• bioinformatics analysis
• immune infiltration
• pan‑cancer
• prognosis
• sideroflexin 1

• Humans
• Adenocarcinoma of Lung/genetics
• Adenocarcinoma of Lung/immunology
• Adenocarcinoma of Lung/pathology
• Adenocarcinoma of Lung/mortality
• Prognosis
• Lung Neoplasms/genetics
• Lung Neoplasms/immunology
• Lung Neoplasms/pathology
• Lung Neoplasms/mortality
• Biomarkers, Tumor/genetics
• Biomarkers, Tumor/metabolism
• Cell Proliferation/genetics
• Gene Expression Regulation, Neoplastic
• Cell Movement
• Female
• Male
• Apoptosis
• Cell Line, Tumor
• Carrier Proteins/genetics
• Carrier Proteins/metabolism
• Gene Expression Profiling

The presence of liver metastasis (LM) is an independent prognostic factor for shorter survival in non-small cell lung cancer (NSCLC) patients. The median overall survival of patients with involvement of the liver is less than 5 mo. At present, identifying prognostic factors and constructing survival prediction nomogram for NSCLC patients with LM (NSCLC-LM) are highly desirable.

To build a forecasting model to predict the survival time of NSCLC-LM patients.

Data on NSCLC-LM patients were collected from the Surveillance, Epidemiology, and End Results database between 2010 and 2018. Joinpoint analysis was used to estimate the incidence trend of NSCLC-LM. Kaplan-Meier curves were constructed to assess survival time. Cox regression was applied to select the independent prognostic predictors of cancer-specific survival (CSS). A nomogram was established and its prognostic performance was evaluated.

The age-adjusted incidence of NSCLC-LM increased from 22.7 per 1000000 in 2010 to 25.2 in 2013, and then declined to 22.1 in 2018. According to the multivariable Cox regression analysis of the training set, age, marital status, sex, race, histological type, T stage, metastatic pattern, and whether the patient received chemotherapy or not were identified as independent prognostic factors for CSS (P < 0.05) and were further used to construct a nomogram. The C-indices of the training and validation sets were 0.726 and 0.722, respectively. The results of decision curve analyses (DCAs) and calibration curves showed that the nomogram was well-discriminated and had great clinical utility.

We designed a nomogram model and further constructed a novel risk classification system based on easily accessible clinical factors which demonstrated excellent performance to predict the individual CSS of NSCLC-LM patients.

• Non-small cell lung cancer
• Liver metastasis
• Nomogram
• Risk classification system

Nowadays, Jin-Fu-Kang oral liquid (JFK), one of Chinese herbal medicines (CHMs) preparations, has been widely used as an adjuvant therapy for primary non-small cell lung cancer (PNSCLC) patients with the syndrome of deficiency of both Qi and Yin (Qi–Yin deficiency pattern) based on Traditional Chinese Medicine (TCM) theory. However, we found insufficient evidence of how long-term CHM treatment influence PNSCLC patients’ progression-free survival (PFS). Thus, using electronic medical records, we established a nomograph-based prognostic model for predicting PNSCLC patients’ PFS involved with JFK supplementary formulas (JFK-SFs) over 6 months, in order to preliminarily investigate potential predictors highly related to adjuvant CHMs therapies in theoretical epidemiology. In our retrospective study, a series of 197 PNSCLC cases from Long Hua Hospital were enrolled by non-probability sampling and divided into 2 datasets at the ratio of 5:4 by Kennard–Stone algorithm, as a result of 109 in training dataset and 88 in validation dataset. Besides, TNM stage, operation history, sIL-2R, and CA724 were considered as 4 highly correlated predictors for modeling based on LASSO-Cox regression. Additionally, we respectively used training dataset and validation dataset for establishment including internal validation and external validation, and the prediction performance of model was measured by concordance index (C-index), integrated discrimination improvement, and net reclassification indices (NRI). Moreover, we found that the model containing clinical characteristics and bio-features presented the best performance by pairwise comparison. Next, the result of sensitivity analysis proved its stability. Then, for preliminarily examination of its discriminative power, all eligible cases were divided into high-risk or low-risk progression by the cut-off value of 57, in the light of predicted nomogram scores. Ultimately, a completed TRIPOD checklist was used for self-assessment of normativity and integrity in modeling. In conclusion, our model might offer crude probability of uncertainly individualized PFS with long-term CHMs therapy in the real-world setting, which could discern the individuals implicated with worse prognosis from the better ones. Nevertheless, our findings were prone to unmeasured bias caused by confounding factors, owing to retrospective cases series.

