The Nomogram Model Predicting Overall Survival and Guiding Clinical Decision in Patients With Glioblastoma Based on the SEER Database

Prognostic relevance of radiological findings on early postoperative MRI for 187 consecutive glioblastoma patients receiving standard therapy

Several prognostic factors are known to influence survival for patients treated with IDH-wildtype glioblastoma, but unknown factors may remain. We aimed to investigate the prognostic implications of early postoperative MRI findings. A total of 187 glioblastoma patients treated with standard therapy were consecutively included. Patients either underwent a biopsy or surgery followed by an early postoperative MRI. Progression-free survival (PFS) and overall survival (OS) were analysed for known prognostic factors and MRI-derived candidate factors: resection status as defined by the response assessment in neuro-oncology (RANO)-working group (no contrast-enhancing residual tumour, non-measurable contrast-enhancing residual tumour, or measurable contrast-enhancing residual tumour) with biopsy as reference, contrast enhancement patterns (no enhancement, thin linear, thick linear, diffuse, nodular), and the presence of distant tumours. In the multivariate analysis, patients with no contrast-enhancing residual tumour or non-measurable contrast-enhancing residual tumour on the early postoperative MRI displayed a significantly improved progression-free survival compared with patients receiving only a biopsy. Only patients with non-measurable contrast-enhancing residual tumour showed improved overall survival in the multivariate analysis. Contrast enhancement patterns were not associated with survival. The presence of distant tumours was significantly associated with both poor progression-free survival and overall survival and should be considered incorporated into prognostic models.

Survival prediction of glioblastoma patients using modern deep learning and machine learning techniques

In this study, we utilized data from the Surveillance, Epidemiology, and End Results (SEER) database to predict the glioblastoma patients' survival outcomes. To assess dataset skewness and detect feature importance, we applied Pearson's second coefficient test of skewness and the Ordinary Least Squares method, respectively. Using two sampling strategies, holdout and five-fold cross-validation, we developed five machine learning (ML) models alongside a feed-forward deep neural network (DNN) for the multiclass classification and regression prediction of glioblastoma patient survival. After balancing the classification and regression datasets, we obtained 46,340 and 28,573 samples, respectively. Shapley additive explanations (SHAP) were then used to explain the decision-making process of the best model. In both classification and regression tasks, as well as across holdout and cross-validation sampling strategies, the DNN consistently outperformed the ML models. Notably, the accuracy were 90.25% and 90.22% for holdout and five-fold cross-validation, respectively, while the corresponding R2 values were 0.6565 and 0.6622. SHAP analysis revealed the importance of age at diagnosis as the most influential feature in the DNN's survival predictions. These findings suggest that the DNN holds promise as a practical auxiliary tool for clinicians, aiding them in optimal decision-making concerning the treatment and care trajectories for glioblastoma patients.

Development and validation of a nomogram for predicting in-hospital mortality in patients with nonhip femoral fractures

BACKGROUND

The incidence of nonhip femoral fractures is gradually increasing, but few studies have explored the risk factors for in-hospital death in patients with nonhip femoral fractures in the ICU or developed mortality prediction models. Therefore, we chose to study this specific patient group, hoping to help clinicians improve the prognosis of patients.

METHODS

This is a retrospective study based on the data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Least absolute shrinkage and selection operator (LASSO) regression was used to screen risk factors. The receiver operating characteristic (ROC) curve was drawn, and the areas under the curve (AUC), net reclassification index (NRI) and integrated discrimination improvement (IDI) were calculated to evaluate the discrimination of the model. The consistency between the actual probability and the predicted probability was assessed by the calibration curve and Hosmer-Lemeshow goodness of fit test (HL test). Decision curve analysis (DCA) was performed, and the nomogram was compared with the scoring system commonly used in clinical practice to evaluate the clinical net benefit.

