Prognostic Value of Inflammatory Biomarkers in Gastric Cancer Patients and the Construction of a Predictive Model

Inflammation-related signature for prognostic prediction, tumor immune, genomic heterogeneity, and drug choices in prostate cancer: Integrated analysis of bulk and single-cell RNA-sequencing

Background

Prostate cancer (PCa) ranks as the second most prevalent malignancy among males on a global scale. Accumulating evidence suggests that inflammation has an intricate relationship with tumorigenesis, tumor progression and tumor immune microenvironment. However, the overall impact of inflammation-related genes on the clinical prognosis and tumor immunity in PCa remains unclear.

Methods

Machine learning methods were utilized to construct and validate a signature using The Cancer Genome Atlas (TCGA) for training, while the Memorial Sloan Kettering Cancer Center (MSKCC) and GSE70769 cohorts for independent validation. The efficacy of the signature in predicting outcomes and its clinical utility were assessed through a series of investigations encompassing in vitro experiments, survival analysis, and nomogram development. The association between the signature and precision medicine was explored via tumor immunity, genomic heterogeneity, therapeutic response, and molecular docking analyses, using bulk and single-cell RNA-sequencing data.

Results

We identified 7 inflammation-related genes with prognostic significance and developed an inflammation-related prognostic signature (IRPS) with 6 genes. Furthermore, we demonstrated that both the IRPS and a nomogram integrating risk score and pathologic T stage exhibited excellent predictive ability for the survival outcomes in PCa patients. Moreover, the IRPS was found to be significantly associated with the tumor immune, genomic heterogeneity, therapeutic response, and drug selection.

Conclusion

IRPS can serve as a reliable predictor for PCa patients. The signature may provide clinicians with valuable information on the efficacy of therapy and help personalize treatment for PCa patients.

The Prognostic Value of the Prognostic Nutritional Index in Patients with Advanced or Metastatic Gastric Cancer Treated with Immunotherapy

In recent years, the therapeutic effect of monoclonal antibodies against programmed cell death protein-1 (PD-1) in patients with locally advanced or metastatic gastric or gastroesophageal junction (G/GEJ) cancer has been confirmed in many studies. The exploration and discovery of new biomarker combinations based on tumor characteristics and tumor microenvironment help screen superior patients and realize precise immunotherapy. As an evaluation index of immunonutritional status, the prognostic nutritional index (PNI) is low cost, simple and easy to obtain, and effective in determining the prognosis of tumor patients. We selected 268 consecutive AGC patients who were treated with ICI therapy from December 2014 to May 2021. We measured their pretreatment of the PNI levels and performed univariate and multivariate Cox regression analyses of progression-free survival (PFS) or overall survival (OS) after ICI therapy. The low pretreatment PNI level of AGC patients was significantly correlated with shorter PFS (p < 0.001) and OS (p < 0.001) after ICI treatment. In univariate and multivariate analyses of the associations between PNI and OS or PFS, PNI is an independent prognostic factor for PFS (HR = 1.511; 95%CI 1.154-1.977; p = 0.003) and OS (HR = 1.431; 95%CI 1.049-1.951; p = 0.024), respectively. Notably, decreased PNI during treatment with ICIs was associated with early relapse and death. Pretreatment with PNI might help to identify AGC patients who will obtain a survival benefit from ICI therapy.

Risk stratification and prognosis prediction based on inflammation-related gene signature in lung squamous carcinoma

BACKGROUND

Inflammation is known to have an intricate relationship with tumorigenesis and tumor progression while it is also closely related to tumor immune microenvironment. Whereas the role of inflammation-related genes (IRGs) in lung squamous carcinoma (LUSC) is barely understood. Herein, we recognized IRGs associated with overall survival (OS), built an IRGs signature for risk stratification and explored the impact of IRGs on immune infiltration landscape of LUSC patients.

METHODS

The RNA-sequencing and clinicopathological data of LUSC patients were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database, which were defined as training and validation cohorts. Cox regression and least absolute shrinkage and selection operator analyses were performed to build an IRG signature. CIBERSORT, microenvironment cell populations-counter and tumor immune dysfunction and rejection (TIDE) algorithm were used to perform immune infiltration analysis.

