Text - It's important to step on a bit and pick the data.

This is an article published in Journal of Philly in May.the main point of this paper is that researchers have constructed a feature which can predict the risk of recurrence of gastric cancer in stage II and stage III by means of absolute contraction and selection operator and Cox regression model, which can be used as a powerful tool for prognosis evaluation and help clinicians identify high-risk patients.a 16 ‐ mRNA signature optimizes recurrence ‐ free survivalprediction of stages II and III gastric cancer 16 mRNA features can effectively predict the recurrence free survival of gastric cancer in stage II and III. Abstract: gastric cancer is one of the most common malignant tumors in the world. Despite the continuous improvement of treatment, the mortality rate is still in the forefront of the world.in clinical diagnosis, patients with the same TNM stage (tumor, lymph node, metastasis) may have different survival conditions based on different tumor molecular characteristics.therefore, there is an urgent need for effective methods to assess the risk of postoperative recurrence.based on this, the researchers developed a tool to effectively predict the risk of second and third stage recurrence of gastric cancer in three clinical patient data sets through absolute contraction and selection operator method and Cox regression model, which will effectively improve the level of clinical diagnosis and prognosis evaluation.II. Materials and methods: 1. Gene expression data of gastric cancer in this study were all from geo (and TCGA ().2. 16 mRNA feature mining and validation: 680 prognostic related genes were screened from the experimental data set gse62254 by using R language package "WGCNA" (weighted correlation network analysis), and lasso regression model was used to analyze the 16mrna characteristics through "glmnet" package of R software.3. Establishment and evaluation of nomograph: nomograph and calibration map were constructed by "RMS" R package, and ROC curve was constructed through "proc" R package to detect the accuracy of nomogram. Data analysis: t test, ROC analysis, gsva, PEC, univariate and multivariate Cox regression analysis were used.3. Results 1. Cox regression analysis was performed on the experimental data set gse62254, and 680 genes related to RFs (relapse ‐ free survival, RFS) were identified.then, the least absolute contraction and selection operator lassocox regression analysis (fig1a, b) was used for these genes.Figure 1. Lassocox regression analysis showed that 680 RFs genes related to the second and third phases of gastric cancer, and the median risk score of Kaplan Meier survival curve was the critical value. The patients were divided into low-risk group or high-risk group. The results showed that the prognosis of high-risk group was worse than that of low-risk group. This conclusion was also verified in two independent data sets gse26253 and TCGA.Figure 2. The median risk score of Kaplan Meier survival curve was the critical value, and patients were divided into low-risk group or high-risk group.3. Taking risk score, tumor stage, age and gender as covariates, univariate and multivariate Cox regression analysis showed that risk score was an independent risk factor for stage II and III GCs.and the researchers concluded that risk scores in all three data sets were significantly correlated with RFs.Fig. 3 Univariate and multivariate Cox regression analysis was performed on the risk score, age, gender and tumor stage of the three data sets. 4. In order to make the analysis results more applicable to clinical practice, a nomograph (fig4a) integrating 16 mRNA markers, tumor stage, Lauren classification, lymph node ratio and chemotherapy was constructed in gse62254 data set. Meanwhile, the calibration map was used to verify the agreement The good performance of the nomogram (fig4b) was proved by the ROC curve based on nomogram (fig4c). The AOC curve analysis showed that the prediction of 5-year recurrence rate by nomography was more conducive to the design of clinical treatment scheme (fig4d).Figure 4. Nomograms for predicting recurrence risk in nomographic data sets.5. In order to find the gene set related to 16 mRNA characteristics, gsva analysis was conducted on the data set gse62254. It was found that there were many genes rich in metastasis and chemotherapy resistance (fig5a) in the high-risk group, and there was a strong positive correlation between 16 mRNA characteristic markers and these genes (fig5b).Figure 5. Gsva analysis was carried out in the data set gse62254. Conclusion this paper uses the methods of absolute contraction and selection operators and Cox regression model to mine new prognostic indicators of stage II and III gastric cancer.in the processing methods of high-dimensional data, the comprehensive use of this life letter analysis method can be regarded as a magic trick.there is no experiment in this study. There are few graphs, and all of them are public database data sets 😮 Why can you send 5.5? The author thinks that: 1. Step on the spot: the researchers only analyze the samples of stage II and III gastric cancer, not all the samples are included in the analysis; 2. The selection of data sets is good: TCGA is used for analysis in general analysis, and other data sets are used for verification.why is this article reversed? You can see Figure 2, because TCGA p value is relatively insignificant.therefore, it is not as difficult to post articles as you think. Sometimes you need to think carefully, and the articles are very different.if you have a set of data with survival follow-up information, and you just read our article, and want to do analysis, ouch (o ゜゜゜゜゜゜゜゜゜゜゜゜゜゜゜゜゜! Remember to contact us if you have any idea!! Welcome to the Shengxin people's transcription group | methylation 𞓜 re sequencing | single cell 𞓜 single cell | m6A | multi genomics | Cytoscape 𞓜 limma 𞓜 WGCNA 𞓜 water bear legend | Linux electrophoresis 𞓜 PCR 𞓜 sequencing history | nuclear type 𞓜 NIPT 𞓜 basic experimental experiment | gene | 2019-ncov | enrichment analysis | enrichment analysis | joint analysis | microenenvironment | plague plague | microenenvironment 𞓜 Linux electrophoresis | PCR 𞓜 PCR | sequencing brief history of sequencing history 𞓜 summary of ideas, scholars, scientific research, manuscript withdrawal and reading Bo gene