Univariate Cox Research Articles

Predictive factors and prognosis of immune checkpoint inhibitor-related pneumonitis in non-small cell lung cancer patients.

To investigate the influencing factors and prognosis of immune checkpoint inhibitor-related pneumonitis (CIP) in advanced non-small cell lung cancer (NSCLC) patients during or after receiving immune checkpoint inhibitors(ICIs). The clinical and laboratory indicator data of 222 advanced NSCLC patients treated with PD-1/PD-L1 inhibitors at the First Affiliated Hospital of Zhengzhou University between December 2017 and November 2021 were collected retrospectively. The patients were divided into a CIP group (n=41) and a non-CIP group (n=181) according to whether they developed CIP or not before the end of follow-up. Logistic regression was used to evaluate risk factors of CIP, and Kaplan‒Meier curves were used to describe the overall survival (OS) of different groups. The log-rank test was used to compare the survival of different groups. There were 41 patients who developed CIP, and the incidence rate of CIP was 18.5%. Univariate and multivariate logistic regression analyses showed that low pretreatment hemoglobin (HB) and albumin (ALB) levels were independent risk factors for CIP. Univariate analysis suggested that history of chest radiotherapy was related to the incidence of CIP. The median OS of the CIP group and non-CIP were 15.63 months and 30.50 months (HR:2.167; 95%CI: 1.355-3.463, P<0.05), respectively. Univariate and multivariate COX analyses suggested that a high neutrophil-to-lymphocyte ratio (NLR) level, a low ALB level and the development of CIP were independent prognostic factors for worse OS of advanced NSCLC patients treated with ICIs. Additionally, the early-onset and high-grade CIP were related to shorter OS in the subgroup. Lower pretreatment HB and ALB levels were independent risk factors for CIP. A high NLR level, a low ALB level and the development of CIP were independent risk factors for the prognosis of advanced NSCLC patients treated with ICIs.

Identification of the cuproptosis-related gene signature associated with the tumor environment and prognosis of patients with glioblastoma multiforme (GBM) (S17.003)

<h3>Objective:</h3> In this study, we aimed to develop a cuproptosis gene-signature risk score using bioinformatics analysis and machine learning. <h3>Background:</h3> Glioblastoma multiforme (GBM) is the most common tumor of the central nervous system with poor prognosis. Cuproptosis is a novel programmed cell death pathway targeting lipoylated tricarboxylic acid cycle proteins. Previous studies have found that it participates in tumor progression, but its role in GBM is still elusive. <h3>Design/Methods:</h3> We acquired transcriptomic data and clinical information of GBM patients from The Cancer Genome Atlas (TCGA). A total of 2283 differentially expressed genes (DEGs) were obtained from the GEPIA2 database. 26 cuproptosis-related genes (CRGs) were retrieved from literature. A correlation analysis between the 26 CRGs and the DEGs were conducted to retrieve the cuproptosis-related DEGs. Then, a univariate cox analysis was conducted to obtain the prognostic-related DEGs for overall survival (OS). The least absolute shrinkage and selection operator (LASSO) were conducted for regularization and the gene risk score was constructed using the multivariate cox coefficients. <h3>Results:</h3> A total of 731 downregulated DEGs were correlated with CRGs, while 68 upregulated DEGs were correlated with CRGs and were further screened for prognostic value using the univariate cox analysis. A total of 70 prognostic related CRGs were identified and were further screened using the LASSO cox analysis. After multivariate cox analysis, a total of seven genes were significantly associated with survival (p-value&lt;0.01). A risk-score gene signature was constructed from the cox coefficients multiplied by the expression of the following genes: −0.0012*DPP10+0.0021*EGR4+0.0015*ITPKA+ 0.0003* PTPRN+ 0.0007* STEAP2+ 0.0006* TENM2+− 0.0017* ZNF540. <h3>Conclusions:</h3> Univariate and multivariate Cox regression analyses showed the CRGs-based prognostic signature independently functioned as a risk factor for OS in GBM patients. Furthermore, our results gave a promising understanding of cuproptosis in GBM, as well as a tailored prediction tool for prognosis and immunotherapeutic responses in patients. <b>Disclosure:</b> Ms. Al-Majali has nothing to disclose. Mr. Ahmed has nothing to disclose. Mr. Ababneh has nothing to disclose. Mr. Khalefa has nothing to disclose. Miss Alzghoul has nothing to disclose. Miss Al-Bzour has nothing to disclose.

