Clinical Significance of Serum CA125, CA19-9, CA72-4, and Fibrinogen-to-Lymphocyte Ratio in Gastric Cancer With Peritoneal Dissemination

A nomogram for predicting survival based on hemoglobin A1c and circulating tumor cells in advanced gastric cancer patients receiving immunotherapy

BACKGROUND

Our study aimed to investigate the correlation between hemoglobin A1c (HbA1c), circulating tumor cells (CTCs) and prognosis in advanced gastric cancer (GC) patients who received immunotherapy and explore the potential prognostic predictors to develop a nomogram.

METHODS

We retrospectively enrolled 259 patients with advanced GC treated at Beijing Friendship Hospital between September 2014 and March 2024. Patients were divided into the immunochemotherapy cohort (ICT) and the chemotherapy (CT) cohort. Survival rate was calculated by Kaplan-Meier survival curve, and the differences were evaluated by log-rank test. The univariate and multivariate Cox proportional hazards regression model was used to identify factors independently associated with survival. A nomogram was developed to estimate 6-, 12-, and 18-month progression-free survival (PFS) probability based on the ICT cohort.

RESULTS

Patients achieved higher PFS in the ICT cohort than the CT cohort. We focused on the ICT cohort and constructed a nomogram based on the multivariate analysis, including five variables: age, PD-L1 expression, HbA1c, CTCs and CEA*. The concordance index value was 0.82 in the training cohort and 0.75 in the validation cohort. Furthermore, we proved the nomogram was clinically useful and performed better than PD-L1 expression staging system. Notably, we found high HbA1c level but not diabetes mellitus significantly affected the efficacy of ICT.

CONCLUSION

ICT showed better PFS than CT. In addition, HbA1c and CTCs were novel biomarkers to predict PFS in patients treated with ICT. The nomogram could predict PFS of advanced GC patients receiving ICT with increased accuracy and favorable clinical utility.

A new TGF-β risk score predicts clinical and immune landscape in colorectal cancer patients

Background

Aberrant TGF-β signaling pathway can lead to invasive phenotype of colorectal cancer (CRC), resulting in poor prognosis. It is pivotal to develop an effective prognostic factor on the basis of TGF-β-related genes to accurately identify risk of CRC patients.

Methods

We performed differential analysis of TGF-β-related genes in CRC patients from databases and previous literature to obtain TGF-β-related differentially expressed genes (TRDEGs). LASSO-Cox regression was utilized to build a CRC prognostic feature model based on TRDEGs. The model was validated using two GEO validation sets. Wilcoxon rank-sum test was utilized to test correlation of model with clinical factors. ESTIMATE algorithm and ssGSEA and tumor mutation burden (TMB) analysis were used to analyze immune landscape and mutation burden of high-risk (HR) and low-risk (LR) groups. CellMiner database was utilized to identify therapeutic drugs with high sensitivity to the feature genes.

Results

We established a six-gene risk prognostic model with good predictive accuracy, which independently predicted CRC patients' prognoses. The HR group was more likely to experience immunotherapy benefits due to higher immune infiltration and TMB. The feature gene TGFB2 could inhibit the efficacy of drugs such as XAV-939, Staurosporine, and Dasatinib, but promote the efficacy of drugs such as CUDC-305 and by-product of CUDC-305. Similarly, RBL1 could inhibit the drug action of Fluphenazine and Imiquimod but promote that of Irofulven.

Conclusion

A CRC risk prognostic signature was developed on basis of TGF-β-related genes, which provides a reference for risk and further therapeutic selection of CRC patients.

Prognostic significance of copper metabolism-related genes as risk markers in bladder urothelial carcinoma

Bladder urothelial carcinoma (BLCA), a prevalent malignant neoplasm affecting the human urinary system, is frequently linked with an unfavorable prognosis in a significant proportion of individuals. More effective and sensitive markers are needed to provide a reference for prognostic judgment. We obtained RNA sequencing data and clinical information of individuals from TCGA, and 133 copper metabolism-related genes from literature. Prognostic genes were evaluated by univariate/multivariate Cox regression analysis and LASSO analysis, and a risk-scoring model was established and validated in the GEO dataset. The CIBERSORT method was utilized to explore immune cell infiltration in BLCA individuals. In addition, tumor immune dysfunction and exclusion (TIDE) and immunophenoscore (IPS) were utilized to verify whether the model can foretell the response of BLCA individuals to immunotherapy. We successfully constructed an 8-gene risk scoring model to foretell individuals' overall survival, and the model performed well in TCGA training and GEO validation cohorts. Lastly, a nomogram containing clinical parameters and risk scores was constructed to help individualized result prediction for individuals. Calibration curves demonstrated a high degree of concordance between the observed and projected survival durations, attesting to its exceptional predictive accuracy. Analysis utilizing CIBERSORT unveiled elevated levels of immune cell infiltration in individuals classified as low risk. TIDE and IPS analyses substantiated that low-risk individuals exhibited a more favorable response to immunotherapy. In summary, the model held immense potential for stratifying the risk of survival and guiding tailored treatment approaches for individuals with BLCA, thereby offering valuable insights for personalized therapeutic interventions.

Molecular subtype identification and prognosis stratification by a immunogenic cell death-related gene expression signature in colorectal cancer

OBJECTIVES

This study intended to develop a new immunogenic cell death (ICD)-related prognostic signature for colorectal cancer (CRC) patients.

RESEARCH DESIGN AND METHODS

The Non-Negative Matrix Factorization (NMF) algorithm was adopted to cluster tumor samples based on ICD gene expression to obtain ICD-related subtypes. Survival analysis and immune microenvironment analysis were conducted among different subtypes. Regression analysis was used to construct the model. Based on riskscore median, cancer patients were classified into high and low risk groups, and independent prognostic ability of the model was analyzed. The CIBERSORT algorithm was adopted to determine the immune infiltration level of both groups.

RESULTS

We analyzed the differential genes between cluster 4 and cluster 1-3 and obtained 12 genes with the best prognostic features finally (NLGN1, SLC30A3, C3orf20, ADAD2, ATOH1, ATP6V1B1, KCNQ2, MUCL3, RGCC, CLEC17A, COL6A5, and INSL4). In addition, patients with lower risk had higher levels of infiltration of most immune cells, lower Tumor Immune Dysfunction and Exclusion (TIDE) level and higher immunophenscore (IPS) level than those with higher risk.

CONCLUSIONS

This study constructed and validated the ICD feature signature predicting CRC prognosis and provide a reference criteria for guiding the prognosis and immunotherapy of CRC cancer patients.

Construction and Validation of a Prognostic Model Based on Mitochondrial Genes in Prostate Cancer

This study attempted to build a prostate cancer (PC) prognostic risk model with mitochondrial feature genes. PC-related MTGs were screened for Cox regression analyses, followed by establishing a prognostic model. Model validity was analyzed via survival analysis and receiver operating characteristic (ROC) curves, and model accuracy was validated in the GEO dataset. Combining risk score with clinical factors, the independence of the risk score was verified by using Cox analysis, followed by generating a nomogram. The Gleason score, microsatellite instability (MSI), immune microenvironment, and tumor mutation burden were analyzed in two risk groups. Finally, the prognostic feature genes were verified through a q-PCR test. Ten PC-associated MTGs were screened, and a prognostic model was built. Survival analysis and ROC curves illustrated that the model was a good predictor for the risk of PC. Cox regression analysis revealed that risk score acted as an independent prognostic factor. The Gleason score and MSI in the high-risk group were substantially higher than in the low-risk group. Levels of ESTIMATE Score, Immune Score, Stromal Score, immune cells, immune function, immune checkpoint, and immunopheno score of partial immune checkpoints in the high-risk group were significantly lower than in the low-risk group. Genes with the highest mutation frequencies in the two groups were SPOP, TTN, and TP53. The q-PCR results of the feature genes were consistent with the gene expression results in the database. The 10-gene model based on MTGs could accurately predict the prognosis of PC patients and their responses to immunotherapy.

Construction of a prognostic model for breast cancer based on moonlighting genes

Breast cancer (BRCA) is a highly heterogeneous disease, with significant differences in prognosis among patients. Existing biomarkers and prognostic models have limited ability to predict BRCA prognosis. Moonlighting genes regulate tumor progression and are associated with cancer prognosis. This study aimed to construct a moonlighting gene-based prognostic model for BRCA. We obtained differentially expressed genes (DEGs) in BRCA from The Cancer Genome Atlas and intersected them with moonlighting genes from MoonProt to acquire differential moonlighting genes. GO and KEGG results showed main enrichment of these genes in the response of BRCA cells to environmental stimuli and pentose phosphate pathway. Based on moonlighting genes, we conducted drug prediction and validated results through cellular experiments. After ABCB1 knockdown, viability and proliferation of BRCA cells were significantly enhanced. Based on differential moonlighting genes, BRCA was divided into three subgroups, among which cluster2 had the highest survival rate and immunophenoscore and relatively low tumor mutation burden. TP53 had the highest mutation frequency in cluster2 and cluster3, while PIK3CA had a higher mutation frequency in cluster1, with the majority being missense mutations. Subsequently, we established an 11-gene prognostic model in the training set based on DEGs among subgroups using univariate Cox regression, LASSO regression, and multivariable Cox regression analyses. Model prognostic performance was verified in GEO, METABRIC and ICGC validation sets. In summary, this study obtained three BRCA moonlighting gene-related subtypes and constructed an 11-gene prognostic model. The 11-gene BRCA prognostic model has good predictive performance, guiding BRCA prognosis for clinical doctors.