RESULTS

The LASSO regression analysis showed that heart rate, temperature, red blood cell distribution width, blood urea nitrogen, Glasgow Coma Scale (GCS), Simplified Acute Physiology Score II (SAPSII), Charlson comorbidity index and cerebrovascular disease were independent risk factors for in-hospital death in patients with nonhip femoral fractures. The AUC, IDI and NRI of our model in the training set and validation set were better than those of the GCS and SAPSII scoring systems. The calibration curve and HL test results showed that our model prediction results were in good agreement with the actual results (P = 0.833 for the HL test of the training set and P = 0.767 for the HL test of the validation set). DCA showed that our model had a better clinical net benefit than the GCS and SAPSII scoring systems.

CONCLUSION

In this study, the independent risk factors for in-hospital death in patients with nonhip femoral fractures were determined, and a prediction model was constructed. The results of this study may help to improve the clinical prognosis of patients with nonhip femoral fractures.

Development and external validation of dual online tools for prognostic assessment in elderly patients with high-grade glioma: a comprehensive study using SEER and Chinese cohorts

Background

Elderly individuals diagnosed with high-grade gliomas frequently experience unfavorable outcomes. We aimed to design two web-based instruments for prognosis to predict overall survival (OS) and cancer-specific survival (CSS), assisting clinical decision-making.

Methods

We scrutinized data from the SEER database on 5,245 elderly patients diagnosed with high-grade glioma between 2000-2020, segmenting them into training (3,672) and validation (1,573) subsets. An additional external validation cohort was obtained from our institution. Prognostic determinants were pinpointed using Cox regression analyses, which facilitated the construction of the nomogram. The nomogram's predictive precision for OS and CSS was gauged using calibration and ROC curves, the C-index, and decision curve analysis (DCA). Based on risk scores, patients were stratified into high or low-risk categories, and survival disparities were explored.

Results

Using multivariate Cox regression, we identified several prognostic factors for overall survival (OS) and cancer-specific survival (CSS) in elderly patients with high-grade gliomas, including age, tumor location, size, surgical technique, and therapies. Two digital nomograms were formulated anchored on these determinants. For OS, the C-index values in the training, internal, and external validation cohorts were 0.734, 0.729, and 0.701, respectively. We also derived AUC values for 3-, 6-, and 12-month periods. For CSS, the C-index values for the training and validation groups were 0.733 and 0.727, with analogous AUC metrics. The efficacy and clinical relevance of the nomograms were corroborated via ROC curves, calibration plots, and DCA for both cohorts.

Conclusion

Our investigation pinpointed pivotal risk factors in elderly glioma patients, leading to the development of an instrumental prognostic nomogram for OS and CSS. This instrument offers invaluable insights to optimize treatment strategies.

Prognostic nomogram for glioblastoma (GBM) patients presenting with distant extension: a seer-based study

BACKGROUND

Glioblastoma (GBM) with distant extension is rarely reported. We retrieved the data of GBM patients from the SEER database to identify the prognostic factors of GBM with distant extension and constructed a nomogram to predict the overall survival (OS) of these patients.

METHODS

The data of GBM patients between 2003 and 2018 were retrieved from the SEER Database. 181 GBM patients with distant extension were randomly divided into the training cohort (n = 129) and the validation cohort (n = 52) at a ratio of 7:3. The prognostic factors associated with the OS of the GBM patients were identified through univariate and multivariate cox analyses. A nomogram was constructed based on the training cohort to predict OS, and its clinical value was verified using the validation cohort data.

RESULTS

Kaplan-Meier curves showed that the prognosis was significantly worse for GBM patients with distant extension than GBM patients without distant extension. Stage (GBM patients with distant extension) was independent prognostic factor of survival. Multivariate Cox analyses demonstrated that age, surgery, radiotherapy and chemotherapy were independent risk factors for OS of GBM patients presenting with distant extension. The C-indexes of the nomogram for predicting OS were 0.755 (95% CI 0.713-0.797) and 0.757 (95% CI 0.703-0.811) for the training and validation cohorts, respectively. The calibration curves of both cohorts showed good consistency. The area under the curve (AUC) for predicting 0.25-year, 0.5-year and 1-year OS in the training cohort were 0.793, 0.864 and 0.867, respectively, and that in the validation cohort were 0.845, 0.828 and 0.803, respectively. The decision curve analysis (DCA) curves showed that the model to predict the 0.25-year, 0.5-year and 1-year OS probabilities was good.

