Survival Prognosis Research Articles

MiR-155-5p regulates autophagy and apoptosis of glioma cells through RICTOR.

Glioma characterized by the high degree of drug resistance and the poor prognosis is the most common primary malignant tumors of the brain. And miRNA is involved in a variety of biological behaviors of tumors, enhancing or inhibiting the occurrence and development of tumors. Therefore, the present study aims to explore whether miR-155-5p can regulate autophagy and apoptosis of glioma through RICTOR. The significantly differential gene miR-155-5p was identified from the Gene Expression Omnibus (GEO; http://www.ncbi.nlm.nih.gov/geo) databases GSE165937 and GSE138764 using bioinformatics analysis, and its expression was validated by quantitative real-time polymerase chain reaction (qRT-PCR). The putative target genes of miR-155-5p were predicted through interrogation of relevant databases, followed by identification of key target genes. Subsequently, core target genes were selected for functional enrichment analysis. The U87MG cell line was utilized as the experimental model and divided into Negative Control1 (NC1) group, Mimic group, Negative Control2 (NC2) group, Inhibitor group, and NC + 3-methyladenine (3-MA) group. The expression levels of miR-155-5p, RICTOR, P62, LC-3, Bax, Bcl-2, and Caspase-3 were assessed using qRT-PCR, cellular fluorescence imaging, and Western blotting; while apoptosis in the U87MG cell line was evaluated via flow cytometry. The results showed that miR-155-5P was highly expressed in glioma cells, which could inhibit the expression of Bax, Caspase-3, LCII/LCI and Beclin-1, and increase the expression of Bcl2 and P62. Flow cytometry and cell fluorescence were used to verify the above results. Moreover, when U87MG cells treated with miR-155-5p inhibitor were inhibited by 3-MA, the results showed that miR-155-5p enhanced the anti-apoptotic ability of U87MG cells by regulating autophagy. In addition, the bioinformatics results show that miR-155-5p survival prognosis in glioma into a strong negative correlation, while the survival prognosis of RICTOR in glioma showed a strong positive correlation. The core target genes Kyoto Encyclopedia of Genes and Genomes (KEGG) mainly occurred in PI3K-AKT signaling pathway; in addition, qRT-PCR and Western blot confirmed the regulatory effect of miR-155-5P on RICTOR. MiR-155-5p regulates autophagy and apoptosis-related proteins in glioma cells through RICTOR, affecting the occurrence and development of glioma.

Risk signature of NETosis-related subtype predicts prognosis and evaluates immunotherapy effectiveness in gastric cancer.

Gastric cancer (GC) has a high incidence and mortality rate with a poor prognosis, so it is crucial to search for new biomarkers. The role of NETosis, a newly identified type of programmed cell death, in GC and its underlying mechanisms have yet to be explored and still require thorough investigation. Our research seeks to enhance our comprehension of NETosis and may offer novel approaches for treating GC. Utilizing The Cancer Genome Atlas-stomach adenocarcinoma (TCGA-STAD) dataset for training and the GSE84433 dataset for validation, our study delved into the associations between NETosis-related genes and the clinical risk of GC. Through comprehensive clustering, enrichment, and immune infiltration analyses, we evaluated the prognostic relevance of these NETosis genes in vivo. Furthermore, we devised a NETosis-related risk signature (NRRS) to assess its implications in risk stratification, survival prognosis, immune infiltration, and drug sensitivity. The NRRS's accuracy was validated by immunohistochemical staining. By applying consensus clustering to data from 62 NETosis-related genes, we categorized patients into two distinct subgroups, C1 and C2. These subgroups demonstrated significant differences. Following this, we developed the NRRS using the least absolute shrinkage and selection operator (LASSO) regression analysis. This process involved the selection of the following genes: CXCR4, NRP1, PDCD1, CTLA4, AKR1B1, SERPINE1, RGS2, SLCO2A1, TNFAIP2, RNASE1, DOC2B, APOD, ENTPD2, and CCL24. High-risk and low-risk groups can be accurately distinguished. We further verify in the verification set. These results suggest that NETosis is related to the microenvironment of GC. Our designed NRRS can predict the survival of patients with GC. We emphasized the relationship between NETosis and GC. We built and validated the value of NRRS. This contributes to deepening our view of NETosis and potentially provides new strategies for GC treatment.