Enhancing gastric cancer early detection: A multi-verse optimized feature selection model with crossover-information feedback

Gastric cancer (GC), an acknowledged malignant neoplasm, threatens life and digestive system functionality if not detected and addressed promptly in its nascent stages. The indispensability of early detection for GC to augment treatment efficacy and survival prospects forms the crux of this investigation. Our study introduces an innovative wrapper-based feature selection methodology, referred to as bCIFMVO-FKNN-FS, which integrates a crossover-information feedback multi-verse optimizer (CIFMVO) with the fuzzy k-nearest neighbors (FKNN) classifier. The primary goal of this initiative is to develop an advanced screening model designed to accelerate the identification of patients with early-stage GC. Initially, the capability of CIFMVO is validated through its application to the IEEE CEC benchmark functions, during which its optimization efficiency is measured against eleven cutting-edge algorithms across various dimensionalities-10, 30, 50, and 100. Subsequent application of the bCIFMVO-FKNN-FS model to the clinical data of 1632 individuals from Wenzhou Central Hospital-diagnosed with either early-stage GC or chronic gastritis-demonstrates the model's formidable predictive accuracy (83.395%) and sensitivity (87.538%). Concurrently, this investigation delineates age, gender, serum gastrin-17, serum pepsinogen I, and the serum pepsinogen I to serum pepsinogen II ratio as parameters significantly associated with early-stage GC. These insights not only validate the efficacy of our proposed model in the early screening of GC but also contribute substantively to the corpus of knowledge facilitating early diagnosis.

A prognostic risk model based on lactate metabolism and transport-related lncRNAs for gastric adenocarcinoma

BACKGROUND

Increased lactate levels and metastasis in tumours are strongly associated with dismal outcomes. But prognostic value of lactate metabolism and transport-related lncRNAs in gastric adenocarcinoma (GA) patients remains unaddressed.

METHODS

Gene expression data of GA were provided by The Cancer Genome Atlas. Lactate metabolism and transport-related gene data were accessed from GSEA. LncRNAs related to lactate metabolism and transport were identified by correlation analysis. A prognostic model was built by regression analysis. Validity of prognostic model was confirmed through survival analysis and receiver operating characteristic (ROC) curve. Immunity of each risk group was evaluated by immune correlation analysis .LncRNA-mRNA network was built by correlation analysis using Cytoscape software.

RESULTS

A 12-gene prognostic model based on lactate metabolism and transport-related lncRNAs was built in GA. Median riskscore was utilized to classify GA samples into high- and low-risk groups. Survival analysis and ROC curves demonstrated validity of prognostic model. Most immune checkpoint molecules and TIDE scores were lower in the low-risk group. LINC01303 and LINC01545 may be the key prognostic factors in patients with GA.

CONCLUSION

This study successfully built a prognostic model of lactate metabolism and transport-related lncRNAs in GA. The findings guide prognostic management of GA patients.

Prognostic Impact of Perioperative CA125 Status in Gastric Cancer Based on New Cutoff Values

Objectives The current carbohydrate antigen 125 (CA125) cutoff value demonstrated high specificity but low sensitivity. Therefore, we used new cutoff values to evaluate the clinical impact of perioperative CA125 in gastric cancer. Methods This study retrospectively analyzed 525 patients with gastric cancer (349 males and 176 females), of whom 445 patients underwent R0 resection and 80 patients underwent R1/R2 resection between 2011 and 2020. The receiver operating characteristic curve indicated preoperative and postoperative cutoff CA125 values of 15.7 IU/mL and 17.3 IU/mL, respectively, to predict overall survival. Furthermore, we analyzed changes in postoperative CA125 levels and evaluated their prognostic impact using multivariate analysis. Results The preoperative CA125-positive rate was 25%. Males, advanced TNM factors, and noncurative resection cases demonstrated significantly higher positive rates than the other group. The preoperative CA125-positive group exhibited a significantly higher noncurative resection rate than the preoperative CA125-negative group (32% versus 10%, P < 0.01). Preoperatively, CA125-positive status was an independent poor prognostic factor (P < 0.01), and at three months postoperatively, it tended to be a poor prognostic factor. Conclusions High preoperative CA125 (>15.7 IU/mL) was a significant predictor for noncurative resection and poor overall prognosis in gastric cancer. Furthermore, postoperative CA125-positive status three months postoperatively was also a potential predictor of recurrence and poor prognosis.

Construction of a prognostic risk model for Stomach adenocarcinoma based on endoplasmic reticulum stress genes

OBJECTIVE

Stomach adenocarcinoma (STAD) is caused by malignant transformation of gastric glandular cells and is characterized by a high incidence rate and a poor prognosis. This study was designed to establish a prognostic risk model for STAD according to endoplasmic reticulum (ER) stress feature genes as cancer cells are susceptible to ER stress.

METHODS

The TCGA-STAD dataset was downloaded to screen differentially expressed genes (DEGs). By intersecting DEGs with ER stress genes retrieved from GeneCards, ER stress-related DEGs in STAD were obtained. Kmeans cluster analysis of STAD subtypes and Single sample gene set enrichment analysis (ssGSEA) analysis of immune infiltration were performed. Cox regression analysis was utilized to construct a risk prognostic model. Samples were split into high-risk and low-risk groups according to the median risk score. Survival analysis and Receiver Operating Characteristic (ROC) curves were conducted to assess the validity of the model. Gene set enrichment analysis (GSEA) was performed to investigate differential pathways in the two risk groups. Cox analysis was performed to verify the independence of the risk model, and a nomogram was generated.

RESULTS

A total of 162 ER stress-related DEGs in STAD were identified by bioinformatics analysis. Kmeans cluster analysis showed that STAD was divided into 3 subgroups. The ssGSEA showed that the levels of immune infiltration in subgroups 2 and 3 were significantly higher than subgroup 1. With 12 prognostic genes (MATN3, ATP2A1, NOX4, AQP11, HP, CAV1, STARD3, FKBP10, EGF, F2, SERPINE1, CNGA3) selected from ER stress-related DEGs using Cox regression analysis, we then constructed a prognostic model. Kaplan-Meier (K‑M) survival curves and ROC curves showed good prediction performance of the model. Significant enrichment of genes in the high-risk group was found in extracellular matrix (ECM) receptor interaction. Cox regression analysis combined with clinical factors showed that the risk model could be used as an independent prognostic factor. The prediction correction curve showed that the good prediction ability of the nomogram.

CONCLUSION

The STAD could be divided into three subgroups, and the 12-gene model constructed by ER stress signatures had a good prognostic performance for STAD patients.

Construction and significance of a breast cancer prognostic model based on cuproptosis-related genotyping and lncRNAs

BACKGROUND

/Purpose: Cuproptosis may play a significant role in breast cancer (BC). We aimed to investigate the prognostic impact of cuproptosis-related lncRNAs in BC.

METHODS

Consensus clustering analysis categorized TCGA-BRCA samples into 3 clusters, followed by survival and immune analyses of the 3 clusters. LASSO-COX analysis was performed on cuproptosis-related lncRNAs differentially expressed in BC to construct a BC prognostic model. Gene Ontology/Kyoto Encyclopedia of Genes and Genomes (GO/KEGG) enrichment, immune, and drug prediction analyses were performed on the high-risk and low-risk groups. Cell experiments were conducted to analyze the results of drug prediction and two cuproptosis-related lncRNAs (AC104211.1 and LINC01863).

RESULTS

Significant differences were observed in survival outcomes and immune infiltration levels among the three clusters (p < 0.05). The validation of the model showed significant differences in survival outcomes between the high-risk and low-risk groups in both the training and validation sets (p < 0.05). Differential mRNAs between the two groups were significantly enriched in the Neuroactive ligand-receptor interaction and cAMP signaling pathway. Additionally, significant differences were found in immune infiltration levels, human leukocyte antigen (HLA) expression, Immunophenoscore (IPS) scores, and Tumor Immune Dysfunction and Exclusion (TIDE) scores between the two groups (p < 0.05). Drug prediction and corresponding cell experimental results showed that Trametinib, 5-fluorouracil, and AICAR significantly inhibited the viability of MCF-7 cells (p < 0.05). AC104211.1 and LINC01863 were found to impact the proliferation of BC cells.

CONCLUSION

The risk-scoring model obtained in this study may serve as a robust prognostic biomarker, potentially aiding in clinical decision-making for BC patients.

Construction of a prognostic model for lung adenocarcinoma based on m6A/m5C/m1A genes

BACKGROUND

Developing a prognostic model for lung adenocarcinoma (LUAD) that utilizes m6A/m5C/m1A genes holds immense importance in providing precise prognosis predictions for individuals.

METHODS

This study mined m6A/m5C/m1A-related differential genes in LUAD based on public databases, identified LUAD tumor subtypes based on these genes, and further built a risk prognostic model grounded in differential genes between subtypes. The immune status between high- and low-risk groups was investigated, and the distribution of feature genes in tumor immune cells was analyzed using single-cell analysis. Based on the expression levels of feature genes, a projection of chemotherapeutic and targeted drugs was made for individuals identified as high-risk. Ultimately, cell experiments were further verified.

RESULTS

The 6-gene risk prognosis model based on differential genes between tumor subtypes had good predictive performance. Individuals classified as low-risk exhibited a higher (P < 0.05) abundance of infiltrating immune cells. Feature genes were mainly distributed in tumor immune cells like CD4+T cells, CD8+T cells, and regulatory T cells. Four drugs with relatively low IC50 values were found in the high-risk group: Elesclomol, Pyrimethamine, Saracatinib, and Temsirolimus. In addition, four drugs with significant positive correlation (P < 0.001) between IC50 values and feature gene expression were found, including Alectinib, Estramustine, Brigatinib, and Elesclomol. The low expression of key gene NTSR1 reduced the IC50 value of irinotecan.

CONCLUSION

Based on the m6A/m5C/m1A-related genes in LUAD, LUAD patients were divided into 2 subtypes, and a m6A/m5C/m1A-related LUAD prognostic model was constructed to provide a reference for the prognosis prediction of LUAD.

Clinical significance of systemic inflammation response index and platelet-lymphocyte ratio in patients with adenocarcinoma of the esophagogastric junction and upper gastric cancer

Background

Tumor immunity plays an important role in assessing the tumor progression. The purpose of this study was to investigate the prognostic value of combined systemic inflammation response index (SIRI) and platelet-lymphocyte ratio (PLR) of gastroesophageal junction cancer (AEG) and upper gastric cancer (UGC) patients.

Methods

In this retrospective study, patients from 2003 to 2014 were divided into training and validation sets. The prognostic accuracy of each variable was compared using time-independent ROC analysis. The scoring system was calculated by cut-off values of SIRI and PLR in 5-year. Kaplan-Meier and Log-rank tests were used to analyze overall survival (OS). Chi-square test was used to analyze the association between clinical characteristics and the scoring system. Univariate and multivariate analyses based on the competitive risk regression model were used to analyze independent predictors of death due to AGC and UGC. R software was used to construct the Nomogram model of risk assessment.