Prognostic factors for non-small cell lung cancer patients with central nervous system metastasis with positive driver genes

Objective: To investigate the prognostic factors of patients with central nervous system (CNS) metastatic non-small cell lung cancer (NSCLC) with positive driver genes. Methods: The clinical data of 103 patients with CNS metastatic NSCLC admitted to Beijing Tongren Hospital from January 2016 to December 2020 were retrospectively analyzed, and the patients were divided into positive driver gene group (patients with driver genes mutation and receiving corresponding targeted therapy) and negative driver gene group. Cox univariate and multivariate regression analyses were performed to identify the factors affecting patients' prognosis, and the receiver operating characteristic curve (ROC) was used to compare the predictive ability of 4 scoring systems [recursive partitioning analysis (RPA) classes, diagnosis-specific graded prognostic assessment (DS-GPA) index, basic score for brain metastasesn (BS-BM) and (lung-molecular graded prognostic assessment (lung-mol GPA)]on patients' prognosis. Results: Among the 103 patients, 48 were males and 55 were females, and aged (64.6±9.7) years old. The median survival time of the 103 patients was 24.0 (95%CI: 20.0-28.0) months, the median survival time of the 59 patients in the positive driver gene group was 33.0 (95%CI: 23.4-42.6) months, the median survival time of the 44 patients in the negative driver gene group was 17.0 (95%CI: 14.4-19.6) months, and the difference was statistically significant (χ2=24.69, P<0.001). The results of Cox multivariate analysis showed that the negative driver genes (HR=3.788, 95%CI: 1.951-7.301, P=0.001), Karnofsky performance status (KPS) score<70 (HR=2.613, 95%CI: 1.185-5.761, P=0.017) and neutrophil-to-lymphocyte ratio (NLR)>3.22 (HR=2.714, 95%CI: 1.157-6.365, P=0.022) were independent risk factors affecting the prognosis of patients with CNS metastatic NSCLC. KPS score<70 (HR=3.719, 95%CI: 1.165-11.876, P=0.027) and no radiotherapy (HR=2.032, 95%CI: 1.033-11.364, P=0.041) were independent risk factors affecting the prognosis of patients with CNS metastatic NSCLC with positive driver genes. ROC curve analysis showed that the area under curve (AUC) value of lung-mol GPA was the highest among the 4 scoring systems (AUC=0.843, 95%CI: 0.731-0.956, P<0.001), and the AUC value of the lung-mol GPA combined scoring system (AUC=0.904, 95%CI: 0.816-0.991, P<0.001) was higher than lung-mol GPA. Conclusions: A low KPS score and no cranial radiation therapy are independent risk factors for the prognosis of patients with CNS metastatic NSCLC with positive driver genes; the lung-mol GPA joint scoring system is more conducive to the prognostic assessment of patients with CNS metastatic NSCLC with positive driver genes.

Interaction between age and gender on survival outcomes in extramedullary multiple myeloma over the past two decades

Extramedullary multiple myeloma (MM) (EMM) is a rare and aggressive subentity of MM that can be present at diagnosis or develop anytime during the disease course. There is a paucity of data on the clinical characteristics and overall epidemiology of EMM. Furthermore, there is a scarcity of data on how the interaction of age and gender influences the survival of EMM. To evaluate the clinical characteristics of patients with EMM over the past 2 decades and to identify epidemiologic characteristics that may impact overall prognosis. A total of 858 patients diagnosed with EMM, between 2000 and 2017, were ultimately enrolled in our study by retrieving the Surveillance, Epidemiology, and End Results database. We analyzed demographics, clinical characteristics, and overall mortality (OM) as well as cancer-specific mortality (CSM) of EMM. Variables with a P value < 0.1 in the univariate Cox regression were incorporated into the multivariate Cox model to determine the independent prognostic factors, with a hazard ratio (HR) of greater than 1 representing adverse prognostic factors. From a sample of 858 EMM, the male gender (63.25%), age range 60-79 years (51.05%), and non-Hispanic whites (66.78%) were the most represented. Central Nervous System and the vertebral column was the most affected site (33.10%). Crude analysis revealed higher OM in the age group 80+ [HR = 6.951, 95% confidence interval (95%CI): 3.299-14.647, P = 0], Non-Hispanic Black population (HR = 1.339, 95%CI: 1.02-1.759, P = 0.036), Bones not otherwise specified (NOS) (HR = 1.74, 95%CI: 1.043-2.902, P = 0.034), and widowed individuals (HR = 2.107, 95%CI: 1.511-2.938, P = 0). Skin involvement (HR = 0.241, 95%CI: 0.06-0.974, P = 0.046) and a yearly income of $75000+ (HR = 0.259, 95%CI: 0.125-0.538, P = 0) had the lowest OM in the crude analysis. Crude analysis revealed higher CSM in the age group 80+, Non-Hispanic Black, Bones NOS, and widowed. Multivariate cox proportional hazard regression analyses only revealed higher OM in the age group 80+ (HR = 9.792, 95%CI: 4.403-21.774, P = 0) and widowed individuals (HR = 1.609, 95%CI: 1.101-2.35, P = 0.014). Multivariate cox proportional hazard regression analyses of CSM also revealed higher mortality of the same groups. Eyes, mouth, and ENT involvement had the lowest CSM in the multivariate analysis. There was no interaction between age and gender in the adjusted analysis for OM and CSM. EMM is a rare entity. To our knowledge, there is a scarcity of data on the clinical characteristics and prognosis factors of patients with extramedullary multiple myeloma. In this retrospective cohort, using a United States-based population, we found that age, marital status, and tumor site were independent prognostic factors. Furthermore, we found that age and gender did not interact to influence the mortality of patients with EMM.