Shared Decision-Making Communication and Prognostic Misunderstanding in the ICU

Surrogate misunderstanding of patient survival prognosis in the intensive care unit (ICU) is associated with poor patient and surrogate outcomes. Shared decision-making (SDM) may reduce misunderstanding. To evaluate the association between SDM-aligned communication and prognostic misunderstanding. This retrospective cohort study was conducted at 13 medical and surgical ICUs at 5 hospitals in North Carolina, Pennsylvania, and Washington between December 2012 and January 2017. Participants were surrogates of adult patients receiving prolonged mechanical ventilation and ICU physicians. Analysis was performed May to November 2023. SDM-aligned communication during ICU family meetings, defined as the presence of high-quality serious illness communication behaviors aligned with SDM principles. The primary outcome was postmeeting surrogate prognostic misunderstanding, defined as the absolute difference between the physician's estimate of survival prognosis and the surrogate's perception of that estimate (range, 0-100 percentage points). The secondary outcome was postmeeting physician misunderstanding, defined as the absolute difference between a surrogate's estimate of survival prognosis and the physician's perception of that estimate (range, 0-100 percentage points). Prognostic misunderstanding of 20 percentage points or greater was considered clinically significant as in prior work. Of 137 surrogates, most were female (102 [74.5%]), and there were 22 (16.1%) Black surrogates, 107 (78.1%) White surrogates, and 8 surrogates (5.8%) with other race and ethnicity. Of 100 physicians, most were male (64 [64.0%]), with 11 (11.0%) Asian physicians, 4 (4.0%) Black physicians, and 75 (75.0%) White physicians. Median (IQR) surrogate prognostic misunderstanding declined significantly after family meetings (before: 22.0 [10.0 to 40.0] percentage points; after: 15.0 [5.0 to 34.0] percentage points; P = .002), but there was no significant change in median (IQR) physician prognostic misunderstanding (before: 12.0 [5.0 to 30.0] percentage points; after: 15.0 [5.0 to 29.0] percentage points; P = .99). In adjusted analyses, SDM-aligned communication was not associated with prognostic misunderstanding among surrogates or physicians (surrogates: β = -0.74; 95% CI, -1.81 to 0.32; P = .17; physicians: β = -0.51; 95% CI, -1.63 to 0.62; P = .38). In a prespecified subgroup analysis of 78 surrogates (56.9%) with clinically significant premeeting prognostic misunderstanding, SDM-aligned communication was associated with reduced surrogate postmeeting prognostic misunderstanding (β = -1.71; 95% CI, -3.09 to -0.34; P = .01). In this retrospective cohort study, SDM-aligned communication was not associated with changes in prognostic misunderstanding for all surrogates or physicians, but it was associated with reduced prognostic misunderstanding among surrogates with clinically significant misunderstanding at baseline.

Machine learning applications in breast cancer survival and therapeutic outcome prediction based on multi-omic analysis.

The high heterogeneity within and between breast cancer patients complicates treatment determination and prognosis assessment. Treatment decision-making is influenced by various factors, such as tumor subtype, histological grade, and genotype, necessitating personalized treatment strategies. Prognostic outcomes vary significantly depending on patient-specific conditions. As a critical branch of artificial intelligence, machine learning efficiently handles large datasets and automates decision-making processes. The introduction of machine learning offers new solutions for breast cancer treatment selection and prognosis assessment. In the field of cancer therapy, traditional methods for predicting treatment and survival outcomes often rely on single or few biomarkers, limiting their ability to capture the complexity of biological processes comprehensively. Machine learning analyzes patients' multi-omic data and the intricate patterns of variations during cancer initiation and progression to predict patients' survival and treatment outcomes. Consequently, it facilitates the selection of appropriate therapeutic interventions to implement early intervention and improve treatment efficacy for patients. Here, we first introduce common machine learning methods, and then elaborate on the application of machine learning in the field of survival prediction and prognosis from two aspects: evaluating survival and predicting treatment outcomes for breast cancer patients. The aim is to provide breast cancer patients with precise treatment strategies to improve therapeutic outcomes and quality of life.

The prognosis of patients with locally advanced cervical cancer undergoing surgical versus non-surgical treatment: A retrospective cohort study based on SEER database and a single-center data.