Results

Patients with SIRI-PLR = 2 had worse survival time than those with 0 and 1 (P < 0.001) and more suitable for postoperative adjuvant chemotherapy (P = 0.002). High PLR patients were more suitable for proximal gastrectomy (P = 0.049). SIRI-PLR were independent predictors in training set (P < 0.001), which could be combined with age, pTNM stage and postoperative chemotherapy to construct Nomogram for predicting OS.

Conclusions

Preoperative SIRI-PLR score was an independent predictor for patients with AEG and UGC. The Nomogram model constructed by age, SIRI-PLR, pTNM stage and postoperative chemotherapy can correctly predict the prognosis of patients.

Prediction of prognostic and immune therapy response in lung adenocarcinoma based on MHC-I-related genes

OBJECTIVES

The study investigated the prognostic and immune predictive potential of major histocompatibility complex class I (MHC-I) in lung adenocarcinoma (LUAD).

MATERIALS AND METHODS

With The Cancer Genome Atlas (TCGA)-LUAD and Gene Expression Omnibus datasets (GSE26939, GSE72094) as the training and validation sets, respectively, we used Cox regression analysis to construct a prognostic model, and verified independence of riskscore. The predictive capacity of the model was assessed in both sets using the receiver operating characteristic curve and Kaplan-Meier survival curves. Immune analysis was performed by using ssGSEA. Additionally, immune checkpoint blockade therapy was assessed by using immunophenoscore, Tumor Immune Dysfunction and Exclusion score. Based on the cMAP database, effective small molecule compounds were predicted.

RESULTS

A prognostic model was established based on 8 MHC-I-related genes, and the predictive capacity of the model was accurate. Immune analysis results revealed that patients classified as high-risk had lower levels of immune cell infiltration and impaired immune function. The low-risk group possessed a better response to immune checkpoint blockade therapy. Theobromine and pravastatin were identified as having great potential in improving the prognosis of LUAD.

CONCLUSION

Overall, the study revealed MHC-I-related molecular prognostic biomarkers as robust indicators for LUAD prognosis and immune therapy response.

A Lactic Acid Metabolism-Related Gene Signature for Predicting Clinical Outcome and Tumor Microenvironmental Status in Patients with Hepatocellular Carcinoma

This study aims to build a prognostic model based on lactic acid metabolism-related genes (LMRGs) to predict survival outcomes and tumor microenvironment status of Hepatocellular carcinoma (HCC) patients. The model was used to calculate riskscores of clinical samples. Survival analysis and Cox regression analysis were conducted to verify the independence and reliability of the riskscore to determine its clinical significance in prognosis evaluation of HCC. Additionally, we conducted a comprehensive analysis of tumor mutation burden (TMB), immune cell infiltration, and gene set molecular function in the high- and low-risk groups. We obtained 134 LMRGs mainly involved in cellular calcium homeostasis and calcium signaling pathways. The LMRGs in the risk assessment model included PFKFB4, SLC16A3, ADRA2B, SLC22A1, QRFPR, and PROK1. This study discovered much shorter overall survival and median survival time of patients with higher riskscores when compared to those with lower riskscores. It was indicated that for independent prediction of patients' prognosis, the riskscore had a significant clinical value. A remarkable difference was also found regarding TMB between the two groups. Finally, cell experiments demonstrated that the knockout of PFKFB4 and SLC16A3 genes suppressed lactate. Our research demonstrated that the riskscore, established based on LMRGs, is a promising biomarker.

A Revised Molecular Model of Ovarian Cancer Biomarker CA125 (MUC16) Enabled by Long-read Sequencing

The biomarker CA125, a peptide epitope located in several tandem repeats of the mucin MUC16, is the gold standard for monitoring regression and recurrence of high-grade serous ovarian cancer in response to therapy. However, the CA125 epitope along with several structural features of the MUC16 molecule are ill defined. One central aspect still unresolved is the number of tandem repeats in MUC16 and how many of these repeats contain the CA125 epitope. Studies from the early 2000s assembled short DNA reads to estimate that MUC16 contained 63 repeats.Here, we conduct Nanopore long-read sequencing of MUC16 transcripts from three primary ovarian tumors and established cell lines (OVCAR3, OVCAR5, and Kuramochi) for a more exhaustive and accurate estimation and sequencing of the MUC16 tandem repeats.The consensus sequence derived from these six sources was confirmed by proteomics validation and agrees with recent additions to the NCBI database. We propose a model of MUC16 containing 19-not 63-tandem repeats. In addition, we predict the structure of the tandem repeat domain using the deep learning algorithm, AlphaFold.The predicted structure displays an SEA domain and unstructured linker region rich in proline, serine, and threonine residues in all 19 tandem repeats. These studies now pave the way for a detailed characterization of the CA125 epitope. Sequencing and modeling of the MUC16 tandem repeats along with their glycoproteomic characterization, currently underway in our laboratories, will help identify novel epitopes in the MUC16 molecule that improve on the sensitivity and clinical utility of the current CA125 assay.

SIGNIFICANCE

Despite its crucial role in clinical management of ovarian cancer, the exact molecular sequence and structure of the biomarker, CA125, are not defined. Here, we combine long-read sequencing, mass spectrometry, and in silico modeling to provide the foundational dataset for a more complete characterization of the CA125 epitope.

Development of lymph node metastasis-related prognostic markers in breast cancer

BACKGROUND

Lymph node metastasis (LNM) from Breast cancer (BC) is commonly seen in BC progression. Currently, the identification of genes linked with LNM in BC remains in mystery.

METHODS

Genes related to BC LNM were screened, and a risk model was constructed based on LASSO-Cox analysis. Combined with the Kaplan-Meier curve, the ability of riskscore to distinguish different baseline characteristics was evaluated, and model was verified by the receiver operating characteristic (ROC) curve. The expression levels of prognostic marker genes were analyzed by qRT-PCR and western blot (WB).

RESULTS

A higher survival rate and longer survival time in low-risk BC patients. The 1, 3 and 5 year AUC values of the training set were 0.79, 0.74, and 0.73, respectively. Results for the validation set was similar to the training set. The differentially expressed genes between the high- and low-risk groups were significantly enriched in immune pathways. In addition, the low-risk group had higher levels of immune infiltration. qRT-PCR and WB results showed that in BC, CDH10, SMR3A, POU3F2, and FABP7 were down-regulated, and LHX1 was up-regulated.

CONCLUSIONS

We built a prognostic model of BC based on LNM-related genes, proffering evaluation for prognosis and precise cure of BC.

SIGNIFICANCE

At present, the genes related to lymph node metastasis in BC are still largely unknown and need to be further explored. Searching for potential lymph node metastasis-related genes of BC will provide meaningful biomarkers for BC treatment. Based on TCGA-BRCA data, we established an effective 11-gene prognostic risk model that could predict patient outcomes independently. Our model could classify BC patients and distinguish patients with poor prognosis effectively. Besides, the feature genes we identified might exert a predictive function in immunotherapy. The results of this study provide a new reference for the prognosis and treatment of BC patients with lymph node metastasis.

Construction of a Liver Cancer Prognostic Model Based on Interferon-Gamma-Related Genes for Revealing the Immune Landscape

Inferferon-gamma (LFN-γ) exerts anti-tumor effects, but there is currently no reliable and comprehensive study on prognostic function of IFN-γ-related genes in liver cancer. In this study, IFN-γ-related differentially expressed genes (DEGs) in liver cancer were identified through GO/KEGG databases and open-access literature. Based on these genes, individuals with liver cancer were clustered. A prognostic model was built based on the intersection genes between differential genes in clusters and in liver cancer. Then, model predictive performance was analyzed and validated in GEO dataset. Regression analysis was fulfilled on the model, and a nomogram was utilized to evaluate model ability as an independent prognostic factor and its clinical application value. An immune-related analysis was conducted on both the H- and L-groups, with an additional investigation into link of model genes to drug sensitivity. Significant differential expression of IFN-γ-related genes was observed between the liver cancer and control groups. Subsequently, individuals with liver cancer were classified into two subtypes based on these genes, which displayed a notable difference in survival between the two subtypes. A 10-gene liver cancer prognostic model was constructed, with good prognostic performance and was an independent prognosticator for patient analysis. L-group patients possessed higher immune infiltration levels, immune checkpoint expression levels, and immunophenoscore, as well as lower TIDE scores. Drugs that had high correlations with the feature genes included SPANXB1: PF-04217903, SGX-523, MMP1: PF-04217903, DUSP13: Imatinib, TFF1: KHK-Indazole, and Fulvestrant. We built a 10-gene liver cancer prognostic model. It was found that L-group patients were more suitable for immunotherapy. This study provided valuable information on the prognosis of liver cancer.

Combined Use of Tumor Markers in Gastric Cancer: A Novel Method with Promising Prognostic Accuracy and Practicality

BACKGROUND

The effect of a single tumor marker on the prognosis of gastric cancer patients is not ideal. This study explored a novel prognostic assessment method for gastric cancer (GC) patients using a combination of three important tumor markers (CEA, CA72-4, and CA19-9).

METHOD

Data from 1966 GC patients who underwent curative gastrectomy at Sun Yat-Sen University Cancer Center (Guangzhou, China) were included. Hazard ratios (HR) for all factors for overall survival (OS) were analyzed by Cox regression. A nomogram and calibration curve were used to establish the survival prediction model. The prediction accuracy was evaluated with the concordance index (C-index).

RESULTS

All patients were divided into four groups (C0-C3) according to the number of elevated tumor markers. The 5-year OS rates of the patients in preoperative groups C0-C3 were 83.8% (81.3-86.4%), 72.8% (68.5-77.4%), 58.9% (50.4-68.9%), and 18.5% (4.0-33.0%), respectively, and those in postoperative groups C0-C3 were 82.1% (79.4-84.8%), 76.1% (72.2-80.3%), 57.6% (48.4-68.5%), and 16.8% (5.1-28.5%), respectively, with significant differences between each C0-C3 subgroup in both preoperative and postoperative cohorts. Multivariate analysis showed that preoperative (HR: 6.001, 95% CI: 3.523-10.221) and postoperative (HR: 8.149, 95% CI: 4.962-13.528) elevated tumor markers were independent risk factors for GC patients. The C-index for the combined use of tumor markers was 0.65-0.66, which was higher than that for using a single tumor marker (0.53-0.56).