Neutrophil extracellular traps-mediated molecular subtypes characterize the hallmarks of tumor microenvironment and guide precision medicine in hepatocellular carcinoma

BackgroundNeutrophil extracellular traps (NETs) are released by neutrophils to capture invading pathogens. Recent studies show that NETs also play a significant role in tumorigenesis and cancer progression. However, the clinical significance of NET-related genes (NETRGs) in hepatocellular carcinoma (HCC) has not yet been fully elucidated. MethodsThe transcriptomic and clinical data of HCC patients were extracted from The Cancer Genome Atlas, the International Cancer Genome Consortium, and Gene Expression Omnibus databases. The prognostic NETRGs were screened based on Kaplan-Meier and univariate COX analyses, and different molecular clusters corresponding to NETs-related modification patterns were identified by unsupervised clustering analyses. A NETs-related prognostic signature (NETRPS) was then constructed using least absolute shrinkage and selection operator regression method, and the patients were accordingly classified into the low-risk and high-risk groups. The molecular clusters and risk groups were compared in terms of the tumor microenvironment characteristics, prognosis, therapeutic response and clinical characteristics through various bioinformatic methods. ResultsThree distinct NETs-related clusters were identified, which were termed A, B and C. Notably, cluster C demonstrated high enrichment of infiltrating immune cells and stromal-related pathways, and was associated with worst prognosis. Furthermore, the low-risk group was characterized by better prognosis, stronger metabolic activation, higher immune cell infiltration, and greater responsiveness to immunotherapy, targeted and interventional therapies compared to the high-risk group. The nomogram based on NETRPS and other clinical information predicted patient prognosis with high accuracy. ConclusionNETs play a significant role in shaping the biological and immune landscape of the HCC microenvironment. NETRPS is a promising biomarker for predicting the prognosis and therapeutic responses of HCC patients.

Novel chemokine related LncRNA signature correlates with the prognosis, immune landscape, and therapeutic sensitivity of esophageal squamous cell cancer

BackgroundEsophageal squamous cell carcinoma (ESCC) is closely correlated with malignant biological characteristics and poor survival. Recently, chemokines have been reported to be involved in the progression of tumors, and they can also regulate the tumor microenvironment. However, it is unclear whether chemokine-related long noncoding RNAs (lncRNAs) affect the prognosis of ESCC.MethodsWe downloaded RNA-seq and clinical data from the Gene Expression Omnibus (GEO database. Chemokine-related lncRNAs were screened by differential analysis and Pearson correlation analysis. Then, prognosis-related lncRNAs were screened by using univariate COX regression, and risk models were constructed after the least absolute shrinkage and selection operator (LASSO) regression and multivariate COX regression. The predictive value of the signature was assessed using Kaplan–Meier test, time-dependent receiver operating characteristic (ROC) curves, decision curve analysis (DCA) and calibration curve. Moreover, a nomogram to predict patients’ 1-year 3-year and 5-year prognosis was constructed. Gene set enrichment analyses (GSEA), Gene Ontology/Kyoto Encyclopedia of Genes and Genomes (GO/KEGG), evaluation of immune cell infiltration, and estimation of drug sensitivity were also conducted.ResultsIn this study, 677 chemokine-related lncRNAs were first obtained by differential analysis and Pearson correlation. Then, six chemokine-related lncRNAs were obtained by using univariate COX, LASSO and multivariate COX to construct a novel chemokine-related lncRNAs risk model. The signature manifested favorable predictive validity and accuracy both in the testing and training cohorts. The chemokine-related signature could classify ESCC patients into two risk groups well, which indicated that high-risk group exhibited poor prognostic outcome. In addition, this risk model played an important role in predicting signaling pathways, immune cell infiltration, stromal score, and drug sensitivity in ESCC patients.ConclusionsThese findings elucidated the critical role of novel prognostic chemokine-related lncRNAs in prognosis, immune landscape, and drug therapy, thus throwing light on prognostic evaluation and therapeutic targets for ESCC patients.

SAA1 Has Potential as a Prognostic Biomarker Correlated with Cell Proliferation, Migration, and an Indicator for Immune Infiltration of Tumor Microenvironment in Clear Cell Renal Cell Carcinoma.