The aim of this study was to investigate the impact of surgical treatment on the survival prognosis of patients with locally advanced cervical cancer (LACC) and to identify factors that may influence the efficacy of surgery. Data from the SEER database (2000-2020) and a hospital (2013-2023) were collected for this investigation. Utilizing multivariable Cox regression analysis, Kaplan-Meier survival analysis, and log-rank tests, we assessed the effects of surgical intervention on overall survival (OS) and disease-specific survival (DSS) in LACC patients. Our results revealed that in the SEER database, the surgical group exhibited significantly better OS and DSS compared to the non-surgical group. Particularly noteworthy was the significantly higher survival rate in the surgical group for patients with tumor diameters less than 6 cm. Furthermore, both OS and DSS were improved in the surgical group regardless of whether the cancer was squamous cell carcinoma or adenocarcinoma. Additionally, patients who underwent surgery combined with radiotherapy had notably better OS and DSS compared to those who received chemoradiotherapy alone. Similarly, our hospital data showed that the surgical group demonstrated significantly better OS than the non-surgical group, especially for patients with tumors smaller than 6 cm in diameter. These findings suggest that surgery combined with radiotherapy may offer more favorable outcomes than chemoradiotherapy alone, particularly for LACC patients with smaller tumors.

Role of Surgery in Potentially Resectable Small-Cell Lung Cancer Based on the Eighth Edition of the TNM Classification: A Population Study of the US SEER Database.

This study aimed to identify a specific SCLC population that would benefit from surgery. This study utilized patient data retrieved from the Surveillance, Epidemiology, and End Results (SEER) database spanning 2010 to 2017. To mitigate clinical biases, the propensity score matching (PSM) technique was employed. Separate cohorts were aligned using PSM according to the AJCC 8th edition TNM classification. The Kaplan-Meier method and a competing risk model were applied to evaluate overall survival (OS) and lung cancer-specific survival (LCSS), respectively. Among the 3394 patients with potentially resectable SCLC included in the study, 3062 underwent chemoradiotherapy and 332 underwent surgical treatment with adjuvant chemotherapy. Surgery was associated with better OS (median OS: 49 months; 95% CI: 35-63 months vs. 27 months; 95% CI: 21-33 months, p < 0.001) and LCSS (SHR, 0.578; 95% CI: 0.411-0.815, p < 0.001) in stage I patients after PSM. However, there was no significant difference in OS and LCSS between the surgery and nonsurgery groups in stage II and III patients after PSM. In the entire cohort, lobectomy was associated with improved OS (median OS: 48.6 vs. 28.7 months, p < 0.0001), but not LCSS (SHR, 0.696; 95% CI: 0.466-1.040, p = 0.078) compared with sublobar resection after PSM. Surgery with adjuvant chemotherapy significantly improved the survival prognosis of patients with early-stage SCLC. However, surgical treatment should be carefully considered in patients with stage II/III disease. Lobectomy is oncologically equal to sublobar resection.

Breast cancer promotes the expression of neurotransmitter receptor related gene groups and image simulation of prognosis model

Breast cancer (BC), a prevalent and severe malignancy, detrimentally affects women globally. Its prognostic implications are profoundly influenced by gene expression patterns. This study retrieved 509 BC-associated oncogenes and 1,012 neurotransmitter receptor-related genes from the GSEA and KEGG databases, intersecting to identify 98 relevant genes. Clinical and transcriptomic expression data related to BC were downloaded from the TCGA, and differential genes were identified based on an FDR value <0.05 & |log2FC| ≥ 0.585. Univariate analysis of these genes revealed that high expression of NSF and low expression of HRAS, KIF17, and RPS6KA1 are closely associated with BC survival prognosis. A prognostic model constructed for these four genes demonstrated significant prognostic relevance for BC-TCGA patients (P < 0.001). Subsequently, an immunofunctional analysis of the BC oncogene-neurotransmitter receptor-related gene cluster revealed the involvement of immune cells such as T cells CD8, T cells CD4 memory resting, and Macrophages M2. Further analysis indicated that immune functions were primarily concentrated in APC_co_inhibition, APC_co_stimulation, CCR, and Check-point, among others. Lastly, a prognostic nomogram model was established, and ROC curve analysis revealed that the nomogram is a vital indicator for assessing BC prognosis, with 1-year, 3-year, and 5-year survival rates of 0.981, 0.897, and 0.802, respectively. This model demonstrates high calibration, clinical utility, and predictive capability, promising to offer an effective preliminary tool for clinical diagnostics.

Intratumoral T cells are associated with prognosis and chemotherapy benefit in gastric cancer.

Tumor-infiltrating lymphocytes (TILs) have been described in various malignancies and viewed as a sign of anti-tumor immunity, so they are frequently thought to be implicated in the prognosis of cancers. However, little information is available on the association of the distribution pattern of TILs with clinical outcomes in gastric cancer (GC). TIL densities at different regions were assessed immunohistochemically in 59 GC patients to analyze their relationship with clinicopathological characteristics. We found that GC patients in the high-density TIL group were significantly associated with reduced tumor invasion depth, absence of lymph node metastasis, earlier TNM stage, and improved progression-free survival (PFS). Both intratumoral CD3+ TILs and pathological T stage were identified as having an independent prognostic value. Additionally, GC patients with a high density of intratumoral CD3+ TILs were found to gain more benefit from chemotherapy. Overall, these results underscored the predictive power of intratumoral TILs in survival prognosis and chemotherapy benefit for GC.