CONCLUSION

Stage (GBM patients with distant extension) is independent prognostic factor for GBM patients. Age, surgery, radiotherapy and chemotherapy are independent prognostic factors for GBM patients presenting with distant extension, and the nomogram based on these factors can accurately predict the 0.25-year, 0.5-year and 1-year OS of these patients.

Clinical Characteristics and Survival Analysis of Patients With Second Primary Malignancies After Hepatocellular Carcinoma Liver Transplantation: A SEER-based Analysis

BACKGROUND

Second primary malignancies (SPMs) after liver transplantation (LT) are becoming the leading causes of death in LT recipients. The purpose of this study was to explore prognostic factors for SPMs and to establish an overall survival nomogram.

METHODS

A retrospective analysis was conducted of data from the Surveillance, Epidemiology, and End Results (SEER) database on adult patients with primary hepatocellular carcinoma who had undergone LT between 2004 and 2015. Cox regression analysis was used to explore the independent prognostic factors for SPMs. Nomogram was constructed using R software to predict the overall survival at 2, 3, and 5 years. The concordance index, calibration curves, and decision curve analysis were used to evaluate the clinical prediction model.

RESULTS

Data from a total of 2078 patients were eligible, of whom 221 (10.64%) developed SPMs. A total of 221 patients were split into a training cohort (n=154) or a validation cohort (n=67) with a 7:3 ratio. The 3 most common SPMs were lung cancer, prostate cancer, and non-Hodgkin lymphoma. Age at initial diagnosis, marital status, year of diagnosis, T stage, and latency were the prognostic factors for SPMs. The C-index of the nomogram for overall survival in the training and validation cohorts were 0.713 and 0.729, respectively.

CONCLUSIONS

We analyzed the clinical characteristics of SPMs and developed a precise prediction nomogram, with a good predictive performance. The nomogram we developed may help clinicians provide personalized decisions and clinical treatment for LT recipients.

Prognostic model and immunotherapy prediction based on molecular chaperone-related lncRNAs in lung adenocarcinoma

Introduction: Molecular chaperones and long non-coding RNAs (lncRNAs) have been confirmed to be closely related to the occurrence and development of tumors, especially lung cancer. Our study aimed to construct a kind of molecular chaperone-related long non-coding RNAs (MCRLncs) marker to accurately predict the prognosis of lung adenocarcinoma (LUAD) patients and find new immunotherapy targets.

Methods: In this study, we acquired molecular chaperone genes from two databases, Genecards and molecular signatures database (MsigDB). And then, we downloaded transcriptome data, clinical data, and mutation information of LUAD patients through the Cancer Genome Atlas (TCGA). MCRLncs were determined by Spearman correlation analysis. We used univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis to construct risk models. Kaplan-meier (KM) analysis was used to understand the difference in survival between high and low-risk groups. Nomogram, calibration curve, concordance index (C-index) curve, and receiver operating characteristic (ROC) curve were used to evaluate the accuracy of the risk model prediction. In addition, we used gene ontology (GO) enrichment analysis and kyoto encyclopedia of genes and genomes (KEGG) enrichment analyses to explore the potential biological functions of MCRLncs. Immune microenvironmental landscapes were constructed by using single-sample gene set enrichment analysis (ssGSEA), tumor immune dysfunction and exclusion (TIDE) algorithm, “pRRophetic” R package, and “IMvigor210” dataset. The stem cell index based on mRNAsi expression was used to further evaluate the patient’s prognosis.

Results: Sixteen MCRLncs were identified as independent prognostic indicators in patients with LUAD. Patients in the high-risk group had significantly worse overall survival (OS). ROC curve suggested that the prognostic features of MCRLncs had a good predictive ability for OS. Immune system activation was more pronounced in the high-risk group. Prognostic features of the high-risk group were strongly associated with exclusion and cancer-associated fibroblasts (CAF). According to this prognostic model, a total of 15 potential chemotherapeutic agents were screened for the treatment of LUAD. Immunotherapy analysis showed that the selected chemotherapeutic drugs had potential application value. Stem cell index mRNAsi correlates with prognosis in patients with LUAD.

