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Small-cell lung cancer (SCLC) is an aggressive lung cancer subtype with an extremely poor prognosis. The 5-year survival rate for limited-stage (LS)-SCLC cancer is 10–13%, while the rate for extensive-stage SCLC cancer is only 1–2%. Given the crucial role of the tumor stage in the disease course, a well-constructed prognostic model is warranted for patients with LS-SCLC. The LS-SCLC patients' clinical data extracted from the Surveillance, Epidemiology, and End Results (SEER) database between 2000 and 2018 were reviewed. A multivariable Cox regression approach was utilized to identify and integrate significant prognostic factors. Bootstrap resampling was used to validate the model internally. The Area Under Curve (AUC) and calibration curve evaluated the model's performance. A total of 5463 LS-SCLC patients' clinical data was collected from the database. Eight clinical parameters were identified as significant prognostic factors for LS-SCLC patients' OS. The predictive model achieved satisfactory discrimination capacity, with 1-, 2-, and 3-year AUC values of 0.91, 0.88, and 0.87 in the training cohort; and 0.87, 0.87, and 0.85 in the validation cohort. The calibration curve showed a good agreement with actual observations in survival rate probability. Further, substantial differences between survival curves of the different risk groups stratified by prognostic scores were observed. The nomogram was then deployed into a website server for ease of access. This study developed a nomogram and a web-based predictor for predicting the overall survival of patients with LS-SCLC, which may help physicians make personalized clinical decisions and treatment strategies. [ABSTRACT FROM AUTHOR]

Introduction: Patients presenting with brain metastases (BMs) from lung squamous cell carcinoma (LUSC) often encounter an extremely poor prognosis. A well‐developed prognostic model would assist physicians in patient counseling and therapeutic decision‐making. Methods: Patients with LUSC who were diagnosed with BMs between 2000 and 2018 were reviewed in the Surveillance, Epidemiology, and End Results (SEER) database. Using the multivariate Cox regression approach, significant prognostic factors were identified and integrated. Bootstrap resampling was used to internally validate the model. An evaluation of the performance of the model was conducted by analyzing the area under the curve (AUC) and calibration curve. Results: A total of 1812 eligible patients' clinical data was retrieved from the database. Patients' overall survival (OS) was significantly prognosticated by five clinical parameters. The nomogram achieved satisfactory discrimination capacity, with 3‐, 6‐, and 9‐month AUC values of 0.803, 0.779, and 0.760 in the training cohort and 0.796, 0.769, and 0.743 in the validation cohort. As measured by survival rate probabilities, the calibration curve agreed well with actual observations. There was also a substantial difference in survival curves between the different prognostic groups stratified by prognostic scores. For ease of access, the model was deployed on a web‐based server. Conclusions: In this study, a nomogram and a web‐based predictor were developed to assist physicians with personalized clinical decisions and treatment of patients who presented with BMs from LUSC. [ABSTRACT FROM AUTHOR]

Clear cell renal cell carcinoma (ccRCC) is the most common histological and devastating subtype of renal cell carcinoma. Necroptosis is a form of programmed cell death that causes prominent inflammatory responses. miRNAs play a significant role in cancer progression through necroptosis. However, the prognostic value of necroptosis-related miRNAs remains ambiguous. In this study, 39 necroptosis-related miRNAs (NRMs) were extracted and 17 differentially expressed NRMs between normal and tumor samples were identified using data form The Cancer Genome Atlas (TCGA). After applying univariate Cox proportional hazard regression analysis and LASSO Cox regression model, six necroptosis-related miRNA signatures were identified in the training cohort and their expression levels were verified by qRT-PCR. Using the expression levels of these miRNAs, all patients were divided into the high- and low-risk groups. Patients in the high-risk group showed poor overall survival (P < 0.0001). Time-dependent ROC curves confirmed the good performance of our signature. The results were verified in the testing cohort and the entire TCGA cohort. Univariate and multivariate Cox regression models demonstrated that the risk score was an independent prognostic factor. Additionally, a predictive nomogram with good performance was constructed to enhance the implementation of the constructed signature in a clinical setting. We then employed miRBD, miRTarBase, and TargetScan to predict the target genes of six necroptosis-related miRNAs. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses indicated that 392 potential target genes were enriched in cell proliferation-related biological processes. Six miRNAs and 59 differentially expressed target genes were used to construct an miRNA–mRNA interaction network, and 11 hub genes were selected for survival and tumor infiltration analysis. Drug sensitivity analysis revealed potential drugs that may contribute to cancer management. Hence, necroptosis-related genes play an important role in cancer biology. We developed, for the first time, a necroptosis-related miRNA signature to predict ccRCC prognosis. [ABSTRACT FROM AUTHOR]