Peripheral NK cell count predicts response and prognosis in breast cancer patients underwent neoadjuvant chemotherapy.

The count of lymphocyte subsets in blood can reflect the immune status of the body which is closely related to the tumor immune microenvironment and the efficacy of NAT. This study aims to explore the relationship between peripheral blood lymphocyte subsets and the efficacy and prognosis of NAT in breast cancer. We retrospectively analyzed clinicopathological information and peripheral blood lymphocyte subpopulation counts of patients receiving NAT from January 2015 to November 2021 at Sun Yat-sen University Cancer Center. Kaplan-Meier curves were used to estimate the survival probability. The independent predictors of NAT response and survival prognosis were respectively analyzed by multivariate logistic regression and Cox regression, and nomograms were constructed accordingly. The prediction efficiency of three nomograms was validated separately in the training cohort and the testing cohort. 230 patients were included in the study, consisting of 161 in the training cohort and 69 in the testing cohort. After a median follow-up of 1238 days, patients with higher NK cell value showed higher pCR rates and higher OS and RFS after NAT (all P < 0.001). Multivariate analyses suggested NK cell count was an independent predictor of NAT response, OS and RFS. We then built nomograms accordingly and validated the prediction performance in the testing cohort (C index for NAT response: 0.786; for OS: 0.877, for RFS: 0.794). Peripheral blood NK cell count is a potential predictive marker for BC patients receiving NAT. Nomograms based on it might help predict NAT response and prognosis in BC.

Molecular subtypes and prognostic factors of lung large cell neuroendocrine carcinoma.

Lung large cell neuroendocrine carcinoma (LCNEC) is an aggressive disease with poor prognosis and short-term survival, which lacks effective prognostic indicators. The study aims to investigate the molecular subtypes and prognostic markers of lung LCNEC. Patients diagnosed with lung LCNEC at Sun Yat-sen University Cancer Center (SYSUCC) between November 2007 and January 2021 were screened. Baseline clinical data were collected and routine blood indexes including lymphocyte-to-monocyte ratio (LMR), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) and systemic immune-inflammation index (SII) were calculated. Immunohistochemistry (IHC) of ASCL1, NEUROD1, POU2F3, YAP1 were done to perform molecular subtyping, while CD56, Syn, CgA, CD3, CD8, CD20, CD68, and CD163 were also stained on tissue samples. Then prognostic factors of lung LCNEC were explored. One hundred and fifty-one lung LCNEC patients were identified, 103 of whom had complete clinical information, available routine blood and biochemical indexes were eventually included in the present study. Tumor tissue specimens were available from 64 patients. Positive expression rates of ASCL1, NEUROD1, and YAP1 were 82.8%, 50.0%, and 28.1%, respectively. No POU2F3+ cases were detected. Forty (62.5%) patients co-expressed with two or three markers. High LMR (>3.3) was an independent predictor of favorable prognosis of disease-free survival (DFS) [hazard ratio (HR), 0.391; 95% confidence interval (CI): 0.161-0.948; P=0.04] and overall survival (OS) (HR, 0.201; 95% CI: 0.071-0.574; P=0.003). Notably, high LMR was correlated with higher intra-tumoral CD3+ (P=0.004), CD8+ (P=0.01), and CD68+ (P<0.001) immune cell infiltration compared to low LMR in lung LCNEC. Our study validated molecular subtypes by IHC in lung LCNEC, and co-expression was found among different subtypes, with no prognostic effect. High blood LMR level was associated with a favorable prognosis in lung LCNEC, which might partly reflect a hot tumor tissue immune microenvironment. Our findings may benefit clinical practice, and further studies are warranted.

ZYG11B participates in the modulation of colorectal cancer cell proliferation and immune infiltration and is a prognostic biomarker.

