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Background: Gastric cardia adenocarcinoma (GCA) is a highly fatal form of cancer in humans. The aim of this study was to extract clinicopathological data of postoperative patients with GCA from the Surveillance, Epidemiology, and End Results database, analyze prognostic risk factors, and build a nomogram. Methods: In this study, the clinical information of 1448 patients with GCA who underwent radical surgery and were diagnosed between 2010 and 2015 was extracted from the SEER database. The patients were then randomly divided into training (n = 1013) and internal validation (n = 435) cohorts at a 7:3 ratio. The study also included an external validation cohort (n = 218) from a Chinese hospital. The study used the Cox and LASSO models to pinpoint the independent risk factors linked to GCA. The prognostic model was constructed according to the results of the multivariate regression analysis. To assess the predictive accuracy of the nomogram, four methods were used: C‐index, calibration curve, time‐dependent ROC curve, and DCA curve. Kaplan–Meier survival curves were also generated to illustrate the differences in cancer‐specific survival (CSS) between the groups. Results: The results of the multivariate Cox regression analysis showed that age, grade, race, marital status, T stage, and log odds of positive lymph nodes (LODDS) were independently associated with cancer‐specific survival in the training cohort. Both the C‐index and AUC values depicted in the nomogram were greater than 0.71. The calibration curve revealed that the nomogram's CSS prediction was consistent with the actual outcomes. The decision curve analysis suggested moderately positive net benefits. Based on the nomogram risk score, significant differences in survival between the high‐ and low‐risk groups were observed. Conclusions: Race, age, marital status, differentiation grade, T stage, and LODDS are independent predictors of CSS in patients with GCA after radical surgery. Our predictive nomogram constructed based on these variables demonstrated good predictive ability. The nomogram for postoperative patients with GCA we constructed has good predictive power and can help clinicians accurately assess patient prognosis and identify high‐risk patients to develop more personalized treatment plans. [ABSTRACT FROM AUTHOR]

Introduction: Cervical cancer has high mortality, high recurrence and poor prognosis. Although prognostic biomarkers such as clinicopathological features have been proposed, their accuracy and precision are far from satisfactory. Therefore, novel biomarkers are urgently needed for disease surveillance, prognosis prediction and treatment selection. Materials: Differentially expressed genes (DEGs) between cervical cancer and normal tissues from three microarray datasets extracted from the Gene Expression Omnibus platform were identified and screened. Based on these DEGs, a six‐gene prognostic signature was constructed using cervical squamous cell carcinoma and endocervical adenocarcinoma data from The Cancer Genome Atlas. Next, the molecular functions and related pathways of the six genes were investigated through gene set enrichment analysis and co‐expression analysis. Additionally, immunophenoscore analysis and the QuartataWeb Server were employed to explore the therapeutic value of the six‐gene signature. Results: We discovered 178 overlapping DEGs in three microarray datasets and established a six‐gene (APOC1, GLTP, ISG20, SPP1, SLC24A3 and UPP1) prognostic signature with stable and excellent performance in predicting overall survival in different subgroups. Intriguingly, the six‐gene signature was closely associated with the immune response and tumour immune microenvironment. The six‐gene signature might be used for predicting response to immune checkpoint inhibitors (ICIs) and the six genes may serve as new drug targets for cervical cancer. Conclusion: Our study established a novel six‐gene (APOC1, GLTP, ISG20, SPP1, SLC24A3 and UPP1) signature that was closely associated with the immune response and tumour immune microenvironment. The six‐gene signature was indicative of aggressive features of cervical cancer and therefore might serve as a promising biomarker for predicting not only overall survival but also ICI treatment effectiveness. Moreover, three genes (UPP1, ISG20 and GLTP) within the six‐gene signature have the potential to become novel drug targets. [ABSTRACT FROM AUTHOR]

