models and calculated the risk scores to divide patients into low-risk and high-risk groups. In the training set, multifactorial Cox regression analysis showed that etiology, hepatic encephalopathy, total bilirubin, serum alkaline phosphatase, platelets, and MELD score were independent factors of short-term prognosis. The RSF model (AUC: 0.863, 0.792) performed better in prediction than the Nomogram model (AUC: 0.816, 0.756) and MELD (AUC: 0.658, 0.700) in the training and validation groups. On top of that, patients in the low-risk group had a significantly better prognosis than those in the high-risk group. We constructed the RSF model with etiology, hepatic encephalopathy, total bilirubin, serum alkaline phosphatase, platelets, and MELD score, which showed better prognostic power than the Nomogram model and MELD score and could help physicians make optimal treatment decisions.

Prognostic value of coagulation markers in patients with colorectal caner: A prospective study.

The occurrence, growth, and metastasis of colorectal cancer (CRC) are connected to the hypercoagulable state of blood (CRC). This study aimed to identify significant coagulation factors to predict metastasis and prognosis of CRC. Thrombomodulin (TM), thrombin-antithrombin complex (TAT), α2-plasmininhibitor-plasmin complex (PIC), and tissue plasminogen activator-inhibitor complex (t-PAIC) were detected by chemiluminescence immunoassay using Sysmex HISCL5000 automated analyzers. The Sysmex CS 5100 automatic blood coagulation analyzer was used to detect d-dimer (DD), fibrin degradation product (FDP), prothrombin time (PT), thrombin time (TT), international normalized ratio (INR), fibrinogen (Fbg), and activated partial thromboplastin time (APTT). Area under the curve (AUC) and the receiver operating characteristic curve (ROC) were used to assess the diagnostic efficacy of markers. Kaplan-Meier analysis was used to calculate survival probabilities. Independent prognostic factors and the nomogram were developed using single-factor and multifactor cox regression analysis model. The following indicators (TM, TAT, PIC, t-PAIC, DD, FDP, PT, INR, APTT, and Fbg) were markedly higher in CRC patients than in healthy controls, and they were higher in the metastasis (M) group than in the nonmetastasis (NM) group. The combination "TAT + PIC + DD + FDP + Fbg" can distinguish M from NM with exceptional sensitivity and specificity. Patients with CRC who had high levels of TAT, PIC, DD, FDP, Fbg, TM, tPAIC, PT, and INR had significantly shorter survival. The prognosis of CRC patients can be predicted by coagulation indicators. The independent predictive variables for overall survival were found to be TM and DD. To forecast CRC patient survival, a nomogram was created.

Identification of key ferroptosis genes and mechanisms associated with breast cancer using bioinformatics, machine learning, and experimental validation.

The aim of this paper is to mine ferroptosis genes associated with breast cancer based on bioinformatics and machine learning, and to perform in vitro functional validation. Transcriptional and clinical data of breast cancer patients were downloaded from TCGA database and ferroptosis-related genes were obtained from FerrDB database. Significant differentially expressed ferroptosis-related genes between breast cancer tissues and adjacent normal tissues were selected. Functional enrichment analysis was performed on these differentially expressed genes. Four machine learning algorithms were used to identify key ferroptosis-related genes associated with breast cancer. A multi-factor Cox regression analysis was used to construct a risk score model for the key ferroptosis-related genes. The accuracy of the risk score model was validated using Kaplan-Meier survival curve analysis and receiver operating characteristic (ROC) curve analysis. Finally, cell experiments were conducted to validate the biological functions of the key ferroptosis-related genes in breast cancer cells MCF-7, further confirming the accuracy of the analysis results. A total of 52 significantly differentially expressed ferroptosis-related genes were identified, which were mainly enriched in cancer pathways, central carbon metabolism in cancer, HIF-1 signaling pathway, and NOD-like receptor signaling pathway. Three key ferroptosis-related genes (TXNIP, SLC2A1, ATF3) closely related to the occurrence, development, and prognosis of breast cancer were identified using machine learning algorithms. The risk model constructed using these three key ferroptosis-related genes showed that the prognosis of the low-risk group was better than that of the high-risk group (P < 0.001). The ROC curve analysis showed that the prognosis model had good predictive ability. In vitro experiments validated the reliability of the bioinformatics and machine learning screening results. Downregulation of SLC2A1 expression promoted ferroptosis and suppressed tumor cell growth in breast cancer cells (P < 0.01), while overexpression of TXNIP or ATF3 had the same effect (P < 0.01). This study identified three key ferroptosis-related genes (TXNIP, SLC2A1, ATF3) associated with breast cancer, which are closely related to the occurrence, development, and prognosis of breast cancer.