The biological function of ZYG11B is still unclear, and few studies on ZYG11B in colorectal cancer were reported. The purpose of our research is to detect the biological functions of ZYG11B in colorectal cancer through The Cancer Genome Atlas (TCGA) database online and the vitro cell experiments. The information of ZYG11B in colorectal cancer were downloaded from TCGA database. The bioinformatics analysis has been utilized to examine the expression, functional enrichment, association of immune, clinicopathological characteristics and diagnostic prognostic value. CCK-8 and apoptosis assays have been utilized to identify the abilities of progression and apoptosis. The abilities of invasion and migration were detected by transwell assay. In contrast to adjacent normal tissues, colorectal cancer tissues exhibited a notably diminished expression of ZYG11B (p < 0.001). Further examination through functional enrichment analysis unveiled the enrichment of various pathways associated with tumor proliferation and apoptosis. The implementation of CCK-8 and apoptosis assays validated the suppressive impact of ZYG11B on the progression of colorectal cancer cells (p < 0.001). A significant positive correlation was found between ZYG11B and Tcm and T helper cells (R ≥ 0.3, p < 0.001). Moreover, the expression of ZYG11B demonstrated a prominent presence among subjects without previous experience of colon polyps (p < 0.05), devoid of lymphatic infiltration (p < 0.01), and age ≤ 65 years (p < 0.01). Additionally, ZYG11B exhibited higher expression levels among patients diagnosed with colorectal adenocarcinoma (p < 0.05). Following the analysis of survival prognosis, it became evident that increased ZYG11B expression correlated with enhanced survival rates (p < 0.01) and the ability to accurately forecast the prognosis and survival of COAD/READ patients. ZYG11B plays a tumor suppressive role in the proliferation process of colorectal cancer and may have a broad application prospect in the diagnosis and prognosis evaluation of colorectal cancer with more study.

A case of masquerade syndrome caused by metastatic iris tumor diagnosed by a high CEA level in the aqueous humor and iris biopsy

BackgroundWith the advent of targeted therapies, the survival prognosis for metastatic tumors has extended, and it has become necessary to diagnose and consider treatment that takes into account Quality of Life for metastatic tumors of the eye. The reports of checking tumor marker in the aqueous humor for diagnosis of metastatic intraocular tumors are few. Here, we report a case of masquerade syndrome with secondary glaucoma in which a high carcinoembryonic antigen (CEA) level in the aqueous humor could assist diagnosis, and continuing targeted therapy and trabeculectomy were effective.Case presentationA 73-year-old man was referred to us for iritis and high intraocular pressure (IOP) with severe eye pain in the left eye. He had Stage IVB lung adenocarcinoma treated with a molecularly targeted drug, Osimertinib. His best corrected visual acuity was 0.15, and IOP was 52 mmHg in the left eye. Anterior chamber cells (+), numerous small nodules in the iris, and small masses in the inferior angle were observed. In the aqueous humor, the CEA level was higher than in the blood. Napsin A and Thyroid Transcription Factor-1 (TTF-1) positive cells showed in the resected tissue at iridectomy performed during trabeculectomy. The pathological diagnosis of metastatic iris tumor of the lung adenocarcinoma was made, and we injected bevacizumab intravitreally once and continued Osimertinib. His IOP lowered to 8–10 mmHg, and the iris masses disappeared. He lost vision by metastasis to the left optic nerve after termination of Osimertinib one and a half years later. The metastasis shrank after restarting the drug. He passed away from an exacerbation of his primary lung cancer two years and nine months after the first visit. Although he lost vision in his left eye, the metastatic tumor in his left eye and optic nerve had disappeared, and his quality of life was maintained without any pain in his eye.ConclusionsChecking tumor markers in the aqueous humor can aid in diagnosis, and aggressive treatment of metastatic iris tumors must help maintain patients’ Quality of Life.

ERP44 could serve as a bridge mediating prognosis and immunity for glioma via single-cell and bulk RNA-sequencing

BackgroundThere was evidence that ERP44 played vital roles in a variety of cancers. However, currently, ERP44 was rarely mentioned in gliomas. Therefore, we firstly integrated proteomics, bulk, as well as single-cell RNA-sequencing (scRNA-seq) to study the possible functions of ERP44 in glioma patients. MethodsFrom online databases, we obtained bulk RNA-seq, scRNA-seq, and proteomic data of ERP44 in gliomas and verified the expression of ERP44 by qRT-PCR. Then, the Noman diagram, gene set enrichment analysis (GSEA), and univariate/multivariate Cox regression analysis were all carried out in turn. Further discussions were also conducted regarding tumor immunity and ERP44 expression. ResultsERP44 in glioma tissues was found to be considerably higher than that in normal tissues (P<0.05) in the TCGA dataset, as well as the verification of GSE50161, GSE4290, and qRT-PCR results. High ERP44 expression indicated poorer overall survival (OS) for glioma (P<0.05), and it might also be used to predict gliomas’ OS independently (P<0.05). In order to estimate these patients’ survival prognosis, a Noman chart was created with effectiveness. According to GSEA analysis, ERP44 might be implicated in five significant pathways in gliomas. The levels of immune cell infiltration of LGG, the tumor immune microenvironments, the immunological checkpoints of LGG, and GBM were all strongly linked with ERP44 in terms of tumor immunity (P<0.05). Further scRNA-seq analysis revealed that ERP44 could be expressed in various cell types, including T cells, Mono/Macrophages, and malignant cells. ConclusionsERP44 was an oncogenic gene in gliomas, serving as a bridge mediating prognosis and immunity.