CONCLUSION

The combined use of tumor markers significantly improved the prognostic value compared with using a single tumor marker. The survival prediction model including the combined tumor markers was accurate and effective.

Identification of prognostic biomarkers of breast cancer based on the immune-related gene module

Breast cancer (BC) is highly malignant and its mortality rate remains high. The development of immunotherapy has gradually improved the prognosis and survival rate of patients. Therefore, identifying molecular markers concerned with BC immunity is of great importance for the treatment of this disease. The Cancer Genome Atlas-breast invasive carcinoma (TCGA-BRCA) was utilized as the training set while the BC expression dataset from the gene expression omnibus database was taken as the validation set here. Weighted gene co-expression network analysis combined with Pearson analysis and Tumor immune estimation resource (TIMER) was used to obtain immune cell-related hub gene module. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on this module. Then, receiver operating characteristic curves combining Kaplan-Meier was used to evaluate the effectiveness of the model. Feature genes were screened and the independence of risk score was evaluated by univariate and multivariate Cox analyses. Differences in immune characteristics were analyzed via single-sample gene set enrichment analysis and CIBERSORT, and differences in gene mutation frequency were assessed via GenVisR analysis. Finally, the expression levels of prognostic feature genes in BC cells were validated by quantitative reverse transcription polymerase chain reaction (qRT-PCR). In this study, cell immune-related gene modules in TCGA-BRCA were successfully excavated, and a five-gene (TNFRSF14, NFKBIA, DLG3, IRF2, and CYP27A1) prognostic model was established. The prognostic model could effectively forecast the prognosis and survival rate of BC patients. The result showed that human leukocyte antigen-related proteins and macrophage M2 scores were remarkably highly expressed in the high-risk group, whereas CD8+ T cells, natural killer cells, M1, and other anti-tumor cells were lowly expressed. The model could be used as an independent prognostic factor to predict the prognosis of BC patients. The results of qRT-PCR validation were consistent with the results in the database, that is, except DLG3, the other four feature genes were lowly expressed in BC. The five-gene model established in this study can predict the prognostic and immune mode of BC patients effectively, which is anticipated to become a feasible molecular target for BC therapy.

Construction of stomach adenocarcinoma prognostic signature based on anoikis-related lncRNAs and clinical significance

As a dominant type of gastric cancer, stomach adenocarcinoma (STAD) is characterized by high morbidity and mortality rates. Anoikis factors participate in tumor metastasis and invasion. This study was designed to identify prognostic risk factors in anoikis-related long non-coding RNAs (lncRNAs) for STAD. First, with STAD expression datasets and anoikis-related gene sets downloaded from public databases, anoikis-related prognostic lncRNA signatures (AC091057.1, ADAMTS9.AS1, AC090825.1, AC084880.3, EMX2OS, HHIP.AS1, AC016583.2, EDIL3.DT, DIRC1, LINC01614, and AC103702.2) were screened by Cox regression to establish a prognostic risk model. Kaplan-Meier and receiver operating characteristic curves were used to evaluate the survival status of patients and verify predictive accuracy of the model. Besides, risk score could be an independent prognostic factor to assess the prognosis of STAD patients. Nomograms of the prognostic model that combined clinical information and risk score could effectively predict survival of STAD patients, as validated by calibration curve. Gene ontology and Kyoto encyclopedia of genes and genomes enrichment analyses were performed for differentially expressed genes (DEGs) in high- and low-risk groups. These DEGs were related to neurotransmitter transmission, signal transmission, and endocytosis. Moreover, we analyzed immune status of different risk groups and found that STAD patients in low-risk group were more sensitive to immunotherapy. A prognostic risk assessment model for STAD using anoikis-related lncRNA genes was constructed here, which was proven to have high predictive accuracy and thus could offer a reference for prognostic evaluation and clinical treatment of STAD patients.

Prognostic characteristics of T-cell mediated cell killing-related genes in lung adenocarcinoma

Constituted by various heterogeneous cells, the tumor microenvironment (TME) is capable of promoting tumor proliferation, invasion, and metastasis through extensive crosstalk. The pivotal factor influencing the survival time of patients and their response to immunotherapy lies in the intratumoral immune environment. We obtained 112 differential genes related to T cell-mediated tumor killing in LUAD by employing bioinformatics analysis on the basis of the TCGA and TISIDB databases. Then the 6-gene prognostic risk score model (CA9, OIP5, TIMP1, SEC11C, FURIN, and TLR10) was constructed by conducting univariate LASSO as well as multivariate Cox regression analyses. The median risk score was taken as the threshold to classify the samples into two groups. Survival analysis revealed that the low-risk group exhibited a more favorable prognosis. Subsequently, the Cox regression analysis combined with clinical information (age, gender, and pathological stage) and the risk score of LUAD patients demonstrated the potential of this model as an independent prognostic factor. The nomogram established based on clinical information and a risk score in combination with the calibration curve indicated that this model had good predictive ability. Notable enrichment of the differential genes from the high- and low-risk groups was discovered in immune-associated processes or pathways, as shown by the GO and KEGG enrichment analyses. The combined use of single-sample gene enrichment analysis (ssGSEA) and immunophenoscore (IPS) demonstrated heightened immune infiltration and IPS scores in the low-risk group, indicating that immunotherapy was likely to show good efficacy in patients from this group. To sum up, the prognostic model of LUAD constructed based on T-cell-mediated cell killing-related genes was not only capable of screening the prognosis of LUAD patients but was also used for screening those LUAD patients with high sensitivity to immunotherapy. Our study offered novel insights into the clinical treatment and prognostic prediction of LUAD patients.

Construction of a prognostic model for lung adenocarcinoma based on heat shock protein-related genes and immune analysis

Lung adenocarcinoma (LUAD) represents a prevalent form of cancer, with low early diagnosis rates and high mortality rates, posing a global health challenge. Heat shock proteins (HSPs) assume a crucial role within the tumor immune microenvironment (TME) of LUAD. Here, a collection of 97 HSP-related genes (HSPGs) was assembled based on prior literature reports, of which 36 HSPGs were differentially expressed in LUAD. In The Cancer Genome Atlas (TCGA) cohort, we constructed a prognostic model for risk stratification and prognosis prediction by integrating 13 HSPGs. In addition, the prognostic significance and predictive efficacy of the HSP-related riskscore were examined and validated in the Gene Expression Omnibus (GEO) cohort. To facilitate the clinical use of this riskscore, we also established a nomogram scale by verifying its effectiveness through different methods. In light of these outcomes, we concluded a significant correlation between HSPs and TME in LUAD, and the riskscore can be a reliable prognostic indicator. Furthermore, this study evaluated the differences in immunophenoscore, tumor immune dysfunction and exclusion score, and sensitivity to several common chemotherapy drugs among LUAD individuals in different risk groups, which may aid in clinical decision-making for immune therapy and chemotherapy in LUAD individuals.

A radiomics nomogram based on 18 F-FDG PET/CT and clinical risk factors for the prediction of peritoneal metastasis in gastric cancer

PURPOSE

Peritoneal metastasis (PM) is usually considered an incurable factor of gastric cancer (GC) and not fit for surgery. The aim of this study is to develop and validate an 18 F-FDG PET/CT-derived radiomics model combining with clinical risk factors for predicting PM of GC.

METHOD

In this retrospective study, 410 GC patients (PM - = 281, PM + = 129) who underwent preoperative 18 F-FDG PET/CT images from January 2015 to October 2021 were analyzed. The patients were randomly divided into a training cohort (n = 288) and a validation cohort (n = 122). The maximum relevance and minimum redundancy (mRMR) and the least shrinkage and selection operator method were applied to select feature. Multivariable logistic regression analysis was preformed to develop the predicting model. Discrimination, calibration, and clinical usefulness were used to evaluate the performance of the nomogram.

RESULT

Fourteen radiomics feature parameters were selected to construct radiomics model. The area under the curve (AUC) of the radiomics model were 0.86 [95% confidence interval (CI), 0.81-0.90] in the training cohort and 0.85 (95% CI, 0.78-0.92) in the validation cohort. After multivariable logistic regression, peritoneal effusion, mean standardized uptake value (SUVmean), carbohydrate antigen 125 (CA125) and radiomics signature showed statistically significant differences between different PM status patients( P  < 0.05). They were chosen to construct the comprehensive predicting model which showed a performance with an AUC of 0.92 (95% CI, 0.89-0.95) in the training cohort and 0.92 (95% CI, 0.86-0.98) in the validation cohort, respectively.

CONCLUSION

The nomogram based on 18 F-FDG PET/CT radiomics features and clinical risk factors can be potentially applied in individualized treatment strategy-making for GC patients before the surgery.

Construction of prognostic risk markers for cervical cancer combined with anoikis-related genes and their clinical significance

CONTEXT

Several studies have demonstrated that anoikis affects the development, metastasis and prognosis of cancer.

AIMS

This study aimed to identify anoikis-related marker genes in cervical cancer (CC).

METHODS

Least absolute shrinkage and selection operator (LASSO) combined with Cox regression analysis was used to construct a prognostic model and analyse the independent prognostic ability of riskscore. Receiver operating characteristic curve (ROC) and survival curves were used to evaluate and verify the performance and accuracy of the model. The nomogram of CC prognostic model was drawn using riskscore combined with clinical information. We analysed the relationship between prognostic riskscore and immune infiltration level and analysed immunophenoscore. Finally, qRT-PCR assay was used to verify the feature genes.

KEY RESULTS

By Cox analysis, we found that the prognostic risk model could effectively predict the risk of CC in patients independently of other clinical factors. Both the levels of immune infiltration and the immunophenoscore were significantly lower in high-risk CC patients than those in low-risk patients, revealing that high-risk patients were likely to have bad response to immunotherapy. The qRT-PCR results of the feature genes were consistent with the results of gene expression in the database.