The tumor microenvironment (TME) plays an important part in the initiation and development of clear cell renal cell carcinoma (ccRCC). However, an understanding of the immune infiltration in TME is still unknown. Our study aims to explore the correlation between the TME and the clinical features, as well as the prognosis of ccRCC. In the present study, ESTIMATE and CIBERSORT computational methods were applied to calculate the proportion of tumor-infiltrating immune cells (TICs) and the amount of immune and stromal fractions in the ccRCC form The Cancer Genome Atlas (TCGA) database. Then, we sought to find out those immune cell types and genes which may play a significant role and validated them in the GEO database. Furthermore, an immunohistochemical analysis of our external validation dataset was used to detect SAA1 and PDL1 expression in the ccRCC cancer tissues and corresponding normal tissues. Statistical analysis was performed to study the relationship between SAA1 and clinical characteristics, as well as PDL1 expression. Furthermore, a ccRCC cell model with SAA1 knockdown was constructed, which was used for cell proliferation and the migration test. The intersection analysis of the univariate COX and PPI analysis were performed to imply Serum Amyloid A1 (SAA1) as a predictive factor. The expression of SAA1 was significantly negatively correlated to OS and positively correlated to the clinical TMN stage system. The genes in the high-expression SAA1 group were basically enriched in immune-related activities. The proportion of mast cells resting was negatively correlated with SAA1 expression, indicating that SAA1 may be involved in the maintenance of the immune status for the TME. Moreover, the PDL1 expression was positively related to the SAA1 expression and negatively correlated with the patients' prognosis. Further experiments revealed that the knockdown of SAA1 inhibited ccRCC development through suppressing cell proliferation and migration. SAA1 may be a novel marker for the prognosis prediction of ccRCC patients and may play a vital role in the TME by mast cell resting and PDL1 expression. SAA1 has the potential to become a therapeutic target and indicator for immune target therapy in ccRCC treatment.

Factors associated with overall survival, progression-free survival and toxicity in patients with small cell lung cancer and thoracic irradiation in a clinical real-world setting

BackgroundSmall-cell lung cancer (SCLC) is a malignant tumor known for its poor prognosis. In addition to chemotherapy and immunotherapy irradiation plays a big role especially in inoperability. This study evaluated prognostic factors in patients with SCLC, receiving chemotherapy and thoracic irradiation, that may affect overall survival (OS), progression-free survival (PFS) and toxicity.MethodsPatients with limited disease (LD) SCLC (n = 57) and extensive disease (ED) SCLC (n = 69) who received thoracic radiotherapy were analyzed retrospectively. The prognostic factors sex, age, Karnofsky performance status (KPS), tumor-, nodal-stage and timepoint of start of irradiation in relation to the first cycle of chemotherapy were evaluated. Start of irradiation was stratified as early (le 2 cycles of chemotherapy), late (3 or 4 cycles) and very late (ge 5 cycles). Results were analyzed by Cox univariate and multivariate as well as logistic regression analysis.ResultsThe median OS of LD-SCLC patients was 23.7 months in early, and 22.0 months in late start of irradiation. In very late start, median OS was not reached. PFS was 11.8, 15.2 and 47.9 months, respectively. In patients with ED-SCLC OS was 4.3 months in early, 13.0 months in late and 12.2 months in very late start of irradiation. PFS was 6.7, 13.0 and 12.2 months, respectively. Prognosis of patients with LD- or ED-SCLC receiving late or very late start of irradiation was significantly prolonged in OS and PFS compared to an early start (p < 0.05). KPS ge 80 shows a significant increase of OS and PFS in ED-SCLC. Female sex and smaller mean lung dose were associated with lower risk of toxicity.ConclusionLate or very late start of irradiation is a prognosis-enhancing factor in LD-SCLC and ED-SCLC for OS and PFS. KPS ge 80 increases prognosis of OS and PFS in ED-SCLC as well. Toxicity is less common in female sex and patients with low mean lung dose in LD-SCLC.

Prognostic value of systemic immune inflammation index and geriatric nutrition risk index in early-onset colorectal cancer.

Systemic nutritional and inflammatory markers, which are easy to measure are associated with the progression and prognosis of many cancers. Nevertheless, among the various available indicators, optimal prognostic indicators for patients with early-onset colorectal cancer have not been identified. Therefore, the aim of this study was to identify optimal nutritional and inflammatory markers for early-onset colorectal cancer and examine the relationship between systemic nutritional and inflammatory markers before treatment and survival in patients with early-onset colorectal cancer. We retrospectively collected data from 236 eligible patients with early-onset colorectal cancer. Area under the prognostic curve (AUC) and concordance index (C-index) were used to compare seven systemic nutritional and inflammatory markers to identify the optimal inflammatory immune markers. Univariate and multivariate COX regression analyses were used to evaluate the prognostic value of indicators in the total study population and different subgroups. The AUC and C-index showed that the systemic immune inflammation index (SII) and geriatric nutrition risk index (GNRI) had higher prognostic values than other systemic nutritional and inflammatory indicators. Compared with patients in the low SII group, those in the high SII group had lower overall survival (HR, 4.42, 95% CI, 2.36-8.27, p = 0.000). Compared with patients in the high GNRI group, those in the low GNRI group had lower overall survival (HR, 0.33, 95% CI, 0.19-0.56, p = 0.000). SII was negatively associated with GNRI (R = -0.3, p < 0.001), and both were correlated with the tumor stage. SII and GNRI are suitable nutritional and inflammatory factors for predicting OS in patients with early-onset colorectal cancer; high SII and low GNRI were correlated with worse prognoses. Identifying the high inflammatory state and low nutritional state of patients before surgery and conducting active and timely therapeutic interventions could improve patient prognosis.

Identification of the pyroptosis-related gene signature and risk score model for esophageal squamous cell carcinoma.