RESULTS

A two-IRG signature consisting of KLF6 and SGMS2 was identified according to the training set, which could categorize patients into two different risk groups with distinct OS. Patients in the low-risk group had more anti-tumor immune cells infiltrated while patient with high-risk had lower TIDE score and higher levels of immune checkpoint molecules expressed. The IRG signature was further identified as an independent prognostic factor of OS. Subsequently, a prognostic nomogram including IRG signature, age, and cancer stage was constructed for predicting individualized OS, whose concordance index values were 0.610 (95% CI: 0.568-0.651) in the training set and 0.652 (95% CI: 0.580-0.724) in validation set. Time-dependent receiver operator characteristic curves revealed that the nomogram had higher prediction accuracy compared with the traditional tumor stage alone.

CONCLUSION

The IRG signature was a predictor for patients with LUSC and might serve as a potential indicator of the efficacy of immunotherapy. The nomogram based on the IRG signature showed a relatively good predictive performance in survival.

Increased prognostic value of clinical–reproductive model in Chinese female patients with esophageal squamous cell carcinoma

BACKGROUND

In China, it has been well recognized that some female patients with esophageal squamous cell carcinoma (ESCC) have different overall survival (OS) time, even with the same tumor-node-metastasis (TNM) stage, challenging the prognostic value of the TNM system alone. An effective predictive model is needed to accurately evaluate the prognosis of female ESCC patients.

AIM

To construct a novel prognostic model with clinical and reproductive data for Chinese female patients with ESCC, and to assess the incremental prognostic value of the full model compared with the clinical model and TNM stage.

METHODS

A new prognostic nomogram incorporating clinical and reproductive features was constructed based on univariatie and Cox proportional hazards survival analysis from a training cohort (n = 175). The results were recognized using the internal (n = 111) and independent external (n = 85) validation cohorts. The capability of the clinical–reproductive model was evaluated by Harrell’s concordance index (C-index), Kaplan–Meier curve, time-dependent receiver operating characteristic (ROC), calibration curve and decision curve analysis. The correlations between estrogen response and immune-related pathways and some gene markers of immune cells were analyzed using the TIMER 2.0 database.

RESULTS

A clinical–reproductive model including incidence area, age, tumor differentiation, lymph node metastasis (N) stage, estrogen receptor alpha (ESR1) and beta (ESR2) expression, menopausal age, and pregnancy number was constructed to predict OS in female ESCC patients. Compared to the clinical model and TNM stage, the time-dependent ROC and C-index of the clinical–reproductive model showed a good discriminative ability for predicting 1-, 3-, and 5-years OS in the primary training, internal and external validation sets. Based on the optimal cut-off value of total prognostic scores, patients were classified into high- and low-risk groups with significantly different OS. The estrogen response was significantly associated with p53 and apoptosis pathways in esophageal cancer.

CONCLUSION

The clinical–reproductive prognostic nomogram has an incremental prognostic value compared with the clinical model and TNM stage in predicting OS in Chinese female ESCC patients.

The Role of Pre-treatment Inflammatory Biomarkers in the Prediction of an Early Response to Panitumumab in Metastatic Colorectal Cancer

Background Systemic inflammation is a critical component of the development and progression of several types of cancer. Neutrophil-lymphocyte ratio (NLR) and lymphocyte-monocyte ratio (LMR) are simple, inexpensive, and reliable predictors of the systemic inflammatory response to the therapy in different malignant tumors, including colorectal cancer. Methods Metastatic colorectal cancer (mCRC) patients treated with panitumumab plus chemotherapy at first-line at the medical oncology unit of Fondazione Institute for Research, Hospitalization and Health Care (IRCCS) Policlinico San Matteo di Pavia between January 1st 2016 and February 1st 2021 were retrospectively analyzed. NLR and LMR were divided into two groups (high and low) based on the cut-off points, with the estimation of the prognostic accuracy of NLR for the early treatment response as the primary end-point of this study. Results The receiver operating characteristic (ROC) analysis showed a fair prognostic accuracy of NLR for early treatment response (area under the curve (AUC)=0.76, 95% CI: 0.62-0.89). A slightly lower prognostic accuracy was found for LMR (AUC=0.71, 95% CI: 0.57-0.85). In the univariable proportional hazard Cox model, no effect of NLR on PFS was found (NLRHigh vs. NLRLow HR=1.3; 95% CI: 0.7-2.4, p=0.414). Patients with higher levels of LMR showed a trend towards higher PFS (LMRHigh vs. LMRLow HR=0.4; 95% CI: 0.2-1.1, p=0.066). No association was found between NLR (or LMR) and skin toxicity. Conclusions NLR and LMR may be used as biomarkers of prognostic accuracy for the early treatment response in mCRC patients treated with panitumumab.