CONCLUSIONS

The prognostic model constructed, based on anoikis-related genes in CC, could predict the prognosis of CC patients.

IMPLICATIONS

The model described here can provide effective support for assessing prognostic risk and devising personalised protocols during clinical treatment.

Hepatocellular Carcinoma Subtyping and Prognostic Model Construction Based on Chemokine-Related Genes

BACKGROUND

Chemokines not only regulate immune cells but also play significant roles in development and treatment of tumors and patient prognoses. However, these effects have not been fully explained in hepatocellular carcinoma (HCC).

MATERIALS AND METHODS

We conducted a clustering analysis of chemokine-related genes. We then examined the differences in survival rates and analyzed immune levels using single-sample Gene Set Enrichment Analysis (ssGSEA) for each subtype. Based on chemokine-related genes of different subtypes, we built a prognostic model in The Cancer Genome Atlas (TCGA) dataset using the survival package and glmnet package and validated it in the Gene Expression Omnibus (GEO) dataset. We used univariate and multivariate regression analyses to select independent prognostic factors and used R package rms to draw a nomogram reflecting patient survival rates at 1, 3, and 5 years.

RESULTS

We identified two chemokine subtypes and, after screening, found that Cluster1 had higher survival rates than Cluster2. In addition, in terms of immune evaluation, stromal evaluation, ESTIMATE evaluation, immune abundance, immune function, and expressions of various immune checkpoints, immune levels of Cluster1 were significantly better than those of Cluster2. The immunophenoscore (IPS) of HCC patients in Cluster1 was significantly higher than that in Cluster2. Furthermore, we established a prognostic model consisting of 9 genes, which correlated with chemokines. Through testing, Riskscore was revealed as an independent prognostic factor, and the model could effectively predict HCC patients' prognoses in both TCGA and GEO datasets.

CONCLUSION

This study resulted in the development of a novel prognostic model related to chemokine genes, providing new targets and theoretical support for HCC patients.

A prognostic model based on regulatory T-cell-related genes in gastric cancer: Systematic construction and validation

Human gastrointestinal tumours have been shown to contain massive numbers of tumour infiltrating regulatory T cells (Tregs), the presence of which are closely related to tumour immunity. This study was designed to develop new Treg-related prognostic biomarkers to monitor the prognosis of patients with gastric cancer (GC). Treg-related prognostic genes were screened from Treg-related differentially expressed genes in GC patients by using Cox regression analysis, based on which a prognostic model was constructed. Then, combined with RiskScore, survival curve, survival status assessment and ROC analysis, these genes were used to verify the accuracy of the model, whose independent prognostic ability was also evaluated. Six Treg-related prognostic genes (CHRDL1, APOC3, NPTX1, TREML4, MCEMP1, GH2) in GC were identified, and a 6-gene Treg-related prognostic model was constructed. Survival analysis revealed that patients had a higher survival rate in the low-risk group. Combining clinicopathological features, we performed univariate and multivariate regression analyses, with results establishing that the RiskScore was an independent prognostic factor. Predicted 1-, 3- and 5-year survival rates of GC patients had a good fit with the actual survival rates according to nomogram results. In addition patients in the low-risk group had higher tumour mutational burden (TMB) values. Gene Set Enrichment Analysis (GSEA) demonstrated that genes in the high-risk group were significantly enriched in pathways related to immune inflammation, tumour proliferation and migration. In general, we constructed a 6-gene Treg-associated GC prognostic model with good prediction accuracy, where RiskScore could act as an independent prognostic factor. This model is expected to provide a reference for clinicians to estimate the prognosis of GC patients.

Semaphorin4F is a potential biomarker for clinical progression and prognosis in gastric cancer

BACKGROUND

Semaphorin4F (Sema4F) is a member of the semaphorin family and exhibits important regulatory functions in cancer biology. We aimed to explore the prognostic value and biologic function of Sema4F in gastric cancer (GC) through clinical data, laboratory studies, and bioinformatic methods.

METHODS

We investigated Sema4F-related data and the prognostic values of patients with GC based on several databases, including Tumor Immune Estimation Resource (TIMER), the Gene Expression Profiling Interactive Analysis 2 (GEPIA2), The University Of Alabama At Birmingham Cancer Data Analysis Portal (UALCAN) and Kaplan-Meier Plotter. We detected the expression of Sema4F in cell lines and tumor tissues by reverse transcription quantitative polymerase chain reaction (RT-qPCR), western blotting and immunohistochemistry. The prognostic value of Sema4F expression on patient overall survival was analyzed retrospectively using Kaplan-Meier survival and Cox regression analyses. Moreover, we used Kyoto encyclopedia of genes and genomes (KEGG), Gene Ontology (GO) and Gene-set enrichment analysis (GSEA) analyses to explore the relevant pathways of Sema4F in GC.

RESULTS

The expression of Sema4F was markedly increased in cancer tissues and cancer cell lines. Furthermore, high Sema4F expression was positively associated with various clinicopathologic data and independently predicted poor prognosis for overall survival in GC. Our functional enrichment analysis revealed that Sema4F was mainly involved in oxidative phosphorylation and tumor-related signaling pathways.

CONCLUSIONS

Sema4F may be a valuable prognostic biomarker and a novel target for gastric cancer.

Construction of a prognostic model based on genes associated with mitochondrial energy metabolic pathway in colon adenocarcinoma and its clinical significance

Mitochondria are the main sites of oxidative metabolism and energy release of sugars, fats and amino acids in the body. According to studies, malignant tumor occurrence and development have been linked to abnormal mitochondrial energy metabolism (MEM). However, the feasible role of abnormal MEM in colon adenocarcinoma (COAD) is poorly understood. In this work, we obtained COAD patient data from The Cancer Genome Atlas (TCGA) as the training set, and GSE103479 from Gene Expression Omnibus (GEO) as the validation set. Combined with the mitochondrial energy metabolic pathway (MEMP)-related genes in Kyoto Encyclopedia of Genes and Genomes (KEGG) database, a risk prognostic model was constructed by utilizing Cox regression analysis to identify 6 feature genes (CYP4A11, PGM2, PKLR, PPARGC1A, CPT2 and ACAT2) that were significantly associated with MEMP in COAD. By stratifying the samples based on riskscore, two distinct groups, namely the high- and low-risk groups, were identified. The model demonstrated accurate assessment of the prognosis risk in COAD patients and exhibited independent prognostic capability, as evidenced by the survival curve and receiver operating characteristic (ROC) curve analysis. A nomogram was plotted based on clinical information and riskscore. We proved it could predict the survival time of COAD patients effectively combined with the calibration curve of risk prediction. Subsequently, based on the immune evaluation and mutation frequency analysis performed on COAD patients, patients in high-risk group had observably higher immune scores, immune activity and PDCD1 expression level than low-risk group. In general, the prognostic model developed using MEMP-related genes served as a valuable biomarker for forecasting the prognosis of COAD patients, which offered a reference for the prognosis evaluation and clinical cure of COAD patients.

A Liquid Biopsy Signature for the Early Detection of Gastric Cancer in Patients

BACKGROUND & AIMS

Diagnosing gastric cancer (GC) while the disease remains eligible for surgical resection is challenging. In view of this clinical challenge, novel and robust biomarkers for early detection thus improving prognosis of GC are necessary. The present study is to develop a blood-based long noncoding RNA (LR) signature for the early-detection of GC.

METHODS

The present 3-step study incorporated data from 2141 patients, including 888 with GC, 158 with chronic atrophic gastritis, 193 with intestinal metaplasia, 501 healthy donors, and 401 with other gastrointestinal cancers. The LR profile of stage I GC tissue samples were analyzed using transcriptomic profiling in discovery phase. The extracellular vesicle (EV)-derived LR signature was identified with a training cohort (n = 554) and validated with 2 external cohorts (n = 429 and n = 504) and a supplemental cohort (n = 69).

RESULTS

In discovery phase, one LR (GClnc1) was found to be up-regulated in both tissue and circulating EV samples with an area under the curve (AUC) of 0.9369 (95% confidence interval [CI], 0.9073-0.9664) for early-stage GC (stage I/II). The diagnostic performance of this biomarker was further confirmed in 2 external validation cohorts (Xi'an cohort, AUC: 0.8839; 95% CI: 0.8336-0.9342; Beijing cohort, AUC: 0.9018; 95% CI: 0.8597-0.9439). Moreover, EV-derived GClnc1 robustly distinguished early-stage GC from precancerous lesions (chronic atrophic gastritis and intestinal metaplasia) and GC with negative traditional gastrointestinal biomarkers (CEA, CA72-4, and CA19-9). The low levels of this biomarker in postsurgery and other gastrointestinal tumor plasma samples indicated its GC specificity.

CONCLUSIONS

EV-derived GClnc1 serves as a circulating biomarker for the early detection of GC, thus providing opportunities for curative surgery and improved survival outcomes.

Application of Circulating Tumor Cells and Interleukin-6 in Preoperative Prediction of Peritoneal Metastasis of Advanced Gastric Cancer

Background

The purpose of this study was to explore the clinical significance of circulating tumor cells (CTCs) and cytokines in peripheral blood in preoperative prediction of peritoneal metastasis (PM) in advanced gastric cancer (AGC).

Methods

The clinicopathological characteristics of 282 patients with AGC were retrospectively analyzed. The patients were divided into training and validation groups according to the time of receiving treatment. We used univariate analysis and multivariate logistic regression analysis to screen out the independent risk factors of PM in AGC. Then, we incorporated independent risk factors into the nomogram, and evaluated the discriminative ability.

Results

The levels of CTCs and interleukin-6 (IL-6) of AGC patients with PM were higher than those without PM (P<0.05). Moreover, the levels of CTCs and IL-6 in the occult peritoneal metastasis (OPM) group and the CT-positive PM group were higher than those in the negative PM (P<0.05). Multivariate logistic regression analysis showed that IL-6 > 12.22 pg/mL, CTCs > 4/5mL, CA724 > 6 IU/mL, CA125 > 35 U/mL and tumor size > 5 cm were independent risk factors for PM of AGC. The area under the ROC curve of the nomogram were 0.898 and 0.926 in the training and validation sets, respectively. The clinical decision curve showed that the nomogram had good clinical utility.