Advanced esophageal squamous cell carcinoma (ESCC) still has a dismal prognostic outcome. However, the current approaches are unable to evaluate patient survival. Pyroptosis represents a novel programmed cell death type which widely investigated in various disorders and can influence tumor growth, migration, and invasion. Furthermore, few existing studies have used pyroptosis-related genes (PRGs) to construct a model for predicting ESCC survival. Therefore, the present study utilized bioinformatics approaches for analyzing ESCC patient data obtained from the TCGA database to construct the prognostic risk model and applied it to the GSE53625 dataset for validation. There were 12 differentially expressed PRGs in healthy and ESCC tissue samples, among which eight were selected through univariate and LASSO cox regression for constructing the prognostic risk model. According to K-M and ROC curve analyses, our eight-gene model might be useful in predicting ESCC prognostic outcomes. Based on the cell validation analysis, C2, CD14, RTP4, FCER3A, and SLC7A7 were expressed higher in KYSE410 and KYSE510 than in normal cells (HET-1A). Hence, ESCC patient prognostic outcomes can be assessed by our PRGs-based risk model. Further, these PRGs may also serve as therapeutic targets.

Influence of adverse effects of neoadjuvant chemoradiotherapy on the prognosis of patients with early-stage esophageal cancer (cT1b-cT2N0M0) based on the SEER database.

To analyze the prognostic impact of neoadjuvant chemoradiotherapy (NCRT) on early-stage (cT1b-cT2N0M0) esophageal cancer (ESCA) and construct a prognostic nomogram for these patients. We extracted the clinical data about patients diagnosed with early-stage esophageal cancer from the 2004-2015 period of the Surveillance, Epidemiology, and End Results (SEER) database. We applied the independent risk factors affecting the prognosis of patients with early-stage esophageal cancer obtained after screening by univariate and multifactorial COX regression analyses to establish the nomogram and performed model calibration using bootstrapping resamples. The optimal cut-off point for continuous variables is determined by applying X-tile software. After balancing the confounding factors by propensity score matching (PSM) and inverse probability of treatment weighting (IPTW) method, Kaplan-Meier(K-M) curve, and log-rank test were applied to evaluate the prognostic impact of NCRT on early-stage ESCA patients. Among patients who met the inclusion criteria, patients in the NCRT plus esophagectomy (ES) group had a poorer prognosis for overall survival (OS) and esophageal cancer-specific survival (ECSS) than patients in the ES alone group (p < 0.05), especially in patients who survived longer than 1 year. After PSM, patients in the NCRT + ES group had poorer ECSS than patients in the ES alone group, especially after 6 months, while OS was not significantly different between the two groups. IPTW analysis showed that, prior to 6 months patients in the NCRT + ES group had a better prognosis than patients in the ES group, regardless of OS or ECSS, whereas after 6 months, patients in the NCRT + ES group had a poorer prognosis. Based on multivariate COX analysis, we established a prognostic nomogram which showed areas under the ROC curve (AUC) for 3-, 5-, and 10-year OS 0.707, 0.712, and 0.706, respectively, with the calibration curves showing that the nomogram was well calibrated. Patients with early-stage ESCA (cT1b-cT2) did not benefit from NCRT, and we established a prognostic nomogram to provide clinical decision aid for the treatment of patients with early-stage ESCA.

Predictors of clinical outcomes in space-occupying cerebellar infarction undergoing suboccipital decompressive craniectomy.

Despite current clinical guidelines recommending suboccipital decompressive craniectomy (SDC) in cerebellar infarction when patients present with neurological deterioration, the precise definition of neurological deterioration remains unclear and accurate timing of SDC can be challenging. The current study aimed at characterizing whether clinical outcomes can be predicted by the GCS score immediately prior to SDC and whether higher GCS scores are associated with better clinical outcomes. In a single-center, retrospective analysis of 51 patients treated with SDC for space-occupying cerebellar infarction, clinical and imaging data were evaluated at the time points of symptom onset, hospital admission, and preoperatively. Clinical outcomes were measured by the mRS. Preoperative GCS scores were stratified into three groups (GCS, 3-8, 9-11, and 12-15). Univariate and multivariate Cox regression analyses were performed using clinical and radiological parameters as predictors of clinical outcomes. In cox regression analysis GCS scores of 12-15 at surgery were significant predictors of positive clinical outcomes (mRS, 1-2). For GCS scores of 3-8 and 9-11, no significant increase in proportional hazard ratios was observed. Negative clinical outcomes (mRS, 3-6) were associated with infarct volume above 6.0 cm3, tonsillar herniation, brainstem compression, and a preoperative GCS score of 3-8 [HR, 2.386 (CI, 1.160-4.906); p = 0.018]. Our preliminary findings suggest that SDC should be considered in patients with infarct volumes above 6.0 cm3 and with GCS between 12 and 15, as these patients may show better long-term outcomes than those in whom surgery is delayed until a GCS score below 11.