Chronic Inflammation and Cancer: The Role of Endothelial Dysfunction and Vascular Inflammation

Long-lasting subclinical inflammation is associated with a wide range of human diseases, particularly at a middle and older age. Recent reports showed that there is a direct causal link between inflammation and cancer development, as several cancers were found to be associated with chronic inflammatory conditions. In patients with cancer, healthy endothelial cells regulate vascular homeostasis, and it is believed that they can limit tumor growth, invasiveness, and metastasis. Conversely, dysfunctional endothelial cells that have been exposed to the inflammatory tumor microenvironment can support cancer progression and metastasis. Dysfunctional endothelial cells can exert these effects via diverse mechanisms, including dysregulated adhesion, permeability, and activation of NF-κB and STAT3 signaling. In this review, we highlight the role of vascular inflammation in predisposition to cancer within the context of two common disease risk factors: obesity and smoking. In addition, we discuss the molecular triggers, pathophysiological mechanisms, and the biological consequences of vascular inflammation during cancer development and metastasis. Finally, we summarize the current therapies and pharmacological agents that target vascular inflammation and endothelial dysfunction.

Prognostic Nomogram for Patients with Radical Surgery for Non-Metastatic Colorectal Cancer Incorporating Hematological Biomarkers and Clinical Characteristics

BACKGROUND

There is a large difference in postoperative survival in patients with non-metastatic colorectal cancer. We aimed to develop nomograms incorporating both hematological biomarkers and clinical characteristics to predict overall survival (OS) in patients with radical surgery for non-metastatic colorectal cancer.

METHODS

A retrospective analysis was performed on date from 508 patients who underwent radical resection of colorectal cancer at the Affiliated Tumor Hospital of Guangxi Medical University from December 2011 to December 2015. Simple random sampling was performed by dividing these patients into a training set (n=355) and validation set(n=153), which yielded a 7:3 ratio in the sample sizes between these groups. Based on COX regression analysis of the results from the training cohort, a nomogram was developed to predict the three-year and five-year overall survival rate, and internal verification was also performed. The nomogram prediction accuracy and discriminating ability were evaluated by Harrell's C-index (C-index), calibration curves and were compared with the colorectal cancer TNM staging system.

RESULTS

We found that age, degree of differentiation, T stage, N stage, neurological invasion, neutrophils, monocytes, HGB, and LDH were independent risk factors for predicting OS in patients with colorectal cancer. In the training cohort, the C index was 0.796 (95% CI: 0.761-0.831). In the validation cohort, the C index was 0.671 (95% CI: 0.656-0.686).The nomogram showed a stronger predictive ability than did TNM staging. Decision curve analysis showed that the nomogram had value in terms of clinical application.

CONCLUSION

Our nomogram combined hematological biomarkers and clinical characteristics and was highly effective in predicting OS in patients with non-metastatic colorectal cancer. Hence, our nomogram may provide a reference tool for clinicians to guide individualized treatment and follow-ups for patients with colorectal cancer.

Development and Validation of a Prognostic Nomogram for Gastric Cancer Based on DNA Methylation-Driven Differentially Expressed Genes