Exploring the impact of HDL and LMNA gene expression on immunotherapy outcomes in NSCLC: a comprehensive analysis using clinical & gene data.

Analyzing the impact of peripheral lipid levels on the efficacy of immune checkpoint inhibitor therapy in non-small cell lung cancer (NSCLC) patient populations and exploring whether it can serve as a biomarker for broadening precise selection of individuals benefiting from immunotherapy. We retrospectively collected clinical data from 201 cases of NSCLC patients receiving immune checkpoint inhibitor therapy. The clinical information included biochemical indicators like total cholesterol, triglycerides, high-density lipoprotein (HDL), and low-density lipoprotein (LDL). We utilized machine learning algorithms and Cox proportional hazards regression models to investigate independent predictors for both short-term and long-term efficacy of immunotherapy. Additionally, we concurrently developed a survival prediction model. Analyzing the Genes of Patients with Treatment Differences to Uncover Mechanisms. Correlation analysis revealed a significant positive association between HDL and ORR, DCR, and PFS. T-test results indicated that the high-HDL group exhibited higher DCR (81.97% vs. 45.57%) and ORR (61.48% vs. 16.46%). Kruskal-Wallis test showed that the high-HDL group had a longer median PFS (11 months vs. 6 months). Utilizing six machine learning algorithms, we constructed models to predict disease relief and stability. The model built using the random forest algorithm demonstrated superior performance, with AUC values of 0.858 and 0.802. Furthermore, both univariate and multivariate Cox analyses identified HDL and LDL as independent risk factors for predicting PFS. In patients with poor immunotherapy response, there is upregulation of BCL2L11, AKT1, and LMNA expression. HDL and LDL are independent factors influencing the survival prognosis of NSCLC patients undergoing immune checkpoint inhibitor therapy. HDL is expected to become new biomarkers for predicting the immunotherapy efficacy in patients with NSCLC. In patients with poor immunotherapy response, upregulation of the LMNA gene leads to apoptosis resistance and abnormal lipid metabolism.

Clinical significance of RETN gene expression and rs3219175 G > a polymorphism in cancer

The inflammatory cytokine resistin, which is encoded by the RETN gene, plays a variety of roles in cancer. This study aimed to assess the relationship between RETN gene expression and cancer stage, survival prognosis, immune infiltration, and drug sensitivity, and whether the rs3219175 G > A polymorphism affected the expression of the RETN gene and cancer risk. The clinical significance of RETN gene expression and the rs3219175 polymorphism in cancer was analyzed by the GSCA platform, GTEx database and STATA software. The results showed that RETN gene expression was associated with the stage of thyroid carcinoma, survival prognosis and immune infiltration of certain cancers, and sensitivity to multiple drugs. The rs3219175 polymorphism could influence the expression of the RETN gene in a wide range of tissues. Furthermore, RETN gene rs3219175 polymorphism was significantly associated with cancer risk [GA vs. GG: OR = 2.27, 95%CI = 1.26–4.09; (GA + AA) vs. GG: OR = 2.23, 95%CI = 1.28–3.88; A vs. G: OR = 1.72, 95%CI = 1.15–2.58]. In conclusion, the current study suggested that resistin might serve as a prognostic marker and therapeutic target for certain cancers, and the rs3219175 polymorphism might be used as a marker for predicting cancer risk.

Impact of sarcopenia on the prognosis of patients with advanced non-small cell lung cancer treated with antiangiogenic therapy: A propensity score matching analysis.