Prognostic nomograms for breast cancer with lung metastasis: a SEER-based population study

BackgroundLung metastasis is a significant adverse predictor of prognosis in patients with breast cancer. Accurate estimation for the prognosis of patients with lung metastasis and population-based validation for the models are lacking. In the present study, we aimed to establish the nomogram to identify prognostic factors correlated with lung metastases and evaluate individualized survival in patients with lung metastasis based on SEER (Surveillance, Epidemiology, and End Results) database.MethodsWe selected 1197 patients diagnosed with breast cancer with lung metastasis (BCLM) from the SEER database and randomly assigned them to the training group (n = 837) and the testing group (n = 360). Based on univariate and multivariate Cox regression analysis, we evaluated the effects of multiple variables on survival in the training group and constructed a nomogram to predict the 1-, 2-, and 3-year survival probability of patients. The nomogram were verified internally and externally by Concordance index (C-index), Net Reclassification (NRI), Integrated Discrimination Improvement (IDI), Decision Curve Analysis (DCA), and calibration plots.ResultsAccording to the results of multi-factor Cox regression analysis, age, histopathology, grade, marital status, bone metastasis, brain metastasis, liver metastasis, human epidermal growth factor receptor 2 (HER2), estrogen receptor (ER), progesterone receptor (PR), surgery, neoadjuvant therapy and chemotherapy were considered as independent prognostic factors for patients with BCLM. The C-index in the training group was 0.719 and the testing group was 0.695, respectively. The AUC values of the 1-, 2-, and 3-year prognostic nomogram in the training group were 0.798, 0.790 and 0.793, and the corresponding AUC values in the testing group were 0.765, 0.761 and 0.722. The calculation results of IDI and NRI were shown. The nomograms significantly improved the risk reclassification for 1-, 2-, and 3-year overall mortality prediction compared with the AJCC 7th staging system. According to the calibration plot, nomograms showed good consistency between predicted and actual overall survival (OS) values for the patients with BCLM. DCA showed that nomograms had better net benefits at different threshold probabilities at different time points compared with the AJCC 7th staging system.ConclusionsNomograms that predicted 1-, 2-, and 3-year OS for patients with BCLM were successfully constructed and validated to help physicians in evaluating the high risk of mortality in breast cancer patients.

Immunohistochemical markers Ki67 and P16 help predict prognosis in locally advanced cervical cancer

ObjectiveTo investigate the relationship between Ki-67 and P16 expression levels after neoadjuvant chemotherapy, and the clinicopathological characteristics and prognosis of patients with locally advanced cervical cancer. MethodsPatients with FIGO 2009 stage IB2 or IIA2 cervical cancer, who underwent neoadjuvant chemotherapy combined with radical hysterectomy at the First Affiliated Hospital of Chongqing Medical University between January 2015 and December 2019, were identified retrospectively to correlate postoperative Ki-67 and P16 expression levels with clinicopathological factors. The optimal threshold for predicting recurrence was analysed using receiver operating characteristic (ROC) curves for the Ki-67 index, and univariate and multi-factorial Cox regression analysis were used to investigate the association between clinicpathological features including Ki-67 and P16 and recurrence-free survival. ResultsIn total, 334 patients were included after screening. The cut-off value of Ki-67 for determining recurrence was 67.5 % according to the ROC curve. On multi-factorial Cox analysis, lymphatic vascular space (p = 0.003) and Ki-67 index (p = 0.005) were shown to increase the risk of recurrence, and were independent prognostic factors for recurrence, while the expression of P16 was not significantly associated with the risk of recurrence (p = 0.097, odds ratio = 0.319). Patients with cervical cancer in the high Ki-67 expression group (Ki-67 ≥ 67.5 %) had lower recurrence-free survival and overall survival than patients in the low Ki-67 expression group (Ki-67 < 67.5 %) (p = 0.001 and 0.036, respectively). ConclusionThe expression levels of Ki-67 and P16 after neoadjuvant chemotherapy for locally advanced cervical cancer correlated with tumour differentiation. High expression of Ki-67 (Ki-67 ≥ 67.5 %) may indicate poorer recurrence-free survival and overall survival.