Conclusion

CTCs and IL-6 in peripheral blood are promising biomarkers for predicting the risk of PM in AGC. The nomogram constructed from five risk factors can effectively assess the risk of PM in AGC patients individually.

A DNA Replication Stress-Based Prognostic Model for Lung Adenocarcinoma

Tumor cells endure continuous DNA replication stress, which opens the way to cancer development. Despite previous research, the prognostic implications of DNA replication stress on lung adenocarcinoma (LUAD) have yet to be investigated. Here, we aimed to investigate the potential of DNA replication stress-related genes (DNARSs) in predicting the prognosis of individuals with LUAD. Differentially expressed genes (DEGs) originated from the TCGA-LUAD dataset, and we constructed a 10-gene LUAD prognostic model based on DNARSs-related DEGs (DRSDs) using Cox regression analysis. The receiver operating characteristic (ROC) curve demonstrated excellent predictive capability for the LUAD prognostic model, while the Kaplan-Meier survival curve indicated a poorer prognosis in a high-risk (HR) group. Combined with clinical data, the Riskscore was found to be an independent predictor of LUAD prognosis. By incorporating Riskscore and clinical data, we developed a nomogram that demonstrated a capacity to predict overall survival and exhibited clinical utility, which was validated through the calibration curve, ROC curve, and decision curve analysis curve tests, confirming its effectiveness in prognostic evaluation. Immune analysis revealed that individuals belonging to the low-risk (LR) group exhibited a greater abundance of immune cell infiltration and higher levels of immune function. We calculated the immunopheno score and TIDE scores and tested them on the IMvigor210 and GSE78220 cohorts and found that individuals categorized in the LR group exhibited a higher likelihood of deriving therapeutic benefits from immunotherapy intervention. Additionally, we predicted that patients classified in the HR group would demonstrate enhanced sensitivity to Docetaxel using anti-tumor drugs. To summarize, we successfully developed and validated a prognostic model for LUAD by incorporating DNA replication stress as a key factor.

Cancer-specific survival in patients with cholangiocarcinoma after radical surgery: a Novel, dynamic nomogram based on clinicopathological features and serum markers

BACKGROUND

This study aims to (1) identify preoperative testing-based characteristics associated with enhanced prognosis and survival for cholangiocarcinoma patients, and (2)create a distinctive nomogram to anticipate each patient's cancer-specific survival (CSS).

METHODS

Retrospective analysis was performed on 197 CCA patients who underwent radical surgery at Sun Yat-sen Memorial Hospital; they were divided into a 131-person "training cohort" and a 66-person "internal validation cohort." The prognostic nomogram was created following a preliminary Cox proportional hazard regression search for independent factors influencing the patients' CSS. Its applicable domain was examined via an external validation cohort, which included 235 patients from the Sun Yat-sen University Cancer Center.

RESULTS

The median follow-up period for the 131 patients in the training group was 49.3 months (range, 9.3 to 133.9 months). One-, three-, and five-year CSS rates were 68.7%, 24.5%, and 9.2%, respectively, with the median CSS length being 27.4 months (range: 1.4 to 125.2 months). PLT, CEA, AFP, tumor location, differentiation, lymph node metastasis, chemotherapy, and TNM stage were determined to be independent risk factors for CCA patients by univariate and multivariate Cox proportional hazard regression analysis. We were able to accurately predict postoperative CSS after incorporating all of these characteristics into a nomogram. The AJCC's 8th edition staging method's C-indices were statistically substantially (P < 0.001) lower than the nomogram's C-indices (0.84, 0.77, and 0.74 in the training, internal and external validation cohorts respectively).

CONCLUSIONS

A realistic and useful model for clinical decision-making and the optimization of therapy is presented as a nomogram that includes serum markers and clinicopathologic features for predicting postoperative survival in cholangiocarcinoma.

Development of an Immune Prognostic Model for Clear Cell Renal Cell Carcinoma Based on Tumor Microenvironment

Immune infiltration remains at a high level in clear cell renal cell carcinoma (ccRCC). It has been confirmed that immune cell infiltration in tumor microenvironment (TME) is intimately bound up with the progression and the clinical outcome of ccRCC. The prognostic model, developed based on different immune subtypes of ccRCC, has a predictive value in patients' prognosis. RNA sequencing data, somatic mutation data of ccRCC and clinical information were acquired from the cancer genome atlas (TCGA) database. The key immune-related genes (IRGs) were selected and by univariate Cox, LASSO, and multivariate Cox regression analyses. Then the ccRCC prognostic model was developed. The applicability of this model was verified in the independent dataset GSE29609. Thirteen IRGs including CCL7, ATP6V1C2, ATP2B3, ELAVL2, SLC22A8, DPP6, EREG, SERPINA7, PAGE2B, ADCYAP1, ZNF560, MUC20, and ANKRD30A were finally selected and a 13-IRGs prognostic model was developed. Survival analysis demonstrated that when compared with the low-risk group, patients in the high-risk group had a lower overall survival (p<0.05). AUC values based on the 13-IRGs prognostic model used to predict 3- and 5-year survival of ccRCC patients were greater than 0.70. And risk score was an independent prognostic factor (p<0.001). In addition, nomogram could accurately predict ccRCC patient's prognosis. This 13-IRGs model can effectively evaluate the prognosis of ccRCC patients, and also provide guidance for the treatment and prognosis of ccRCC patients.

Prognosis Prediction of Lung Adenocarcinoma Patients Based on Molecular Subgroups of DNA Methylation

Lung adenocarcinoma (LUAD) is a malignant tumor with high mortality. At present, the clinicopathologic feature is the main breakthrough to assess the prognosis of LUAD patients. However, in most cases, the results are less than satisfactory. Cox regression analysis was conducted in this study to obtain methylation sites with significant prognostic relevance based on mRNA expression, DNA methylation data, and clinical data of LUAD from The Cancer Genome Atlas Program database. LUAD patients were grouped into 4 subtypes according to different methylation levels using K-means consensus cluster analysis. By survival analysis, patients were grouped into high-methylation and low-methylation groups. Later, 895 differentially expressed genes (DEGs) were obtained. Eight optimal methylation signature genes associated with prognosis were screened by Cox regression analysis, and a risk assessment model was constructed based on these genes. Samples were then classified into high-risk and low-risk groups depending on the risk assessment model, and prognostic, predictive ability was assessed using survival and receiver operating characteristic (ROC) curves. The results showed that this risk model had a great efficacy in predicting the prognosis of patients, and it was, therefore, able to be an independent prognostic factor. At last, the enrichment analysis demonstrated that the signaling pathways, including cell cycle, homologous recombination, P53 signaling pathway, DNA replication, pentose phosphate pathway, and glycolysis gluconeogenesis were remarkably activated in the high-risk group. In general, we construct an 8-gene model based on DNA methylation molecular subtypes by a series of bioinformatics methods, which can provide new insights for predicting the prognosis of patients with LUAD.

Construction of a prognostic risk assessment model for HER2 + breast cancer based on autophagy-related genes

Although breast cancer (BC) has a low mortality rate relative to other cancers, it prominently affects the survival of patients with human epidermal growth factor receptor-2 (HER2 +) BC due to its high recurrence rate. By far, it has been found that autophagy can affect various tumor occurrence and development, as well as patients' prognosis. HER2 + BC patient samples and autophagy-related genes (ARGs) were acquired from a public database, least absolute shrinkage and selection operator (LASSO) and Cox analyses (including univariate and multivariate analyses) were utilized to construct a 9-ARGs model, which was verified by using HER2 + BC patient samples in The Cancer Genome Atlas (TCGA) dataset. Sample risk score was worked out based on characteristic genes, and prominent differences in overall survival were tracked down between high- and low-risk groups. Predictive ability of the model was validated by drawing receiver operating characteristic (ROC) curves and then calculating the area under the curves (AUC) value. Results showed good accuracy and prediction ability of the model in both validation set and training set. For the purpose of facilitating model application in clinical practice, we constructed a nomogram combing clinical factors and risk scores to evaluate 1-year, 3-year and 5-year survival of HER2 + BC patients. In addition, we assessed the correlation of risk score with tumor mutational burden and tumor immune infiltration. Results exhibited that in a high-risk group, tumor mutation was relatively high, while tumor immune infiltration was relatively poor. Overall, based on ARGs, the prognostic signature in this study can tellingly evaluate prognoses of HER2 + BC patients and provide a reference for clinicians.

A near-infrared photoelectrochemical immunosensor for CA72-4 sensing based on SnS nanorods integrated with gold nanoparticles

SnS nanorods with near-infrared photoelectric conversion characteristics were successfully synthesized through a simple hydrothermal method. Gold nanoparticles were self-assembled onto SnS nanorods surface to form SnS/AuNPs nanocomposites. The integration of AuNP can significantly improve the photocurrent response of SnS nanorods under being illuminated with 808 nm near-infrared light. A near-infrared photoelectrochemical immunosensing platform based on SNS/AuNPs nanocomposites was constructed for sensing gastric cancer tumor marker CA72-4. Experimental conditions were optimized to improve the immunosensing performances for CA72-4 determination. As CA72-4 concentration varied from 0.01 to 50 U mL^-1, the photocurrent variation between the immunosensor before and after reacting with CA72-4 was linearly related to the logarithm of its concentration. The detection limit was calculated to be 0.008 U mL^-1. The practicability of the immunosensor was demonstrated by determining CA72-4 in human serum samples.