Background/Aims: The incidence of gastric cancer (GC) ranks fifth among common tumors and GC is the third leading cause of cancer-related death worldwide. The aim of this study was to develop and validate a nomogram for predicting the overall survival (OS) of patients with GC. Methods: DNA methylation (DNAm)-driven genes were identified by integrating DNAm and gene expression profiling analyses from The Cancer Genome Atlas (TCGA) GC cohort. Then, a risk score model was built based on Kaplan-Meier (K-M), least absolute shrinkage and selector operation (LASSO), and multivariate Cox regression analyses. After analyzing the clinical parameters, a nomogram was constructed and assessed. Another cohort (GSE62254) was used for external validation. Results: Thirteen differentially expressed DNAm-driven genes were narrowed down to a six-gene signature (PODN, NPY, MICU3, TUBB6 and RHOJ were hypermethylated, and MYO1A was hypomethylated), which was associated with OS (P < 0.05) after survival and LASSO regression analyses. These differentially expressed genes (DEGs) with altered DNAm statuses were included in the prognostic risk score model. The univariate Cox regression analysis indicated that risk score, age, and number of positive lymph nodes were significantly associated with survival time in GC patients. The multivariate Cox regression analysis also indicated that these variables were significant prognostic factors for GC. A nomogram including these variables was constructed, and its performance in predicting the 1-, 3- and 5-year survival outcomes of GC patients was estimated through time-dependent receiver operating characteristic (ROC) curves. In addition, the clinical benefit of this model was revealed by decision curve analysis (DCA). Pathway enrichment analysis suggested that these DNAm-driven genes might impact tumor progression by affecting signaling pathways such as the "ECM RECEPTOR INTERACTION" and "DNA REPLICATION" pathways. Conclusions: The altered status of the DNAm-driven gene signature (PODN, MYO1A, NPY, MICU3, TUBB6 and RHOJ) was significantly associated with the OS of GC patients. A nomogram incorporating risk score, age and number of positive lymph nodes can be conveniently used to facilitate the individualized prediction of OS in patients with GC.

Application of preoperative artificial neural network based on blood biomarkers and clinicopathological parameters for predicting long-term survival of patients with gastric cancer

BACKGROUND

Because of the powerful abilities of self-learning and handling complex biological information, artificial neural network (ANN) models have been widely applied to disease diagnosis, imaging analysis, and prognosis prediction. However, there has been no trained preoperative ANN (preope-ANN) model to preoperatively predict the prognosis of patients with gastric cancer (GC).

AIM

To establish a neural network model that can predict long-term survival of GC patients before surgery to evaluate the tumor condition before the operation.

METHODS

The clinicopathological data of 1608 GC patients treated from January 2011 to April 2015 at the Department of Gastric Surgery, Fujian Medical University Union Hospital were analyzed retrospectively. The patients were randomly divided into a training set (70%) for establishing a preope-ANN model and a testing set (30%). The prognostic evaluation ability of the preope-ANN model was compared with that of the American Joint Commission on Cancer (8^th edition) clinical TNM (cTNM) and pathological TNM (pTNM) staging through the receiver operating characteristic curve, Akaike information criterion index, Harrell's C index, and likelihood ratio chi-square.

RESULTS

We used the variables that were statistically significant factors for the 3-year overall survival as input-layer variables to develop a preope-ANN in the training set. The survival curves within each score of the preope-ANN had good discrimination (P < 0.05). Comparing the preope-ANN model, cTNM, and pTNM in both the training and testing sets, the preope-ANN model was superior to cTNM in predictive discrimination (C index), predictive homogeneity (likelihood ratio chi-square), and prediction accuracy (area under the curve). The prediction efficiency of the preope-ANN model is similar to that of pTNM.

CONCLUSION

The preope-ANN model can accurately predict the long-term survival of GC patients, and its predictive efficiency is not inferior to that of pTNM stage.

Evaluating and validating the predictive ability of preoperative systemic inflammatory/immune cells in gastric cancer following R0 resection