Effects of TP regimen combined with intraperitoneal hyperthermic perfusion chemotherapy on immune function, quality of life and prognosis of patients with advanced ovarian cancer.

To evaluate the effect of paclitaxel and cisplatin (TP regimen) combined with intraperitoneal hyperthermic perfusion chemotherapy on immune function and quality of life in patients with advanced ovarian cancer. This retrospective study involved 107 patients with advanced ovarian cancer who were treated in Baoji Central Hospital between March 2016 and March 2020. The control group was treated with the TP regimen alone (n=48), while the observation group received additional intraperitoneal heat infusion chemotherapy containing 60 mg of cisplatin on the 8th day following the final chemotherapy (n=59). Immunoglobulin (IgG, IgA, IgM) levels, quality of life, tumor marker levels, incidence of adverse effects, and 3-year survival were compared between the two groups. Besides, factors affecting patients' prognosis were detected by unifactorial and multifactorial analyses. Before treatment, there was no significant difference between the two groups in terms of IgG, IgA, and IgM levels (all P>0.05). After treatment, the observation group showed significantly higher levels of IgG, IgA, and IgM than those in the control group (all P<0.05). There were no significant differences in pre-treatment Kamofsky (KPS) score, carcinoembryonic antigen (CEA), and carbohydrate antigen 125 (CA125) between the two groups (all P>0.05). However, after treatment, the KPS score was significantly increased in the observation group as compared to pre-treatment or control group (both P<0.05), while CEA and CA125 significantly decreased in the observation group as compared to pre-treatment or control group (all P<0.05). Nevertheless, the incidence of gastrointestinal reactions in the observation group was higher than that in the control group (P<0.05). The survival rate of the observation group was significantly higher than that of the control group (P<0.05). The AUC of post-treatment IgG for predicting 3-year survival of patients was 0.743. The 3-year survival rate of patients with IgG≥10.950 g/L was significantly higher than that of patients with IgG<10.950 g/L (P<0.05). Multifactorial Cox regression analysis revealed that higher FIGO stage, presence of ascites, higher post-treatment IgG level, and higher post-treatment CEA and CA125 levels were independent risk factors for patients' 3-year mortality. TP regimen combined with intraperitoneal hyperthermic perfusion chemotherapy significantly improves immune function and quality of life in patients with advanced ovarian cancer, although it increases the incidence of gastrointestinal reactions. Higher FIGO stage, presence of ascites, higher IgG after treatment, higher CEA, and higher CA125 were independent risk factors for patients' 3-year mortality.

Application value of laparoscopic surgery in elderly patients (≥ 75 years) with colorectal cancer and prognostic factors influencing 5-year overall survival.