Increasing evidences show the clinical significance of the interaction between hypoxia and immune in clear cell renal cell carcinoma (ccRCC) microenvironment. However, reliable prognostic signatures based on a combination of hypoxia and immune have not been well established. Moreover, many studies have only used RNA-seq profiles to screen the prognosis feature of ccRCC. Presently, there is no comprehensive analysis of multiomics data to mine a better one. Thus, we try and get it. First, t-SNE and ssGSEA analysis were used to establish tumor subtypes related to hypoxia-immune, and we investigated the hypoxia-immune–related differences in three types of genetic or epigenetic characteristics (gene expression profiles, somatic mutation, and DNA methylation) by analyzing the multiomics data from The Cancer Genome Atlas (TCGA) portal. Additionally, a four-step strategy based on lasso regression and Cox regression was used to construct a satisfying prognostic model, with average 1-year, 3-year and 5-year areas under the curve (AUCs) equal to 0.806, 0.776 and 0.837. Comparing it with other nine known prognostic biomarkers and clinical prognostic scoring algorithms, the multiomics-based signature performs better. Then, we verified the gene expression differences in two external databases (ICGC and SYSU cohorts). Next, eight hub genes were singled out and seven hub genes were validated as prognostic genes in SYSU cohort. Furthermore, it was indicated high-risk patients have a better response for immunotherapy in immunophenoscore (IPS) analysis and TIDE algorithm. Meanwhile, estimated by GDSC and cMAP database, the high-risk patients showed sensitive responses to six chemotherapy drugs and six candidate small-molecule drugs. In summary, the signature can accurately predict the prognosis of ccRCC and may shed light on the development of novel hypoxia-immune biomarkers and target therapy of ccRCC. [ABSTRACT FROM AUTHOR]

Background: Lung adenocarcinoma is one of the most common malignant tumors of the respiratory system, ranking first in morbidity and mortality among all cancers. This study aims to establish a ferroptosis-related gene-based prognostic model to investigate the potential prognosis of lung adenocarcinoma. Methods: We obtained gene expression data with matching clinical data of lung adenocarcinoma from the The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The ferroptosis-related genes (FRGs) were downloaded from three subgroups in the ferroptosis database. Using gene expression differential analysis, univariate Cox regression, and LASSO regression analysis, seven FRGs with prognostic significance were identified. The result of multivariate Cox analysis was utilized to calculate regression coefficients and establish a risk-score formula that divided patients with lung adenocarcinoma into high-risk and low-risk groups. The TCGA results were validated using GEO data sets. Then we observe d that patients divided in the low-risk group lived longer than the overall survival (OS) of the other. Then we developed a novel nomogram including age, gender, clinical stage, TNM stage, and risk score. Results: The areas under the curves (AUCs) for 3- and 5-years OS predicted by the model were 0.823 and 0.852, respectively. Calibration plots and decision curve analysis also confirmed the excellent predictive performance of the model. Subsequently, gene function enrichment analysis revealed that the identified FRGs are important in DNA replication, cell cycle regulation, cell adhesion, chromosomal mutation, oxidative phosphorylation, P53 signaling pathway, and proteasome processes. Conclusions: Our results verified the prognostic significance of FRGs in patients with lung adenocarcinoma, which may regulate tumor progression in a variety of pathways. [ABSTRACT FROM AUTHOR]

Background: As an end-proteolytic enzyme that cleaves the last three residues of proteins with a terminal CAAX, Ras-converting enzyme 1 (RCE1) has an essential role in multiple signaling pathways and take part in the process of differentiation, proliferation and carcinogenesis. The aim of the study is to investigate expression pattern of RCE1 and its prognosis in colorectal carcinoma (CRC).Methods: The expression of RCE1 and phospho-MAPK family members was confirmed by immunohistochemical staining of CRC tissues. miR-RCE1 lentiviral vectors were transduced into HCT116 and SW489 cells. Reverse transcription PCR (RT-PCR) and western blot were conducted to measure the transfection efficiency. Transwell assays were used to detect the invasiveness of CRC cells.Results: In the present study, we assessed RCE1 expression in 244 CRC specimens and matching adjacent, non-tumorous tissues by immunohistochemistry (IHC). Compared with the matched adjacent non-tumor tissue samples, the RCE1 reduced in the tumor tissue samples (p < 0.001). RCE1 expression was significantly decreased in 106 of 244 (43.4%) CRC cases. In univariate and multivariate analyses, Decreasing expression of RCE1 independently predicts poor prognosis for patients in both overall survival and disease-free survival. Further study indicated that RCE1 influenced tumor invasion through the p38 pathway. Knockdown of RCE1 reduced phosphorylation and significantly increased the invasive capacity of CRC cells.Conclusion: Taken together, the outcomes of this study indicate that RCE1 acts as a tumor suppressor in CRC, as its reduced expression may increase CRC cell invasion and independently predict an unsatisfactory prognosis in CRC patients. [ABSTRACT FROM AUTHOR]

The high incidence of recurrence and the poor prognosis of hepatocellular carcinoma ( HCC) necessitate the discovery of new predictive markers of HCC invasion and prognosis. In this study, we evaluated the expression pattern of two members of a novel oncogene family, Musashi1 ( MSI1) and Musashi2 ( MSI2) in 40 normal hepatic tissue specimens, 149 HCC specimens and their adjacent non-tumourous tissues. We observed that MSI1 and MSI2 were significantly up-regulated in HCC tissues. High expression levels of MSI1 and MSI2 were detectable in 37.6% (56/149) and 49.0% (73/149) of the HCC specimens, respectively, but were rarely detected in adjacent non-tumourous tissues and were never detected in normal hepatic tissue specimens. Nevertheless, only high expression of MSI2 correlated with poor prognosis. In addition, MSI2 up-regulation correlated with clinicopathological parameters representative of highly invasive HCC. Further study indicated that MSI2 might enhance invasion of HCC by inducing epithelial-mesenchymal transition ( EMT). Knockdown of MSI2 significantly decreased the invasion of HCC cells and changed the expression pattern of EMT markers. Moreover, immunohistochemistry assays of 149 HCC tissue specimens further confirmed this correlation. Taken together, the results of our study demonstrated that MSI2 correlates with EMT and has the potential to be a new predictive biomarker of HCC prognosis and invasion to help guide diagnosis and treatment of post-operative HCC patients. [ABSTRACT FROM AUTHOR]