OHCCPredictor: an online risk stratification model for predicting survival duration of older patients with hepatocellular carcinoma

BackgroundAlthough the elderly constitute more than a third of hepatocellular carcinoma (HCC) patients, they have not been adequately represented in treatment and prognosis studies. Thus, there is not enough evidence to guide the treatment of such patients. The objective of this study is to identify the prognostic factors of older patients with HCC and to construct a new prognostic model for predicting their overall survival (OS).Methods2,721 HCC patients aged ≥ 65 were extracted from the public database-Surveillance, Epidemiology, and End Results (SEER) and randomly divided into a training set and an internal validation set with a ratio of 7:3. 101 patients diagnosed from 2008 to 2017 in the First Affiliated Hospital of Zhejiang University School of Medicine were identified as the external validation set. Univariate cox regression analyses and multivariate cox regression analyses were adopted to identify these independent prognostic factors. A predictive nomogram-based risk stratification model was proposed and evaluated using area under the receiver operating characteristic curve (AUC), calibration curves, and a decision curve analysis (DCA).ResultsThese attributes including age, sex, marital status, T stage, N stage, surgery, chemotherapy, tumor size, alpha-fetoprotein level, fibrosis score, bone metastasis, lung metastasis, and grade were the independent prognostic factors for older patients with HCC while predicting survival duration. We found that the nomogram provided a good assessment of OS at 1, 3, and 5 years in older patients with HCC (1-year OS: (training set: AUC = 0.823 (95%CI 0.803–0.845); internal validation set: AUC = 0.847 (95%CI 0.818–0.876); external validation set: AUC = 0.732 (95%CI 0.521–0.943)); 3-year OS: (training set: AUC = 0.813 (95%CI 0.790–0.837); internal validation set: AUC = 0.844 (95%CI 0.812–0.876); external validation set: AUC = 0.780 (95%CI 0.674–0.887)); 5-year OS: (training set: AUC = 0.839 (95%CI 0.806–0.872); internal validation set: AUC = 0.800 (95%CI 0.751–0.849); external validation set: AUC = 0.821 (95%CI 0.727–0.914)). The calibration curves showed that the nomogram was with strong calibration. The DCA indicated that the nomogram can be used as an effective tool in clinical practice. The risk stratification of all subgroups was statistically significant (p < 0.05). In the stratification analysis of surgery, larger resection (LR) achieved a better survival curve than local destruction (LD), but a worse one than segmental resection (SR) and liver transplantation (LT) (p < 0.0001). With the consideration of the friendship to clinicians, we further developed an online interface (OHCCPredictor) for such a predictive function (https://juntaotan.shinyapps.io/dynnomapp_hcc/). With such an easily obtained online tool, clinicians will be provided helpful assistance in formulating personalized therapy to assess the prognosis of older patients with HCC.ConclusionsAge, sex, marital status, T stage, N stage, surgery, chemotherapy, tumor size, AFP level, fibrosis score, bone metastasis, lung metastasis, and grade were independent prognostic factors for elderly patients with HCC. The constructed nomogram model based on the above factors could accurately predict the prognosis of such patients. Besides, the developed online web interface of the predictive model provide easily obtained access for clinicians.

TROP-2 is widely expressed in vulvar squamous cell carcinoma and represents a potential new therapeutic target.

Vulvar squamous cell carcinoma (VSCC) is a rare malignancy of the female genital tract with increasing incidence rates. Etiologically, HPV-dependent and HPV-independent VSCC are distinguished. Surgical treatment and/or radiotherapy represent the therapeutic mainstay for localized disease. For recurrent or metastatic VSCC, treatment options are limited. Research has identified trophoblast cell surface antigen 2 (TROP-2) to be broadly expressed across different tumor entities. The aim of the present study was to systematically investigate the expression of TROP-2 in VSCC. TROP-2 protein expression was investigated by immunohistochemistry in a cohort comprising n = 103 patients with primary VSCC. A four-tier scoring system (0: no staining, 1 + : low staining, 2 + : moderate staining, 3 + : high staining) was applied for quantification of protein expression. For further analyses, two groups (low TROP-2 expression: 0/1 + ; high TROP-2 expression: 2 + /3 +) were generated. The entire study cohort, as well as HPV-dependent and HPV-independent VSCC were considered separately. In the entire VSCC study cohort, TROP-2 expression was present in 97.1% of all cases (n = 100) with 74.8% displaying high TROP-2 expression (2 + /3 +). Only 2.9% of tumors showed absent TROP-2 expression. Of note, all HPV-dependent VSCC (n = 18) demonstrated high TROP-2 expression (2 + /3 +). In the subgroup of HPV-independent VSCC (n = 70), high TROP-2 expression was associated with favorable clinical outcomes based on log rank test and univariate cox analysis. TROP-2 protein expression is of prognostic value in HPV-independent VSCC. The broad expression of TROP-2 in VSCC indicates the TROP-2 directed ADC Sacituzumab govitecan as a potential new therapeutic strategy for VSCC patients.

Establishment of a 7-gene prognostic signature based on oxidative stress genes for predicting chemotherapy resistance in pancreatic cancer.