To investigate the application value of laparoscopic surgery in elderly patients (≥ 75 years) with colorectal cancer, and to identify the prognostic factors influencing the long-term survival in this demographic, and to establish a predictive nomogram model. A retrospective analysis was conducted on 146 elderly (≥ 75 years old) colorectal cancer patients who underwent radical surgery in Baoji People's Hospital from August 2016 to February 2018, including 55 patients who underwent laparotomy and 91 patients who underwent laparoscopic surgery. Survival curves were plotted using the Kaplan-Meier method, and differences in prognosis were assessed using the Log-rank test. Prognostic impacts of various factors on 5-year survival were analyzed using a Cox proportional hazards model. Significant predictors identified in the Cox model were used to construct a nomogram for predicting survival, which was then validated for accuracy and clinical utility. Laparoscopic surgery was associated with shorter hospital stays (P = 0.022), although at a higher cost (P = 0.011). The laparoscopic group also had less intraoperative bleeding (P < 0.001), incision length (P < 0.001), time to first postoperative expectoration (P < 0.001), time to first postoperative feeding (P = 0.002), and time to postoperative peritoneal drainage (P = 0.003) compared to the open surgery group. Additionally, the rate of postoperative wound complications was also lower in the laparoscopic group (P = 0.014). There was no significant difference in the 5-year post-treatment survival between the two groups (P = 0.150). Multifactorial Cox regression analysis revealed that a history of diabetes mellitus (P = 0.037), vascular infiltration (P = 0.026), nerve bundle invasion (P = 0.001), and TNM stage (P = 0.001) were independent prognostic factors affecting the 5-year survival of patients with advanced colorectal cancer. The constructed nomogram showed high predictive accuracy for 1-, 3-, and 5-year survival, with AUC values of 0.91, 0.87, and 0.79, respectively. Calibration curves and decision curve analysis confirmed the model's clinical utility. Risk formula: History of diabetes mellitus * -0.696194503 + Vascular infiltration * -0.769736513 + Nerve bundle invasion * -1.1709777 + TNM staging * 1.201933691. Laparoscopic surgery can reduce intraoperative trauma and accelerate postoperative recovery in elderly colorectal cancer patients (≥ 75 years) compared to open surgery. The developed nomogram model based on independent prognostic factors such as diabetes history, vascular infiltration, nerve bundle invasion, and TNM staging, facilitates tailored prognostic assessment, enhancing individual patient management.

Development and validation of prognostic nomograms for overall survival in patients with locally advanced colorectal cancer

BackgroundMany colorectal cancer (CRC) patients are initially diagnosed at an advanced stage. Some evidence indicating that TNM staging may not be entirely reliable in forecasting survival outcomes. The research aims to develop accurate prediction models that can determine the survival outcomes of patients with clinically locally advanced colorectal cancer (LACRC). The study aims to provide valuable insights for clinical professionals to make right clinical decisions. MethodsWe retrospectively analyzed 636 LACRC patients admitted to the General Hospital of Western Theater Command between Jan 1, 2017, and Dec 31, 2019. Significant factors determining the prognosis of LACRC patients screened by lasso regression and multifactorial cox regression analysis. These factors were utilized to develop a nomogram model, which serves as a valuable predictive tool for estimating the overall survival (OS) of LACRC patients. Next, we validated the nomogram in the validation group. ResultsThe nomogram emphasized that the TNM stage holds the most prognostic value, followed by CA19–9 levels, smoking history, and MLR. Multiple assessments confirmed the superior discriminatory ability of the nomograms. ConclusionsWe have developed and verified nomograms that can predict OS cohort 635 LACRC patients. The survival nomogram may assist clinicians in better strategizing the therapeutic options for patients with LACRC.

P16 and p53 can Serve as Prognostic Markers for Head and Neck Squamous Cell Carcinoma

ObjectiveThe present study aimed to explore the expression and clinical significance of human papilloma virus-related pathogenic factors (p16, cyclin D1, p53) in patients with head and neck squamous cell carcinoma (HNSCC) and construct a predictive model. MethodsThe Cancer Genome Atlas was used to obtain clinical data for 112 patients with HNSCC. Expression of p16, p53, and cyclin D1 was quantified. We used the survival package of the R program to set the cut-off value. Values above the cut-off were considered positive, while values below the cut-off were negative. Kaplan–Meier analysis and univariate and multivariate Cox regression analyses were performed to investigate prognostic clinicopathological indicators and the expression of p16, p53, and cyclin D1. A predictive model was constructed based on the results of multifactor Cox regression analysis, and the accuracy of the predictive model was verified through final calibration analysis. Follow-up of patients with HNSCC at the Affiliated Hospital of Binzhou Medical University was conducted from 2015 to 2017, and reliability of the predictive model was validated based on follow-up data and molecular expression levels. ResultsAccording to the results, expression of p16 and p53 was significantly associated with prognosis (P < .05). The predictive model constructed based on the expression levels of p16 and p53 was useful for evaluating the prognosis of patients with HNSCC. The predictive model was validated using follow-up data obtained from the hospital, and the trend of the follow-up results was consistent with the predictive model. Conclusionp16 and p53 can be used as key indicators to predict the prognosis of HNSCC patients and as critical immunohistochemical indicators in clinical practice. The survival model constructed based on p16 and p53 expression levels reliably predicts patient prognosis.