The aim of this study was to explore risk factors for the prognosis of lung cancer with simple brain metastasis (LCSBM) patients and to establish a prognostic predictive nomogram for LCSBM patients.

Three thousand eight hundred and six cases of LCSBM were extracted from the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015 using SEER Stat 8.3.5. Lung cancer patients only had brain metastasis with no other organ metastasis were defined as LCSBM patients. Prognostic factors of LCSBM were analyzed with log-rank method and Cox proportional hazards model. Independent risk and protective prognostic factors were used to construct nomogram with accelerated failure time model. C-index was used to evaluate the prediction effect of nomogram.

The younger patients (18–65 years old) accounted for 54.41%, while patients aged over 65 accounted for 45.59%.The ratio of male: female was 1:1. Lung cancer in the main bronchus, upper lobe, middle lobe and lower lobe were accounted for 4.91%, 62.80%, 4.47% and 27.82% respectively; and adenocarcinoma accounted for 57.83% of all lung cancer types. The overall median survival time was 12.2 months. Survival rates for 1-, 3- and 5-years were 28.2%, 8.7% and 4.7% respectively. We found female (HR = 0.81, 95% CI 0.75–0.87), the married (HR = 0.80; 95% CI 0.75–0.86), the White (HR = 0.90, 95% CI 0.84–0.95) and primary site (HR = 0.45, 95% CI 0.39–0.52) were independent protective factors while higher age (HR = 1.51, 95% CI 1.40–1.62), advanced grade (HR = 1.19, 95% CI 1.12–1.25) and advanced T stage (HR = 1.09, 95% CI 1.05–1.13) were independent risk prognostic factors affecting the survival of LCSBM patients. We constructed the nomogram with above independent factors, and the C-index value was 0.634 (95% CI 0.622–0.646). We developed a nomogram with seven significant LCSBM independent prognostic factors to provide prognosis prediction.

• Lung cancer with simple brain metastasis
• Nomogram
• Prognostic analysis
• SEER database

• M1 macrophage
• epithelial ovarian cancer
• microRNA
• plasma cells
• recurrence risk
• tumour-infiltrating lymphocytes

Current international prognostic index is widely questioned on the risk stratification of peripheral T‐cell lymphoma and does not accurately predict the outcome for patients. We postulated that multiple mRNAs could combine into a model to improve risk stratification and helping clinicians make treatment decisions. In this study, the gene expression profiles were downloaded from the Gene Expression Omnibus (GEO) database. Weighted gene co‐expression network analysis (WGCNA) was used to screening genes in selected module which most closely related to PTCLs, and then built a mRNA signature using a LASSO Cox regression model and validated the prognostic accuracy of it. Finally, a nomogram was constructed and the performance was assessed. A total of 799 WGCNA‐selected mRNAs in black module were identified, and a mRNA signature which based on DOCK2, GSTM1, H2AFY, KCNAB2, LAPTM5 and SYK for PTCLs was developed. Significantly statistical difference can be seen in overall survival of PTCLs between low‐risk group and high‐risk group (training set:hazard ratio [HR] 4.3, 95% CI 2.4‐7.4, P < .0001; internal testing set:hazard ratio [HR] 2.4, 95% CI 1.2‐4.8, P < .01; external testing set:hazard ratio [HR] 2.3, 95% CI 1.10‐4.7, P = .02). Furthermore, multivariate regression demonstrated that the signature was an independently prognostic factor. Moreover, the nomogram which combined the mRNA signature and multiple clinical factors suggesting that predicted survival probability agreed well with the actual survival probability. The signature is a reliable prognostic tool for patients with PTCLs, and it has the potential for clinicians to implement personalized therapeutic regimen for patients with PTCLs.