Limited information is available regarding the impact of sarcopenia on the prognosis of antiangiogenic therapy in individuals with advanced non-small cell lung cancer (NSCLC). This study primarily sought to examine the prognostic significance of sarcopenia in individuals with advanced NSCLC undergoing antiangiogenic therapy. We retrospectively enrolled all patients who met the inclusion and exclusion criteria from 2019 to 2021 at Nantong University Hospital. Patients were grouped according to the presence or absence of sarcopenia. After propensity score matching (PSM), progression-free survival (PFS), overall survival (OS), and adverse event rates were compared between the two groups. Factors associated with prognosis were screened using univariate and multivariate analyses. A total of 267 patients were included, with a total of 201 matched at baseline after PSM (77 in the sarcopenia group and 124 in the non-sarcopenia group). The sarcopenia group had lower PFS (p = 0.043) and OS (p = 0.011) than the non-sarcopenia group and a higher incidence of adverse events (p = 0.044). Multivariate analysis suggested that sarcopenia is an independent prognostic risk factor for OS in advanced NSCLC patients receiving antiangiogenic therapies (p = 0.009). Results of subgroup analyses showed some differences in the impact of sarcopenia on survival prognosis in populations with different characteristics. Patients with advanced NSCLC with comorbid sarcopenia exhibit a worse prognosis when treated with antiangiogenic therapy, and preventing and ameliorating sarcopenia may lead to better survival outcomes in patients with advanced NSCLC.

The prognostic value of bioinformatics analysis of ECM receptor signaling pathways and LAMB1 identification as a promising prognostic biomarker of lung adenocarcinoma.

The extracellular matrix (ECM) is a complex and dynamic network of cross-linked proteins and a fundamental building block in multicellular organisms. Our study investigates the impact of genes related to the ECM receptor interaction pathway on immune-targeted therapy and lung adenocarcinoma (LUAD) prognosis. This study obtained LUAD chip data (GSE68465, GSE31210, and GSE116959) from NCBI GEO. Moreover, the gene data associated with the ECM receptor interaction pathway was downloaded from the Molecular Signature Database. Differentially expressed genes were identified using GEO2R, followed by analyzing their correlation with immune cell infiltration. Univariate Cox regression analysis screened out ECM-related genes significantly related to the survival prognosis of LUAD patients. Additionally, Lasso regression and multivariate Cox regression analysis helped construct a prognostic model. Patients were stratified by risk score and survival analyses. The prognostic models were evaluated using receiver operating characteristic curves, and risk scores and prognosis associations were analyzed using univariate and multivariate Cox regression analyses. A core gene was selected for gene set enrichment analysis and CIBERSORT analysis to determine its function and tumor-infiltrating immune cell proportion, respectively. The results revealed that the most abundant pathways among differentially expressed genes in LUAD primarily involved the cell cycle, ECM receptor interaction, protein digestion and absorption, p53 signaling pathway, complement and coagulation cascade, and tyrosine metabolism. Two ECM-associated subtypes were identified by consensus clustering. Besides, an ECM-related prognostic model was validated to predict LUAD survival, and it was associated with the tumor immune microenvironment. Additional cross-analysis screened laminin subunit beta 1 (LAMB1) for further research. The survival time of LUAD patients with elevated LAMB1 expression was longer than those with low LAMB1 expression. Gene set enrichment analysis and CIBERSORT analyses revealed that LAMB1 expression correlated with tumor immune microenvironment. In conclusion, a prognostic model of LUAD patients depending on the ECM receptor interaction pathway was constructed. Screening out LAMB1 can become a prognostic risk factor for LUAD patients or a potential target during LUAD treatment.

Identification of prognostic biomarkers in neuroblastoma using WGCNA and multi-omics analysis

BackgroundNeuroblastoma (NB) is one of the most frequent parenchymal tumors among children, with a high degree of heterogeneity and wide variation in clinical presentation. Despite significant therapeutic advances in recent years, long-term survival in high-risk patients remains low, emphasizing the urgent need to find new biomarkers and construct reliable prognostic models.MethodsIn this study, data from neuroblastoma samples in the ArrayExpress database were utilized to identify key gene modules and pivotal genes associated with NB prognosis by weighted gene co-expression network analysis (WGCNA). The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis was performed using the DAVID database. Based on these hub genes, survival prognosis models were constructed and validated on an independent validation set in the Gene Expression Omnibus (GEO) database. Differences in biological functions and immune microenvironments and the sensitivity to pharmacological and immunotherapeutic treatments of patients in the high- and low-risk groups were examined by gene set enrichment analysis (GSEA) and immune infiltration analysis.ResultsWGCNA identified 14 gene modules and screened the module with the highest relevance to the International Neuroblastoma Staging System (INSS), containing 60 pivotal genes. GO and KEGG analyses demonstrated that these pivotal genes were mainly implicated in biological processes and signaling pathways including DNA replication, cell division, mitotic cell cycle, and cell cycle. Based on Lasso regression and COX regression analysis, a prognostic model containing DHFR, GMPS and E2F3 was constructed, and the RiskScore was significantly correlated with the 1-, 3- and 5-year survival of the patients. GSEA and immune infiltration analyses revealed significant differences in the levels of cell cycle-related pathways and immune cell infiltration between the high and low RiskScore groups. In particular, patients in the high-risk group are less likely to benefit from immunotherapy and may be better suited for treatment with drugs such as Oxaliplatin and Alpelisib.ConclusionThis research systematically identified biomarkers related to NB prognosis and developed a reliable prognostic model applying WGCNA and multiple bioinformatics methods. The model has important application value in predicting patients’ prognosis, evaluating drug sensitivity and immunotherapy effect, and provides new ideas and directions for precise treatment of neuroblastoma.