Conclusion: Our study established a kind of novel MCRLncs marker that can effectively predict OS in LUAD patients and provided a new model for the application of immunotherapy in clinical practice.

Development and validation of lymph node ratio-based nomograms for primary duodenal adenocarcinoma after surgery

Background

The prediction models for primary duodenal adenocarcinoma (PDA) are deficient. This study aimed to determine the predictive value of the lymph node ratio (LNR) in PDA patients and to establish and validate nomograms for predicting overall survival (OS) and cancer-specific survival (CSS) for PDAs after surgical resection.

Methods

We extracted the demographics and clinicopathological information of PDA patients between 2004 and 2018 from the Surveillance, Epidemiology and End Results database. After screening cases, we randomly divided the enrolled patients into training and validation groups. X-tile software was used to obtain the best cut-off value for the LNR. Univariate and multivariate Cox analyses were used in the training group to screen out significant variables to develop nomograms. The predictive accuracy of the nomograms was evaluated by the concordance index (C-index), calibration curves, area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA). Finally, four risk groups were created based on quartiles of the model scores.

Results

A total of 978 patients were included in this study. The best cut-off value for the LNR was 0.47. LNR was a negative predictive factor for both OS and CSS. Age, sex, grade, chemotherapy and LNR were used to construct the OS nomogram, while age, grade, chemotherapy, the number of lymph nodes removed and LNR were incorporated into the CSS nomogram. The C-index, calibration curves and AUC of the training and validation sets revealed their good predictability. DCA showed that the predictive value of the nomograms was superior to that of the American Joint Committee on Cancer (AJCC) TNM staging system (8th edition). In addition, risk stratification demonstrated that patients with higher risk correlated with poor survival.

Conclusions

The LNR was an adverse prognostic determinant for PDAs. The nomograms provided an accurate and applicable tool to evaluate the prognosis of PDA patients after surgery.

Nomograms for prognostic risk assessment in glioblastoma multiforme: Applications and limitations

Glioblastoma multiforme (GBM) is the most common and aggressive form of brain cancer. Prognosis evaluation is of great significance in guiding individualized treatment and monitoring of GBM. By integrating different prognostic variables, nomograms simplify the statistical risk prediction model into numerical estimates for death or recurrence, and are hence widely applied in prognosis prediction. In the past two decades, the application of high-throughput profiling technology and the establishment of TCGA database and other public data deposits have provided opportunities to identify cancer-related molecules and prognostic biomarkers. As a result, both molecular features and clinical characteristics of cancer have been reported to be the key factors in nomogram model construction. This article comprehensively reviewed 35 studies of GBM nomograms, analyzed the present situation of GBM nomograms, and discussed the role and significance of nomograms in personalized risk assessment and clinical treatment decision-making. To facilitate the application of nomograms in the prognostic prediction of GBM patients, a website has been established for the online access of nomograms based on the studies of this review, which is called Consensus Nomogram Spectrum for Glioblastoma (CNSgbm) and is accessible through https://bioinfo.henu.edu.cn/nom/NomList.jsp.

Identified Gefitinib Metabolism-Related lncRNAs can be Applied to Predict Prognosis, Tumor Microenvironment, and Drug Sensitivity in Non-Small Cell Lung Cancer

Gefitinib has shown promising efficacy in the treatment of patients with locally advanced or metastatic EGFR-mutated non-small cell lung cancer (NSCLC). Molecular biomarkers for gefitinib metabolism-related lncRNAs have not yet been elucidated. Here, we downloaded relevant genes and matched them to relevant lncRNAs. We then used univariate, LASSO, and multivariate regression to screen for significant genes to construct prognostic models. We investigated TME and drug sensitivity by risk score data. All lncRNAs with differential expression were selected for GO/KEGG analysis. Imvigor210 cohort was used to validate the value of the prognostic model. Finally, we performed a stemness indices difference analysis. lncRNA-constructed prognostic models were significant in the high-risk and low-risk subgroups. Immune pathways were identified in both groups at low risk. The higher the risk score the greater the value of exclusion, MDSC, and CAF. PRRophetic algorithm screened a total of 58 compounds. In conclusion, the prognostic model we constructed can accurately predict OS in NSCLC patients. Two groups of low-risk immune pathways are beneficial to patients. Gefitinib metabolism was again validated to be related to cytochrome P450 and lipid metabolism. Finally, drugs that might be used to treat NSCLC patients were screened.