Background: Oxidative stress is involved in regulating various biological processes in human cancers. However, the effect of oxidative stress on pancreatic adenocarcinoma (PAAD) remained unclear. Methods: Pancreatic cancer expression profiles from TCGA were downloaded. Consensus ClusterPlus helped classify molecular subtypes based on PAAD prognosis-associated oxidative stress genes. Limma package filtered differentially expressed genes (DEGs) between subtypes. A multi-gene risk model was developed using Lease absolute shrinkage and selection operator (Lasso)-Cox analysis. A nomogram was built based on risk score and distinct clinical features. Results: Consistent clustering identified 3 stable molecular subtypes (C1, C2, C3) based on oxidative stress-associated genes. Particularly, C3 had the optimal prognosis with the greatest mutation frequency, activate cell cycle pathway in an immunosuppressed status. Lasso and univariate cox regression analysis selected 7 oxidative stress phenotype-associated key genes, based on which we constructed a robust prognostic risk model independent of clinicopathological features with stable predictive performance in independent datasets. High-risk group was found to be more sensitive to small molecule chemotherapeutic drugs including Gemcitabine, Cisplatin, Erlotinib and Dasatinib. The 6 of 7 genes expressions were significantly associated with methylation. Survival prediction and prognostic model was further improved through a decision tree model by combining clinicopathological features with RiskScore. Conclusion: The risk model containing seven oxidative stress-related genes may have a greater potential to assist clinical treatment decision-making and prognosis determination.

Copper-binding proteins genes set predicting the overall survival and immune infiltration in hepatocellular carcinoma by bioinformatic analysis

Abnormal Copper (Cu) accumulation shared a close association with hepatocellular carcinoma (HCC), but the regulatory role of Copper-binding proteins in HCC remains largely unknown. The aim of study was to identify the potential regulatory role of Cu-binding proteins, including copper homeostasis maintainer and the downstream effectors of Cu, in the progression of HCC. We conducted a comprehensive bioinformatic analysis of Cu-binding proteins in HCC using data from TCGA and ICGC database. Univariate cox regression analysis was conducted, and four prognostic Cu-binding proteins was identified to be differentially expressed between the normal liver tissues and HCC tissues. In addition, the Cu-binding proteins-based predictive signature (CuPscore) model was generated using the least absolute shrinkage and selection operator (LASSO) cox regression model. Here, we identified the crucial prognostic value of CuPscore in HCC. The pathological stage and CuPscore were independent risk factors for the prognosis of HCC patients. Pathological stage and CuPscore-based nomogram model exhibited great performance in predicting the prognosis of HCC patients. We also observed that the CuPscore shared a close association with several immunomodulatory molecules and the proportion of several tumor infiltrating immune cells, suggesting a potential value of CuPscore in predicting the response to immunotherapy in HCC. Our results demonstrated the prognostic value of Cu-binding proteins and its correlation with immune microenvironment in HCC, providing a therapeutic basis for the precision medicine strategy through targeting Cu-binding proteins in HCC.

A single-arm phase II clinical trial of anlotinib combined with chemotherapy for the treatment of metastatic triple-negative breast cancer.

Anlotinib is a novel oral small-molecule tyrosine kinase inhibitor (TKI), which can inhibit angiogenesis. The purpose of this study was to evaluate the efficacy and safety of anlotinib combined with chemotherapy in patients with metastatic triple-negative breast cancer (TNBC). This phase II clinical trial included 40 patients with metastatic TNBC who had previously received anthracycline and/or taxane treatment. All patients received anlotinib combined with chemotherapy. The primary endpoint was progression-free survival (PFS). The secondary endpoints included overall survival (OS), objective response rate (ORR), clinical benefit rate (CBR), disease control rate (DCR) and safety. During May 1, 2019 and April 30, 2022, there were 40 patients enrolled in this study. The median PFS and median OS were 8.8 months (95% confidence interval [CI] 6.5-11.1 months) and 19.0 months (95% CI, 12.1-25.9 months), respectively. The ORR, CBR and DCR were 40.0% (16/40), 85.0% (34/40) and 95.0% (38/40), respectively. Cox univariate and multivariate analyses demonstrated that having more than 3 metastatic sites (p = 0.001; p = 0.020) was an independent and meaningful unfavorable prognostic factor for PFS. 37.5% of patients had grade 3 to 4 treatment-related adverse events (TRAEs). The grade 3 to 4 TRAEs included neutropenia (22.5%), leukopenia (20.0%), secondary hypertension (10.0%), hand-foot syndrome (5.0%), vomiting (5.0%), proteinuria (5.0%) and thrombocytopenia (2.5%). None of the patients withdrew from the study or died due to TRAEs. In this single-arm study, the treatment of metastatic TNBC with anlotinib combined with chemotherapy showed certain efficacy, and its toxicity was acceptable.