The present study aimed to compare the predictive abilities of preoperative systemic inflammatory/immune cell ratios in gastric cancer (GC) following curative R0 resection, and to screen the optimal parameter incorporated into nomograms to predict the postoperative overall survival (OS) and recurrence-free survival (RFS). A total of 679 patients with GC were included in the study, divided into a primary cohort (300 cases), an internal validation cohort (278 cases), and an external validation cohort (101 cases). In the primary cohort, the prognostic abilities of all systemic inflammatory/immune cell accounts or ratios were compared by receiver operating characteristic (ROC) curve analysis. The area under the ROC curve (AUC) of the neutrophil-monocyte-lymphocyte ratio (NMLR) was largest for the prediction of OS (AUC=0.728) and RFS (AUC=0.695). The independent predictive factors for OS or RFS, including NMLR, degree of differentiation (DD), T-stage and N-stage were used to establish the 2 nomograms. The comprehensive predictive power of nomograms was compared with that of the tumor-nodes-metastasis (TNM) staging system and validated by bootstrap resampling. The concordance indexes (C-indexes) of the nomograms for OS [C-index, 0.851; 95% confidence interval (CI), 0.817-0.883] and RFS (C-index, 0.860; 95% CI, 0.831-0.889), were increased compared with those for the DD, the NMLR and the TNM stage. The AUCs of the 2 nomograms (0.933 for OS and 0.944 for RFS) were largest among all predictive scoring systems. In the internal validation cohort, the C-indexes of the nomograms for OS and RFS were 0.840 and 0.916, respectively. In the external validation cohort, the C-indexes of the nomograms for OS and RFS nomograms were 0.827 and 0.891, respectively. The present study demonstrated that the NMLR was an independent prognostic factor for patients with GC. The proposed nomograms were demonstrated to have a good predictive ability with improved sensitivity and accuracy in survival and recurrence in patients with GC undergoing R0 resection.

Prognostic significance of FA score based on plasma fibrinogen and serum albumin in patients with epithelial ovarian cancer

Objective

To evaluate the significance of fibrinogen and albumin (FA) score based on preoperative peripheral blood plasma fibrinogen and serum albumin in the prognosis of patients with epithelial ovarian cancer (EOC).

Methods

Patients' clinicopathological data of 186 cases of EOC were retrospectively collected, and these patients were divided into three groups according to their FA scores (both plasma fibrinogen and serum albumin abnormal were allocated a score of 2; one of them abnormal were allocated a score of 1; neither of them abnormal were allocated a score of 0; optimal cut-off point is taken as the critical point whether the value is abnormal or not). Correlation between FA score in patients with EOC as well as clinicopathological features and overall survival (OS) was analyzed.

Results

(1) Receiver operating characteristic curve showed that the optimal cut-off point of plasma fibrinogen in the preoperative peripheral blood of patients with EOC was 3.63 g/L. The optimal cut-off point for serum albumin level was 42.45 g/L. (2) There was no significant difference in age, tumor size, neutrophil count, lymphocyte count, C reactive protein and preoperative tumor marker CA125 between the three groups (FA score=0, FA score=1, FA score=2) (P>0.05). However, there was statistically significant difference in tumor grade, tumor stage and the presence of lymph node metastasis between different FA scoring groups (P<0.05). (3) Univariate and multivariate analyses showed that tumor size, tumor grade, tumor stage, plasma fibrinogen, serum albumin, FA score and tumor marker CA125 were statistically correlated with OS of EOC patients after surgery (P<0.05). The complex index FA score is superior to the single plasma fibrinogen and serum albumin when it comes to predicting prognosis. (4) FA score can better predict the prognosis of postoperative patients with EOC whose tumor size is ≥6 cm, whose EOC is advanced (stages III-IV) (P=0.0138) and whose tumor stage is medium or high grade (P=0.0005).

Conclusion

FA score is closely related to the clinicopathological characteristics and OS of patients with EOC and is an independent risk factor indicating the prognosis of EOC patients.

Prognostic value of advanced lung cancer inflammation index in head and neck squamous cell carcinoma

PURPOSE

The advanced lung cancer inflammation index (ALI) is a useful tool for prediction of outcome in several malignancies. However, to date, its significance in head and neck cancer patients has not been evaluated.

METHODS

We retrospectively analyzed data from 93 patients who were diagnosed with head and neck squamous cell carcinoma (HNSCC) and treated with surgical resection and postoperative radiotherapy between 2002 and 2012. The aim of this study was to investigate whether the preoperative ALI is a prognostic indicator for disease-free survival and overall survival in HNSCC patients.

RESULTS

A low ALI was significantly associated with a worse 5-year disease-free survival (47.0 vs. 83.5%, p < 0.001), and overall survival (44.4 vs. 73.6%, p = 0.008). Multivariate analysis showed that low ALI was independently associated with disease-free survival (p < 0.001) and overall survival (p = 0.02).

CONCLUSION

The ALI could serve as an easily available prognostic indicator for disease-free and overall survival prediction in patients with HNSCC.