A Multiparametric MRI-Based Radiomics Nomogram for Preoperative Prediction of Survival Stratification in Glioblastoma Patients With Standard Treatment

OBJECTIVE

To construct and validate a radiomics nomogram for preoperative prediction of survival stratification in glioblastoma (GBM) patients with standard treatment according to radiomics features extracted from multiparameter magnetic resonance imaging (MRI), which could facilitate clinical decision-making.

METHODS

A total of 125 eligible GBM patients (53 in the short and 72 in the long survival group, separated by an overall survival of 12 months) were randomly divided into a training cohort (n = 87) and a validation cohort (n = 38). Radiomics features were extracted from the MRI of each patient. The T-test and the least absolute shrinkage and selection operator algorithm (LASSO) were used for feature selection. Next, three feature classifier models were established based on the selected features and evaluated by the area under curve (AUC). A radiomics score (Radscore) was then constructed by these features for each patient. Combined with clinical features, a radiomics nomogram was constructed with independent risk factors selected by the logistic regression model. The performance of the nomogram was assessed by AUC, calibration, discrimination, and clinical usefulness.

RESULTS

There were 5,216 radiomics features extracted from each patient, and 5,060 of them were stable features judged by the intraclass correlation coefficients (ICCs). 21 features were included in the construction of the radiomics score. Of three feature classifier models, support vector machines (SVM) had the best classification effect. The radiomics nomogram was constructed in the training cohort and exhibited promising calibration and discrimination with AUCs of 0.877 and 0.919 in the training and validation cohorts, respectively. The favorable decision curve analysis (DCA) indicated the clinical usefulness of the radiomics nomogram.

CONCLUSIONS

The presented radiomics nomogram, as a non-invasive tool, achieved satisfactory preoperative prediction of the individualized survival stratification of GBM patients.

Prognostic Value of Lymphocyte-C-Reactive Protein Ratio in Patients Undergoing Radical Cystectomy for Bladder Cancer: A Population-Based Study

Purpose

This study aimed to assess the prognostic value of the lymphocyte–C-reactive protein ratio (LCR) in patients with bladder cancer (BCa) who underwent radical cystectomy (RC).

Materials and Methods

BCa patients between 2009 and 2018 were retrieved from our medical center. The predictive value of LCR on survival of BCa patients was evaluated through the Kaplan–Meier survival and receiver operating characteristic (ROC) curves. The multivariate Cox regression results were used for conducting the nomogram, which were further verified by ROC, decision curve analysis (DCA), and calibration curves. Propensity score matching (PSM) was performed to validate our findings.

Results

A total of 201 BCa patients who received RC were included in this study, with 62 (30.8%) patients in the low LCR group and 139 (69.2%) in the high LCR group. Multivariate analysis results revealed that the high LCR group was significantly related to better prognosis and functioned as a prognostic biomarker for overall survival (OS) [hazard ratio (HR) = 0.41, 95% CI, 0.26–0.66; p < 0.001] and disease-free survival (DFS) [HR = 0.40, 95% CI, 0.26–0.66; p < 0.001]. The nomogram processed better predictive capability and accuracy than TNM stage from ROC results (AUC = 0.754 vs. AUC = 0.715), with the confirmation of calibration curves and DCA. The result of PSM confirmed that LCR was significantly correlated with OS and DFS.

Conclusion

Our finding demonstrates that LCR is a novel, convenient, and effective predictor that may provide vital assistance for clinical decision and individualized therapy in BCa patients after RC.