Anoikis-related genes in breast cancer patients: reliable biomarker of prognosis

BackgroundBreast cancer (BC) is the most common cancer in women, and its progression is closely related to the phenomenon of anoikis. Anoikis, the specific programmed death resulting from a lack of contact between cells and the extracellular matrix, has recently been recognized as playing a critical role in tumor initiation, maintenance, and treatment. The ability of cancer cells to resist anoikis leads to cancer progression and metastatic colonization. However, the impact of anoikis on the prognosis of BC patients remains unclear.MethodThis study utilized data from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to collect transcriptome and clinical data of BC patients. Anoikis-related genes (ARGs) were classified into subtypes A and B through consensus clustering. Subsequently, survival prognosis analysis, immune cell infiltration analysis, and functional enrichment analysis were performed for both subtypes. Using the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, a set of 10 ARGs related to prognosis was identified. Immune cell infiltration and tumor microenvironment analyses were conducted on these 10 ARGs to develop a prognostic model. Furthermore, single-cell data analysis and real-time polymerase chain reaction (RT-PCR) analysis were employed to study the expression of the 10 identified prognostic ARGs in BC cells.ResultsOne hundred thirty-five ARGs were identified as differentially expressed genes in the TCGA and GEO databases, with 42 of them associated with the survival prognosis of BC patients. Analyses involving Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP) revealed distinct expression patterns of ARGs between types A and B. Patients in type A exhibited worse survival prognosis and lower immune cell infiltration compared to type B. Subsequent analyses identified 10 key ARGs (YAP1, PIK3R1, BAK1, PHLDA2, EDA2R, LAMB3, CD24, SLC2A1, CDC25C, and SLC39A6) relevant to BC prognosis. Kaplan–Meier analysis indicated that high-risk patients based on these ARGs had a poorer BC prognosis. Additionally, Cox regression analysis established gender, age, T (tumor), N (nodes), and risk score as predictive factors in a nomogram model for BC. The model demonstrated diagnostic value for BC patients at 1, 3, and 5 years. Decision curve analysis (DCA) verified the risk score as a reliable predictor of BC patient survival rates. Moreover, RT-PCR results confirmed differential expressions of YAP1, PIK3R1, BAK1, PHLDA2, CD24, SLC2A1, and CDC25C in BC cells, with SLC39A6, EDA2R, and LAMB3 showing low expression levels.ConclusionARGs markers can be used as BC biomarkers for risk stratification and survival prediction in BC patients. Besides, ARGs can be used as stratification factors for individualized and precise treatment of BC patients.

Predictive Value of the Naples Prognostic Score for Cardiovascular Outcomes in Patients With Chronic Kidney Disease Receiving Percutaneous Coronary Intervention

The Naples prognostic score (NPS) is a novel multidimensional inflammatory and nutritional assessment system in cancer patients. However, its significance in patients with chronic kidney disease (CKD) after percutaneous coronary intervention (PCI) remains unclear. The study has a single-center, retrospective design and included 631 patients with CKD who underwent index PCI between 2019 and 2022. All participants were divided into 2 groups according to the NPS (Low-risk group: n = 209; High-risk group: n = 422) and followed up until November 2022. The primary endpoint was Major Adverse Cardiac Events (MACE). NPS predicted MACE events better than other scores, besides, high-risk NPS with severe renal dysfunction (RD) group (MODEL 2) had superior MACE diagnostic efficiency than NPS high-risk group lonely. (NPS: AUC: 0.605, P &lt; .001; MODEL 2: AUC: 0.624, P &lt; .001, respectively). Kaplan–Meier survival analysis of two groups showed that high-risk group had higher incidence of MACE ( P &lt; .001). Meanwhile, high-risk group had higher MACE events [adjusted Hazard Ratio (aHR) 2.013, 95% CI 1.294, 3.132; P = .002]. NPS is an independent prognostic factor for CKD patients undergoing index PCI before operation whose predictive value for survival prognosis is better than other nutritional and inflammatory indicators. Compared with low NPS, patients with high NPS have a relatively poor prognosis.