• Prognostic
• mRNA
• peripheral T-cell lymphomas
• predictive
• signature

• ANLN
• HIST1H1C
• overall survival
• pancreatic cancer
• prognosis

Although genes have been previously detected in pancreatic cancer (PC), aberrant genes that play roles in resectable pancreatic cancer should be further assessed.

Messenger RNA samples and clinicopathological data corrected with PC were downloaded from The Cancer Genome Atlas (TCGA). Resectable PC patients were randomly divided into a primary set and a validation set. Univariable Cox regression analysis, lasso-penalized Cox regression analysis, and multivariable Cox analysis were implemented to distinguish survival-related genes (SRGs). A risk score based on the SRGs was calculated by univariable Cox regression analysis. A genomic-clinical nomogram was established by integrating the risk score and clinicopathological data to predict overall survival (OS) in resectable PC.

Five survival-related genes (AADAC, DEF8, HIST1H1C, MET, and CHFR) were significantly correlated with OS in resectable PC. The resectable PC patients, based on risk score, were sorted into a high-risk group that showed considerably unfavorable OS (p < 0.001) than the low-risk group, in both the primary set and the validation set. The concordance index (C-index) was calculated to evaluate the predictive performance of the nomogram were respectively in the primary set [0.696 (0.608–0.784)] and the validation set [0.682 (0.606–0.758)]. Additionally, gene set enrichment Analysis discovered several meaningful enriched pathways.

Our study identified five prognostic gene biomarkers for OS prediction and which facilitate postoperative molecular target therapy for the resectable PC, especially the nomic-clinical nomogram which may be used as an effective model for the postoperative OS evaluation and also an optimal therapeutic tool for the resectable PC.

• Biomarkers
• Nomogram
• Pancreatic cancer
• Prognostic model
• Survival
• TCGA

• bronchial carcinoid tumor
• seer
• outcomes
• nomogram

• Age Factors
• Aged
• Bone Neoplasms/secondary
• Carcinoid Tumor/mortality
• Carcinoid Tumor/secondary
• Female
• Forecasting/methods
• Humans
• Lung Neoplasms/mortality
• Lung Neoplasms/pathology
• Male
• Middle Aged
• Neoplasm Staging
• Nomograms
• Probability
• Prognosis
• Proportional Hazards Models
• ROC Curve
• Retrospective Studies
• SEER Program
• Sex Factors
• Survival Rate
• United States/epidemiology

Hepatocellular carcinoma (HCC) represents the fifth most common cause of cancer-associated mortality in men, and the seventh in women, worldwide. The aim of the present study was to identify a reliable and robust RNA-based risk score for the survival prediction of patients with hepatocellular carcinoma (HCC). Gene expression data from HCC and healthy control samples were obtained from The Cancer Genome Atlas to screen differentially expressed mRNAs and long non-coding RNAs (lncRNAs). Univariate and multivariate Cox proportional-hazards regression models and the LASSO algorithm for the Cox proportional-hazards model (LASSO Cox-PH model) were used to identify the prognostic mRNAs and lncRNAs among differentially expressed mRNAs (DEMs) and differentially expressed lncRNAs (DELs), respectively. Prognostic risk scores were generated based on the expression level or status of the prognostic lncRNAs and mRNAs, and the predictive abilities of these RNAs in TCGA and validation datasets were compared. Functional enrichment analyses were also performed. The results revealed a total of 154 downregulated and 625 upregulated mRNAs and 18 upregulated lncRNAs between tumor and control samples in TCGA dataset. A three-mRNA and a five-lncRNA expression signatures were identified using the LASSO Cox-PH model. Three-mRNA and five-lncRNA expression and status risk scores were generated. Using likelihood ratio P-values and area under the curve values from TCGA and the validation datasets, the three-mRNA status risk score was more accurate compared with the other risk scores in predicting the mortality of patients with HCC. The three identified mRNAs, including hepatitis A virus cellular receptor 1, MYCN proto-oncogene BHLH transcription factor and stratifin, were associated with the cell cycle and oocyte maturation pathways. Therefore, a three-mRNA status risk score may be valuable and robust for risk stratification of patients with HCC. The three-mRNA status risk score exhibited greater prognostic value compared with the lncRNA-based risk score.