Prognostic Value of the Systemic Inflammatory Response Index in Patients Undergoing Radical Cystectomy for Bladder Cancer: A Population-Based Study

Purpose

The aim of this study was to evaluate the prognostic significance of the systemic inflammatory response index (SIRI) in patients with bladder cancer (BCa) treated with radical cystectomy (RC) and develop a survival predictive model through establishing a nomogram.

Materials and Methods

A total of 203 BCa patients who underwent RC were included in this study. The relationship between the SIRI and overall survival (OS), disease-free survival (DFS), and clinicopathological features were evaluated. Cox regression analysis was performed to investigate the effect of the factors on the OS and DFS. The results were applied in the establishment of a nomogram. Receiver operating characteristic (ROC) curves, decision curve analysis (DCA) curves, and calibration curves were performed to assess the predictive performance and accuracy of the nomogram, respectively.

Results

According to the classification of the SIRI, 81 patients (39.9%) were assigned to SIRI grade 1, 94 patients (46.3%) to SIRI grade 2, and the remaining 28 patients (13.8%) to SIRI grade 3. Multivariate Cox regression revealed that a higher SIRI grade was significantly associated with a poor prognosis and served as an independent prognostic factor for the OS [Grade 2 vs Grade 1, odds ratio = 2.54, 95% confidence interval (CI),1.39–4.64, P = 0.002; Grade 3 vs Grade 1, odds ratio = 4.79, 95%CI: 2.41–9.50, P < 0.001] and DFS [Grade 2 vs Grade 1, odds ratio = 2.19, 95% CI, 1.12–4.31, P = 0.023; Grade 3 vs Grade 2, odds ratio = 3.36, 95%CI, 1.53–7.35, P = 0.002]. The ROC and DCA analysis indicated that the nomogram based on the SIRI contained a better predictive performance compared with the TNM stage (AUC = 0.750 and 0.791; all P < 0.05). The ROC analysis showed that nomograms can better predict the 3- and 5-year OS and DFS. The calibration curves exhibited a significant agreement between the nomogram and the actual observation.

Conclusion

SIRI as a novel independent prognostic index and potential prognostic biomarker can effectively improve the traditional clinicopathological analysis and optimize individualized clinical treatments for BCa patients after RC.

A Prognostic Model for Glioblastoma Patients Treated With Standard Therapy Based on a Prospective Cohort of Consecutive Non-Selected Patients From a Single Institution

Background

Glioblastoma patients administered standard therapies, comprising maximal surgical resection, radiation therapy with concomitant and adjuvant temozolomide, have a variable prognosis with a median overall survival of 15–16 months and a 2-year overall survival of 30%. The aim of this study was to develop a prognostic nomogram for overall survival for glioblastoma patients treated with standard therapy outside clinical trials.

Methods

The study included 680 consecutive, non-selected glioblastoma patients administered standard therapy as primary treatment between the years 2005 and 2016 at Rigshospitalet, Copenhagen, Denmark. The prognostic model was generated employing multivariate Cox regression analysis modeling overall survival.

Results

The following poor prognostic factors were included in the final prognostic model for overall survival: Age (10-year increase: HR = 1.18, 95% CI: 1.08–1.28, p < 0.001), ECOG performance status (PS) 1 vs. 0 (HR = 1.30, 95% CI: 1.07–1.57, p = 0.007), PS 2 vs. 0 (HR = 2.99, 95% CI: 1.99–4.50, p < 0.001), corticosteroid use (HR = 1.42, 95% CI: 1.18–1.70, p < 0.001), multifocal disease (HR = 1.63, 95% CI: 1.25–2.13, p < 0.001), biopsy vs. resection (HR = 1.35, 95% CI: 1.04–1.72, p = 0.02), un-methylated promoter of the MGMT (O⁶-methylguanine-DNA methyltransferase) gene (HR = 1.71, 95% CI: 1.42–2.04, p < 0.001). The model was validated internally and had a concordance index of 0.65.

Conclusion

A nomogram for overall survival was established. This model can be used for risk stratification and treatment planning, as well as improve enrollment criteria for clinical trials.