A 9-gene prognostic signature for kidney renal clear cell carcinoma overall survival based on co-expression and regression analyses

This research attempted to screen potential signatures associated with KIRC progression and overall survival by weighted gene co-expression network analysis (WGCNA) and Cox regression. The KIRC-associated mRNA expression and clinical data were accessed from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were screened by differential analysis. A co-expression network was constructed by "WGCNA". Based on WGCNA module, GO and KEGG analyses were performed. Protein-protein interaction (PPI) network was constructed. Prognostic signatures were screened by Lasso-Cox regression. Prognostic model was evaluated by Receiver Operating Characteristic (ROC) and Kaplan-Meier (K-M) curves. Multivariate Cox and nomogram were introduced to examine whether risk score could be an independent marker. qRT-PCR was introduced to determine expression of 9 hub genes in KIRC clinical tumor tissues and adjacent tissues, respectively. Genes in the green module were highly associated with clinical status, and green module genes were significantly enriched in mitotic nuclear division, cell cycle, and p53 signaling pathway. Twenty-six candidates were subsequently screened out from the green module. Next, a 9-gene prognostic model (DLGAP5, NUF2, TOP2A, RRM2, HJURP, PLK1, AURKB, KIF18A, CCNB2) was constructed. The predicting ability of the model was optimal. Some cancer-related signaling pathways were differently activated between two risk score groups. Additionally, under-expression of some signature genes (AURKB, CCNB2, PLK1, RRM2, TOP2A) was associated with better survival rate for KIRC patients. Meanwhile, all 9 hub genes were substantially overexpressed in KIRC patients. A KIRC prognostic signature was screened in this study, contributing valuable findings to KIRC biomarker development.

Development and validation of a nomogram for predicting metachronous peritoneal metastasis in colorectal cancer: A retrospective study

BACKGROUND

Peritoneal metastasis (PM) after primary surgery for colorectal cancer (CRC) has the worst prognosis. Prediction and early detection of metachronous PM (m-PM) have an important role in improving postoperative prognosis of CRC. However, commonly used imaging methods have limited sensitivity to detect PM early. We aimed to establish a nomogram model to evaluate the individual probability of m-PM to facilitate early interventions for high-risk patients.

AIM

To establish and validate a nomogram model for predicting the occurrence of m-PM in CRC within 3 years after surgery.

METHODS

We used the clinical data of 878 patients at the Second Hospital of Jilin University, between January 1, 2014 and January 31, 2019. The patients were randomly divided into training and validation cohorts at a ratio of 2:1. The least absolute shrinkage and selection operator (LASSO) regression was performed to identify the variables with nonzero coefficients to predict the risk of m-PM. Multivariate logistic regression was used to verify the selected variables and to develop the predictive nomogram model. Harrell’s concordance index, receiver operating characteristic curve, Brier score, and decision curve analysis (DCA) were used to evaluate discrimination, distinctiveness, validity, and clinical utility of this nomogram model. The model was verified internally using bootstrapping method and verified externally using validation cohort.

RESULTS

LASSO regression analysis identified six potential risk factors with nonzero coefficients. Multivariate logistic regression confirmed the risk factors to be independent. Based on the results of two regression analyses, a nomogram model was established. The nomogram included six predictors: Tumor site, histological type, pathological T stage, carbohydrate antigen 125, v-raf murine sarcoma viral oncogene homolog B mutation and microsatellite instability status. The model achieved good predictive accuracy on both the training and validation datasets. The C-index, area under the curve, and Brier scores were 0.796, 0.796 [95% confidence interval (CI) 0.735-0.856], and 0.081 for the training cohort and 0.782, 0.782 (95%CI 0.690-0.874), and 0.089 for the validation cohort, respectively. DCA showed that when the threshold probability was between 0.01 and 0.90, using this model to predict m-PM achieved a net clinical benefit.

CONCLUSION

We have established and validated a nomogram model to predict m-PM in patients undergoing curative surgery, which shows good discrimination and high accuracy.

Fibrinogen-to-Lymphocyte Ratio Was an Independent Predictor of Lymph Node Metastasis in Patients with Clinically Node-Negative Advanced-Stage Gastric Cancer

Purpose

Various hematological indicators have been reported to predict lymph node metastasis (LNM) in gastric cancer (GC) patients, but the relationship between FLR and LNM has not been studied. Therefore, the aim of this study was to evaluate the role of preoperative fibrinogen-to-lymphocyte ratio (FLR) in predicting LNM in patients with clinically node-negative (cN0) advanced gastric cancer (AGC).

Patients and Methods

We retrospectively reviewed 571 eligible patients with primary AGC adenocarcinoma who underwent radical gastrectomy (discovery cohort). Patients were divided into high and low FLR groups according to the optimal cutoff value determined by Youden index. FLR is an independent predictor of LNM determined by logistic regression and validated in the validation cohort of 207 patients. Receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive value of FLR for LNM. The nonlinear relationship between FLR and LNM risk was assessed using restricted cubic spline. Sensitivity analyses were performed according to FLR quartiles to further assess the robustness of the results. The nomogram was built based on FLR and clinicopathological characteristics, and was evaluated by calibration curves, ROC curve analysis and decision curve analysis.

Results

In the discovery cohort, the area under the curve (AUC) value for FLR to predict LNM was 0.592. There is a linear relationship between the FLR value and the risk of LNM, and the risk of LNM increased with FLR value. High FLR level is an independent risk factor for LNM, and the results of sensitivity analysis robust this finding. The nomogram for individual risk assessment performed well. Furthermore, we verified the FLR was an independent predictor of LNM in the validation cohort.

Conclusion

FLR was an independent predictor of LNM in patients with cN0 AGC.

Prognostic value of genes related to cancer-associated fibroblasts in lung adenocarcinoma

BACKGROUND

Although it has been established that cancer-associated fibroblasts (CAFs) facilitate tumor development, the relationship between CAFs and the prognosis of patients with lung adenocarcinoma (LUAD) has not been extensively explored.

OBJECTIVE

This study was formulated to investigate the prognostic value of CAF-related genes in LUAD.

METHODS

Differential analysis was carried out with TCGA-LUAD dataset as the training set. By overlapping differentially expressed genes (DEGs) with genes associated with CAF, CAF-related DEGs specific to LUAD were obtained. A prognostic risk model was constructed by Lasso and Cox regression analysis, and samples were grouped according to median risk score. The efficacy of the model was accessed through survival curve and receiver operating characteristic curve (ROC) analyses, with the validation set for verification. Risk score combined with clinical factors was utilized for Cox analysis to verify the independence of the model, and a nomogram was drawn. GSEA was performed on different risk groups. Immunologic infiltration and tumor mutational burden were assessed in different risk groups.

RESULTS

Eleven feature genes including DLGAP5, KCNE2, UPK2, NPAS2, ARHGAP11A, ANGPTL4, ANLN, DKK1, SMUG1, C16orf74, and ACAD8 were identified, based on which a prognostic model was constructed. Risk score could predict the prognosis of LUAD patients and could be an independent prognostic factor for LUAD patients. GSEA outcomes displayed significant enrichment of genes in the high-risk group in the P53 SIGNALING PATHWAY. In comparison to the low-risk group, the high-risk group exhibited a decreased degree of immune infiltration and an elevated level of tumor mutational burden.

CONCLUSION

An 11-gene model was constructed based on CAF-related genes to predict LUAD prognosis. This model represented an independent prognostic factor for LUAD.

A novel prognostic model to predict OS and DFS of stage II/III gastric adenocarcinoma patients in China

Background

The prognosis of advanced gastric adenocarcinoma (GAC) after radical gastrectomy varies greatly. We aimed to build and validate a novel individualized nomogram based on inflammation index and tumor markers for patients with stage II/III GAC.

Methods

A total of 755 individuals with stage II/III GAC who had undergone radical gastrectomy at the First Affiliated Hospital of Zhengzhou University between 2012 and 2017 were included in this retrospective study. The patients were randomly divided into a training cohort (n ​= ​503) and a validation cohort (n ​= ​252). Univariate and multivariate analyses were used to determine independent prognostic factors of overall survival (OS) and disease-free survival (DFS). A nomogram was developed based on these independent factors. The concordance index (C-index) and calibration curves were used to evaluate the predictive accuracy of the nomogram.

Results

Univariate and multivariate analyses demonstrated that older age, poor differentiation, advanced stage, elevated neutrophil-to-lymphocyte ratio (NLR), lower hemoglobin, and high carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19-9) levels were significantly associated with lower OS and DFS and were independent prognostic factors in stage II/III GAC. The nomogram developed based on these factors in the training cohort showed excellent calibration and discrimination (OS: C-index ​= ​0.739, 95% CI ​= ​0.706-0.772; DFS: C-index ​= ​0.735, 95% CI ​= ​0.702-0.769). In the internal validation cohort, the nomogram was also well-calibrated for the prediction of OS and DFS; it was superior to the 8th edition UICC/AJCC TNM staging system (for OS: C-index ​= ​0.746 vs. 0.679, respectively; for DFS: C-index ​= ​0.736 vs. 0.675, respectively; P ​< ​0.001).

Conclusion

The nomogram model could reliably predict OS and DFS in stage II/III gastric cancer patients with radical gastrectomy. It may help physicians make better treatment decisions.

Identification and validation of a hypoxia-immune signature for overall survival prediction in lung adenocarcinoma

Objective: The interaction between immunity and hypoxia in tumor microenvironment (TME) has clinical significance, and this study aims to explore immune-hypoxia related biomarkers in LUAD to guide accurate prognosis of patients.

Methods: The LUAD gene expression dataset was downloaded from GEO and TCGA databases. The immune-related genes and hypoxia-related genes were acquired from ImmPort and MSigDB databases, respectively. Genes related to immune and hypoxia in LUAD were obtained by intersection. The significantly prognostic genes in LUAD were obtained by LASSO and Cox regression analyses and a prognostic model was constructed. Kaplan-Meier and receiver operating characteristic curves were generated to evaluate and validate model reliability. Single-sample gene set enrichment analysis (ssGSEA) and gene set variation analysis (GSVA) were employed to analyze immune cell infiltration and pathway differences between high- and low-risk groups. Nomogram and calibration curves for survival curve and clinical features were drawn to measure prognostic value of the model.

Results: The prognosis model of LUAD was constructed based on seven immune-hypoxia related genes: S100P, S100A16, PGK1, TNFSF11, ARRB1, NCR3, and TSLP. Survival analysis revealed a poor prognosis in high-risk group. ssGSEA result suggested that activities of immune cells in high-risk group was remarkably lower than in low-risk group, and GSVA result showed that immune-related pathway was notably activated in low-risk group.

Conclusion: Immune-hypoxia related genes were found to be prognostic biomarkers for LUAD patients, based on which a 7-immune-hypoxia related gene-signature was constructed. This model can assess immune status of LUAD patients, and provide clinical reference for individualized prognosis, treatment and follow-up of LUAD patients.