Tumor size as a significant prognostic factor in T1 gastric cancer: a Surveillance, Epidemiology, and End Results (SEER) database analysis

BackgroundIt has previously been observed that the prognostic value of tumor size varied according to different stages patients enrolled in gastric cancer. We aimed to investigate the influence of T stage on the prognostic and predicting value of tumor size.Material and methodsA total of 13,585 patients with stage I–III gastric cancer were selected from the Surveillance, Epidemiology, and End Results Program (SEER) database. Univariate and multivariate cox regression analysis stratified by T stage were performed. C-index and time-dependent receiver operating characteristic curve (ROC) curve were applied to assess discrimination ability of tumor size and other factors. Nomograms were constructed to further assess the performance of tumor size in a specific model. Calibration ability, discrimination ability, reclassification ability and clinical benefits were executed to judge the performance of models.ResultsStratified analyses according to T stage illustrated that with the increase of T stage, the effect of tumor size on overall survival (OS) and cancer-specific survival (CSS) significantly decreased. Moreover, tumor size showed superior discrimination ability in T1 gastric cancer, outperformed other prognostic factors in predicting both CSS (C-index: 0.666, AUC: 0.687) and OS (C-index: 0.635, AUC: 0.660). The cox regression model included tumor size showed better performance than the model excluded tumor size in every aspect.ConclusionT stage had a negative impact on the predicting value of tumor size. Tumor size showed significant prognostic value in T1 gastric cancer, which may be effective in clinical practice.

RNA methylation-related genes of m6A, m5C, and m1A predict prognosis and immunotherapy response in cervical cancer

Purpose To investigate the prognostic value of N6-methyladenosine (m6A)-, 5-methylcytosine (m5C)-, and N1-methyladenosine (m1A)-related genes in cervical cancer (CESC) and predicting immunotherapy response. Methods We downloaded cervical cancer mRNA expression profiles, clinical data, and m6A, m5C, m1A-related genes from public databases, and subjected them to serial bioinformatics analysis and clinical sample validation. Results Differential analysis revealed 106 methylation-related differential genes (MEDs), including 44 differentially downregulated and 62 upregulated genes. We then obtained methylation models containing 10 genes by univariate and multifactorial COX analysis. High risk genes with HR > 1 include IQGAP3, PTBP1, STAC3, CUX1, SLC2A1, and CA2, and low risk genes with HR < 1 include IGBP1, DUOX1, CHAF1A, and STAC3. We verified the accuracy of the model from inside TCGA and outside GSE39001 (AUC = 0.729). K-M analysis showed shorter survival times in the High-risk group, and Immunocytic infiltration analysis showed model genes closely associated with six immune cells. The high-risk group may benefit more effectively from immunosuppressive therapy, especially anti-CTLA-4 therapy (p < .05). We also screened nine drugs for potential treatment and verified the expression of three key genes SLC2A1, CUX1, and CA2 using immunohistochemistry and RT-qPCR experiments with clinical samples. Conclusion We identified a prognostic model using m6A/m5C/m1A-related genes in cervical cancer, which can predict survival time and correlate with immune cell infiltration. Additionally, anti-CTLA-4 may be used as an immunotherapeutic agent for cervical cancer. KEY MESSAGES Cervical cancer still has a high mortality rate, we aim to establish a strong prognostic index and new treatment goals for improving patient survival. The role of three types of RNA methylation modifications, m6A, m5C, and m1A, in cervical cancer, remains unknown. Therefore, it is essential to explore the potential molecular mechanisms of m6A, m5C, and m1A methylation regulation in cervical cancer. We also screened nine drugs for potential treatment and anti-CTLA-4 may be used as an immunotherapeutic agent for cervical cancer. We verified the expression of three key genes SLC2A1, CUX1, and CA2

ALOX5AP is an Indicator for High CD8 Lymphocyte Infiltration and "Hot" Tumor Microenvironment in Osteosarcoma: A Bioinformatic Study.

Little progress has been made in the treatment and prognosis of osteosarcoma in the past 40years. Tumor microenvironment (TME) plays a critical role in the progression of osteosarcoma. This study aims to determine immune-associated prognostic biomarkers for osteosarcoma patients. With the help of analytical tools including ESTIMATE, differential gene expression, LASSO, and univariate cox and multivariate cox regression analysis, osteosarcoma gene expression data from Gene Expression Omnibus (GEO) databases were investigated. Following the establishment of a prognostic risk score model, internal and external validations using the GEO and TARGET databases were carried out. A total of 44 and 55 samples respectively in the GSE21257 and the TARGET databases were included. Our analysis found 93 differentially expressed genes (DEGs) between the high and low-ImmuneScore groups. Through univariate cox and LASSO analysis, ALOX5AP was identified as an indicator of TME in osteosarcomas. ALOX5AP was then used to construct a prognostic risk model. Internal and external verification revealed that higher expression of ALOX5AP was correlated with lower risk. Through the CIBERSORT algorithm, the level of CD8 T cells was found to negatively correlate with the risk score. This study revealed that ALOX5AP is an indicator for predicting high CD8 lymphocyte infiltration and "hot" tumor microenvironment in osteosarcomas. Thus, ALOX5AP has the potential to act as a biomarker for effective immunotherapies in osteosarcoma patients.