Imaging genomics for accurate diagnosis and treatment of tumors: A cutting edge overview

Imaging genomics refers to the establishment of the connection between invasive gene expression features and non-invasive imaging features. Tumor imaging genomics can not only understand the macroscopic phenotype of tumor, but also can deeply analyze the cellular and molecular characteristics of tumor tissue. In recent years, tumor imaging genomics has been a key in the field of medicine. The incidence of cancer in China has increased significantly, which is the main reason of disease death of urban residents. With the rapid development of imaging medicine, depending on imaging genomics, many experts have made remarkable achievements in tumor screening and diagnosis, prognosis evaluation, new treatment targets and understanding of tumor biological mechanism. This review analyzes the relationship between tumor radiology and gene expression, which provides a favorable direction for clinical staging, prognosis evaluation and accurate treatment of tumors.

Cancer evolution: A means by which tumors evade treatment

Although various methods have been tried to study and treat cancer, the cancer remains a major challenge for human medicine today. One important reason for this is the presence of cancer evolution. Cancer evolution is a process in which tumor cells adapt to the external environment, which can suppress the human immune system's ability to recognize and attack tumors, and also reduce the reproducibility of cancer research. Among them, heterogeneity of the tumor provides intrinsic motivation for this process. Recently, with the development of related technologies such as liquid biopsy, more and more knowledge about cancer evolution has been gained and interest in this topic has also increased. Therefore, starting from the causes of tumorigenesis, this paper introduces several tumorigenesis processes and pathways, as well as treatment options for different targets.

Tumor-infiltrating lymphocytes: Warriors fight against tumors powerfully

Tumor-infiltrating lymphocytes (TILs) are infiltrating lymphocytes in tumor tissues. After isolation, screening and amplification in vitro, they will be implanted into patients and play a specific killing effect on tumors. Since TILs have not been genetically modified and come from the body of patients, there will be relatively few adverse reactions. This is also the advantage of TIL treatment. In recent years, its curative effect on solid tumors began to show its sharpness. However, due to the limitations of the immune microenvironment and the mutation of antigens, TIL's development was slowed down. This article reviews the research progress, biological characteristics, preparation and methods of enhancing the therapeutic effect of tumor-infiltrating lymphocytes, their roles in different tumors and prognosis, and emphasizes the important value of tumor-infiltrating lymphocytes in anti-tumor.

Gut microecology: Why our microbes could be key to our health

The human body contains a large number of microorganisms, and the gut microecology environment contains the largest number and types of microorganisms. The structure and function of gut microbiota are closely related to the health of the human body. In a cascade of studies, the diversity of gut microbiota and its metabolite often found changed in patients or mice model. What kind of gut microbiota that associated with the occurrence or treatment of diseases were also found in many studies. Gut microbiota and its products can affect the function of the human body. Short-chain fatty acids, bile acid, indoles and so on were found can regulate the inflammation, immune response to affect the process of diseases. Immune cells like natural killer T cells, CD3 + T cells were also found had a link to gut microbiota which associated with diseases. Changes in gut microbiota are associated with changes in the body's major systems, such as the digestive system, the endocrine system, the cardiovascular system, the endocrine and metabolic system, the urinary system diseases, the respiratory system and so on. It is of great significance to study gut microecology for the prevention and treatment of various human diseases.

Principles of RNA methylation and their implications for biology and medicine

RNA methylation is a post-transcriptional level of regulation. At present, more than 150 kinds of RNA modifications have been identified. They are widely distributed in messenger RNA (mRNA), transfer RNA (tRNA), ribosomal RNA (rRNA), noncoding small RNA (sncRNA) and long-chain non-coding RNA (lncRNA). In recent years, with the discovery of RNA methylation related proteins and the development of high-throughput sequencing technology, the mystery of RNA methylation has been gradually revealed, and its biological function and application value have gradually emerged. In this review, a large number of research results of RNA methylation in recent years are collected. Through systematic summary and refinement, this review introduced RNA methylation modification-related proteins and RNA methylation sequencing technologies, as well as the biological functions of RNA methylation, expressions and applications of RNA methylation-related genes in physiological or pathological states such as cancer, immunity and virus infection, and discussed the potential therapeutic strategies.