• hepatocellular carcinoma
• long non-coding RNA
• mRNA
• pathway
• risk score

The role of immune parameters in the prognosis of lung cancer has attracted more and more attention. However, studies of the association between immune scores and prognosis of lung cancer are scarce. The goal of our research was to investigate the correlation between immune scores and overall survival (OS) of early-stage non-small cell lung cancer (NSCLC).

All data regarding patient immune and stromal scores, clinicopathological features, and survival was obtained from the TCGA datasets. Univariable and multivariable Cox regression analyses were utilized to recognize risk factors associated with OS. Afterward, a prognostic nomogram was constructed for predicting 3- and 5-year OS of stage I and II NSCLC patients. Calibration curves and receiver operating characteristic (ROC) were performed to assess the predictive accuracy of the nomogram. Kaplan-Meier methodology was also applied for the survival analysis.

In total, 764 NSCLC (stage I–II) patients were analyzed, and all patients were classified into 3 groups based on immune scores. Results showed that patients with medium-immune scores had significantly worse OS (hazard ratio=1.73, 95% confidence interval: 1.22–2.46) compared with those with low- and high immune scores. Area under the ROC curves (AUC) values for 3- and 5-year OS were 0.65 and 0.64, respectively. Calibration plots demonstrated good consistency in the probability of OS between nomogram predictions and actual observations.

Medium-immune scores are correlated with unsatisfactory prognosis in NSCLC (stage I–II) patients. In addition, the prognostic nomogram may be helpful in predicting OS for stage I and II NSCLC patients.

• cognitive ageing
• predictive markers

• Aged
• Aged, 80 and over
• Alzheimer Disease/cerebrospinal fluid
• Alzheimer Disease/diagnostic imaging
• Amyloid beta-Peptides/cerebrospinal fluid
• Cognitive Dysfunction/cerebrospinal fluid
• Cognitive Dysfunction/diagnostic imaging
• Cohort Studies
• Disease Progression
• Female
• Follow-Up Studies
• Humans
• Male
• Middle Aged
• Models, Theoretical
• Nomograms
• Predictive Value of Tests

• National Natural Science Foundation of China (National Science Foundation of China)

• Prognostic index
• Prognostic risk prediction
• Resected non-small cell lung cancer
• Software
• Survival prediction

• Adjuvant chemotherapy
• Competing risks analysis
• Lung cancer–specific death
• Recurrence
• Sublobar resection

• Adenocarcinoma of Lung/surgery
• Cohort Studies
• Female
• Humans
• Lung Neoplasms/surgery
• Male
• Neoplasm Recurrence, Local
• Neoplasm Staging
• Pneumonectomy/methods
• Retrospective Studies
• Risk
• Treatment Outcome

• P30 CA008748 NCI NIH HHS
• R01 CA217169 NCI NIH HHS
• R01 CA236615 NCI NIH HHS

• GEO
• TCGA
• nomogram
• risk score

The current TNM staging system is far from perfect in predicting the survival of individual non-small cell lung cancer (NSCLC) patients. In this study, we aim to combine clinical variables and molecular biomarkers to develop a prognostic model for patients with NSCLC.

Candidate molecular biomarkers were extracted from the Gene Expression Omnibus (GEO), and Cox regression analysis was performed to determine significant prognostic factors. The survival prediction model was constructed based on multivariable Cox regression analysis in a cohort of 152 NSCLC patients. The predictive performance of the model was assessed by the Area under the Receiver Operating Characteristic Curve (AUC) and Kaplan–Meier survival analysis.

The survival prediction model consisting of two genes (TPX2 and MMP12) and two clinicopathological factors (tumor stage and grade) was developed. The patients could be divided into either high-risk group or low-risk group. Both disease-free survival and overall survival were significantly different among the diverse groups (P < 0.05). The AUC of the prognostic model was higher than that of the TNM staging system for predicting survival.

We developed a novel prognostic model which can accurately predict outcomes for patients with NSCLC after surgery.

The online version of this article (10.1186/s12885-018-4881-9) contains supplementary material, which is available to authorized users.

• MMP12
• Non-small cell lung cancer
• Survival
• TPX2