Development and validation of a nomogram based on neutrophil-to-lymphocyte ratio and fibrinogen-to-lymphocyte ratio for predicting recurrence of colorectal adenoma

Background

There are many risk factors for the recurrence of colorectal adenoma (CRA). The purpose of this study was to explore the predictive performance of fibrinogen-to-lymphocyte ratio (FLR) and neutrophil-to-lymphocyte ratio (NLR) on the recurrence of CRA and to construct a predictive model.

Methods

This study analyzed the clinicopathological features of 421 CRA patients who underwent colonoscopy and adenectomy, and evaluated the recurrence of polyps under colonoscopy. Among them, 301 were training cohort and 120 were validation cohort. Multivariate logistic regression was used to identify independent risk factors associated with CRA recurrence. Established a nomogram model to predict the risk of recurrence in CRA patients using independent risk factors. The receiver operating characteristic (ROC) curves were used to verify the nomogram model discrimination. Calibration curves were used to verify the model calibration degree. The decision curve analysis (DCA) curves were used to verify the clinical efficacy of the nomogram model.

Results

Totally, six independent predictors, including smoking, diabetes, adenoma number, adenoma size, NLR, and FLR, were enrolled in the nomogram. In the training cohort and validation cohort, the area under the curve (AUC) of the nomogram for predicting the risk of CRA recurrence was 0.846 and 0.841, respectively. The calibration curves displayed a good agreement. DCA curves showed that this model had a high net clinical benefit.

Conclusions

Smoking, diabetes, adenoma number, adenoma size, NLR, and FLR were influencing factors for CRA recurrence.

Construction of Prognostic Risk Model for Small Cell Lung Cancer Based on Immune-Related Genes

Small cell lung cancer (SCLC) is a highly invasive and fatal malignancy. Research at the present stage implied that the expression of immune-related genes is associated with the prognosis in SCLC. Accordingly, it is essential to explore effective immune-related molecular markers to judge prognosis and treat SCLC. Our research obtained SCLC dataset from Gene Expression Omnibus (GEO) and subjected mRNAs in it to differential expression analysis. Differentially expressed mRNAs (DEmRNAs) were intersected with immune-related genes to yield immune-related differentially expressed genes (DEGs). The functions of these DEGs were revealed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Thereafter, we categorized 3 subtypes of immune-related DEGs via K-means clustering. Kaplan-Meier curves analyzed the effects of 3 subtypes on SCLC patients' survival. Single-sample gene set enrichment analysis (ssGSEA) and ESTIMATE validated that the activation of different immune gene subtypes differed significantly. Finally, an immune-related-7-gene assessment model was constructed by univariate-Lasso-multiple Cox regression analyses. Riskscores, Kaplan-Meier curves, receiver operating characteristic (ROC) curves, and independent prognostic analyses validated the prognostic value of the immune-related-7-gene assessment model. As suggested by GSEA, there was a prominent difference in cytokine-related pathways between high- and low-risk groups. As the analysis went further, we discovered a statistically significant difference in the expression of human leukocyte antigen (HLA) proteins and costimulatory molecules expressed on the surface of CD274, CD152, and T lymphocytes in different groups. In a word, we started with immune-related genes to construct the prognostic model for SCLC, which could effectively evaluate the clinical outcomes and offer guidance for the treatment and prognosis of SCLC patients.

Preoperative low absolute lymphocyte count to fibrinogen ratio correlated with poor survival in nonmetastatic colorectal cancer

Background

Preoperative absolute lymphocyte count (LC) and fibrinogen (FIB) are useful prognostic indicators in colorectal cancer (CRC). However, the prognostic value of the LC to FIB ratio (LFR) has never been addressed.

Methods

A total of 189 nonmetastatic CRC patients after resection were enrolled retrospectively. The significance of the LFR in predicting disease-free survival (DFS) and overall survival (OS) was estimated by receiver operating characteristic curve analysis, and the prognostic efficacy was compared with individual LC and FIB. Patients were assigned to LFR low or high subgroups. Differences in clinicopathological features among these subgroups were calculated, and the survival differences of these subgroups were determined by the Kaplan-Meier analysis. A Cox proportional hazards model was applied to test the risk factors for survival.

Results

Taking 0.54 as the optimal cutoff point, the LFR had sensitivities of 79.70% and 86.40% and specificities of 52.30% and 51.00% in predicting the DFS and OS, respectively. A total of 109/189 (57.67%) patients were assigned to the LFR low group, and these patients were more likely to be characterized by criteria such as T₃ + T₄ (P < 0.01), stage 3 (P < 0.01), tumor deposits (P = 0.01), high CEA (P < 0.01), or CA19-9 levels (P = 0.04). And they also displayed worse DFS (log rank = 18.57, P < 0.01) and OS (log rank = 20.40, P < 0.01) than the high LFR group. Finally, the LFR was independently associated with inferior DFS (HR = 0.32, 95% CI: 0.16–0.61, P < 0.01) and OS (HR = 0.23, 95% CI: 0.09–0.55, P < 0.01).

Conclusions

The LFR is a useful prognostic indicator in nonmetastatic CRC, and patients with a relatively low LFR had poor survival.

Analysis of gastrointestinal function and prognostic value of tumor markers in patients with laparoscopic radical resection of colorectal cancer

OBJECTIVE

To analyze the gastrointestinal function and prognostic value of tumor markers (TMs) in patients with laparoscopic radical resection of colorectal cancer (LRRCC).

METHODS

The research population of this retrospective study comprised 141 patients with CC who received treatment in the Zhongshan Hospital of Xiamen University between July 2017 and August 2018, including 74 cases (observation group, OG) treated with LRRCC and 67 cases (control group, CG) undergoing open surgery (OS). Postoperative gastrointestinal function and complications were recorded. Besides, alterations in serum TMs carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19-9), and the 3-years survival of patients were observed. Receiver operating characteristic (ROC) curves were used to determine the prognostic value of TMs. Risk factors affecting the prognosis of LRRCC patients were analyzed by the Cox regression model.

RESULTS

Significantly higher levels of motilin (MOT) and gastrin (GT) were determined in OG compared with CG. The two groups showed no notable difference in the postoperative complication rate. Postoperative serum CEA and CA199 levels were obviously lower in OG as compared with CG. A higher 3-year survival rate was determined in OG. The areas under the receiver operating characteristic (ROC) curve (AUCs) of CEA and CA19-9 levels in predicting patients' 3-year survival were 0.826 and 0.867, respectively. According to the Cox regression analysis, tumor diameter, lymph node involvement, TNM staging, vascular invasion, CEA, and CA19-9 were independent risk factors for poor prognosis of LRRCC patients.

CONCLUSIONS

LRRCC is well-tolerated by patients with CC and contributes to favorable outcomes. Besides, CEA and CA19-9, the two TMs, may be candidate prognostic markers for patients undergoing LRRCC.

Identification and Verification of an Alternative Polyadenylation-Related lncRNA Prognostic Signature for Glioma

Due to the high mortality and modality of glioma, it was urgently needed to develop a glioma prognostic assessment system. Previous studies demonstrated that alternative polyadenylation- (APA-) related genes are important in immune response and oncogenesis. mRNA and lncRNA expression information of glioma samples were acquired from CGGA and TCGA databases, and lncRNAs associated with APA were selected through correlation analysis. The prognosis model of APA-related lncRNAs was built by the univariate Cox, random forest, and univariate Cox regression analyses. Glioma samples were assigned into high- and low-risk groups. Independence and effectiveness of the prognostic model were evaluated by Kaplan-Meier analysis, ROC curve, and Cox regression analyses. GO, KEGG enrichment, and GSEA analyses showed that the mainly involved signaling pathways were enriched in cellular immunity and immune signal transduction. We further analyzed expression differences of negative immune regulatory genes and immune cell infiltration degree between two groups. Immune checkpoints CTLA4 and LAG3 and immune suppressors TGFB, IL10, NOS3, and IDO1 and immune cell infiltration were notably upregulated in the high-risk group. The PD1/PDL1 expression was significantly correlated with risk score, showing that the prognostic model of APA-related lncRNA could effectively assess the tumor immune suppression. In conclusion, we established a risk assessment model of APA-related lncRNA in glioma, which could effectively evaluate prognosis of patients with glioma and tumor immune suppression and could provide guidance for clinical treatment.

Construction of Prognostic Risk Model of Patients with Skin Cutaneous Melanoma Based on TCGA-SKCM Methylation Cohort

Skin cutaneous melanoma (SKCM) is a common malignant skin cancer. Early diagnosis could effectively reduce SKCM patient's mortality to a large extent. We managed to construct a model to examine the prognosis of SKCM patients. The methylation-related data and clinical data of The Cancer Gene Atlas- (TCGA-) SKCM were downloaded from TCGA database. After preprocessing the methylation data, 21,861 prognosis-related methylated sites potentially associated with prognosis were obtained using the univariate Cox regression analysis and multivariate Cox regression analysis. Afterward, unsupervised clustering was used to divide the patients into 4 clusters, and weighted correlation network analysis (WGCNA) was applied to construct coexpression modules. By overlapping the CpG sites between the clusters and turquoise model, a prognostic model was established by LASSO Cox regression and multivariate Cox regression. It was found that 9 methylated sites included cg01447831, cg14845689, cg20895058, cg06506470, cg09558315, cg06373660, cg17737409, cg21577036, and cg22337438. After constructing the prognostic model, the performance of the model was validated by survival analysis and receiver operating characteristic (ROC) curve, and the independence of the model was verified by univariate and multivariate regression. It was represented that the prognostic model was reliable, and riskscore could be used as an independent prognostic factor in SKCM patients. At last, we combined clinical data and patient's riskscore to establish and testify the nomogram that could determine patient's prognosis. The results found that the reliability of the nomogram was relatively good. All in all, we constructed a prognostic model that could determine the prognosis of SKCM patients and screened 9 key methylated sites through analyzing data in TCGA-SKCM dataset. Finally, a prognostic nomogram was established combined with clinical diagnosed information and riskscore. The results are significant for improving the prognosis of SKCM patients in the future.