Geriatric assessment can aid in optimizing treatment strategies and supportive interventions for older patients with diffuse large B-cell lymphoma (DLBCL). Fondazione Italiana Linformi has recently introduced novel geriatric assessment tools, simplified Geriatric Assessment (sGA) and Elderly Prognostic Index (EPI), aimed at tailoring the treatment and predicting the outcomes for older patients with DLBCL. The objectives of this study are the validation and possible modification of the sGA and EPI in China. In the study, both sGA and EPI demonstrated the predictive capabilities for overall survival (OS) and early mortality (both p<0.05) in older individuals with DLBCL. Albumin, serving as an independent predictive biomarker for OS (p=0.006), was utilized to adjust the measurements, resulting in the establishment of sGA-A and EPI-A. The sGA-A effectively addressed the shortcomings of the sGA and EPI in predicting PFS and surpassed them in predicting OS and early mortality. Nevertheless, there is insufficient evidence to support the use of sGA and EPI as treatment guidance tools. In conclusion, the modified sGA-A model proved to be a successful instrument for geriatric assessment of older patients with DLBCL. [ABSTRACT FROM AUTHOR]

Background: Cornichon homolog 4 (CNIH4) belongs to the CNIH family. It functions as an oncogene in many tumors. However, CNIH4's significance in the immune landscape and its predictive potential in cervical cancer (CESC) is unexplored. Methods: CNIH4 levels and its effect on the survival of patients with CESC were evaluated using data retrieved from The Cancer Genome Atlas (TCGA). The oncogenic effect of CNIH4 in CESC was determined using small interfering RNA‐mediated transfected cell lines and tumorigenesis experiments in animal models. Results: Higher expression of CNIH4 was found in advanced tumor and pathological stages, as well as lymph node metastasis. CNIH4 expression correlated positively with the infiltration of macrophages M2 and resting dendritic cells into the affected tissue. Additionally, functional enrichment of RNA‐sequencing of CNIH4‐knocked down CESC cell lines showed the association of CNIH4 to the PI3K‐Akt signaling pathway. Single‐sample gene set enrichment analysis highlighted several immune pathways that were elevated in the CESC samples with enhanced levels of CNIH4, including Type‐I and Type‐II IFN‐response pathways. The impact of CNIH4 on drug sensitivity was further assessed using the GDSC database. As CNIH4 is linked to the immune landscape in CESC, this study determined a four‐gene risk prediction signature utilizing CNIH4‐related immunomodulators. The risk score quantified from the prediction signature was an independent predictive indicator in CESC. Receiver operating characteristic curve analysis verified the good predictive ability of the four‐gene signature in TCGA‐CESC cohort. Thus, the CNIH4‐related model showed potential as an auxiliary TNM staging system tool. Conclusion: CNIH4 may be an effective predictive biomarker for patients with cervical cancer, thus providing new ideas and research directions for CESC. [ABSTRACT FROM AUTHOR]

Background: Chemotherapy is still the standard regimen for treating acute myeloid leukemia (AML) and its disappointing efficacy requires the urgent need for new therapeutic targets. It is well known that immune response plays an increasingly significant role in the pathogenesis of AML. Methods: We detected nine single nucleotide polymorphisms (SNPs) in immune checkpoint‐related genes, including PD1, LAG3, TIM3, and TIGIT in 285 AML inpatients and 324 healthy controls. SNP genotyping was performed on the MassARRAY platform. Furthermore, we analyzed the relationship between the susceptibility and prognosis of AML and the selected SNPs. Results: Our results showed that rs2227982 and rs10204525 in PD1 were significantly associated with susceptibility to AML after false discovery rate correction. PD1 rs10204525 also showed a significant correlation with the response to chemotherapy and risk stratification of AML. Importantly, the AA genotype of PD1 (rs2227982) under the recessive model showed a negative impact on AML prognosis independently. Conclusions: Our results indicate that PD1 SNPs are important for susceptibility and prognosis in AML, which may provide a new therapeutic target for AML patients. [ABSTRACT FROM AUTHOR]