Development and validation of a machine learning-based 18F-fluorodeoxyglucose PET/CT radiomics signature for predicting gastric cancer survival.

BACKGROUND: Survival prognosis of patients with gastric cancer (GC) often influences physicians' choice of their follow-up treatment. This study aimed to develop a positron emission tomography (PET)-based radiomics model combined with clinical tumor-node-metastasis (TNM) staging to predict overall survival (OS) in patients with GC. METHODS: We reviewed the clinical information of a total of 327 patients with pathological confirmation of GC undergoing 18 F-fluorodeoxyglucose (18 F-FDG) PET scans. The patients were randomly classified into training (n = 229) and validation (n = 98) cohorts. We extracted 171 PET radiomics features from the PET images and determined the PET radiomics scores (RS) using the least absolute shrinkage and selection operator (LASSO) and random survival forest (RSF). A radiomics model, including PET RS and clinical TNM staging, was constructed to predict the OS of patients with GC. This model was evaluated for discrimination, calibration, and clinical usefulness. RESULTS: On multivariate COX regression analysis, the difference between age, carcinoembryonic antigen (CEA), clinical TNM, and PET RS in GC patients was statistically significant (p < 0.05). A radiomics model was developed based on the results of COX regression. The model had the Harrell's concordance index (C-index) of 0.817 in the training cohort and 0.707 in the validation cohort and performed better than a single clinical model and a model with clinical features combined with clinical TNM staging. Further analyses showed higher PET RS in patients who were older (p < 0.001) and those who had elevated CEA (p < 0.001) and higher clinical TNM (p < 0.001). At different clinical TNM stages, a higher PET RS was associated with a worse survival prognosis. CONCLUSIONS: Radiomics models based on PET RS, clinical TNM, and clinical features may provide new tools for predicting OS in patients with GC.

Development and external validation of a prognostic nomogram to predict survival in patients aged ≥60 years with pancreatic ductal adenocarcinoma.

Background: Pancreatic ductal adenocarcinoma (PDAC), which accounts for the vast majority of pancreatic cancer (PC), is a highly aggressive malignancy with a dismal prognosis. Age is shown to be an independent factor affecting survival outcomes in patients with PDAC. Our study aimed to identify prognostic factors and construct a nomogram to predict survival in PDAC patients aged ≥60 years. Methods: Data of PDAC patients aged ≥60 years were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Multivariate Cox regression analysis was used to determined prognostic factors of overall survival (OS) and cancer-specific survival (CSS), and two nomograms were constructed and validated by calibration plots, concordance index (C-index) and decision curve analysis (DCA). Additionally, 432 patients from the First Affiliated Hospital of Wenzhou Medical University were included as an external cohort. Kaplan-Meier curves were applied to further verify the clinical validity of the nomograms. Results: Ten independent prognostic factors were identified to establish the nomograms. The C-indexes of the training and validation groups based on the OS nomogram were 0.759 and 0.760, higher than those of the tumor-node-metastasis (TNM) staging system (0.638 and 0.636, respectively). Calibration curves showed high consistency between predictions and observations. Better area under the receiver operator characteristic (ROC) curve (AUC) values and DCA were also obtained compared to the TNM system. The risk stratification based on the nomogram could distinguish patients with different survival risks. Conclusions: We constructed and externally validated a population-based survival-predicting nomogram for PDAC patients aged ≥60 years. The new model could help clinicians personalize survival prediction and risk assessment.

Survival Analysis of Elderly Patients With Laryngeal Cancer After Total Laryngectomy: A Retrospective Cohort Study.

Objective This study investigates the overall survival (OS) of elderly patients who underwent total laryngectomy for laryngeal cancer (LC) and examines the impact of tumor-node-metastasis (TNM) staging on survival rates. Methods A retrospective cohort study utilized data from the Otorhinolaryngology Clinic at the University Hospital of Patras, including 75 elderly patients (>65 years) who underwent total laryngectomy for LC between 2000 and 2015. Survival analysis was performed using the Kaplan-Meier estimator, with comparisons made using the Log-rank test. Statistical significance was defined as the p-value being less than or equal to 0.05. Results Over the 16-year period, new LC cases were predominantly male (97.3%) with a mean age of 73.88 years (range: 65-89 years). Most patients were smokers (96%) and alcohol users (54.7%). Histologically, 18.7% of tumors were classified as poorly differentiated, 65.3% as moderately differentiated and 16% as well differentiated. Post-surgical TNM staging indicated 10.7% stage II, 37.3% stage III and 52% stage IV, primarily located in the glottis (62.7%) and followed by supraglottis (34.7%). All patients underwent total laryngectomy, with 69.3% and 37.3% receiving neck dissection and adjuvant therapy (chemotherapy or radiotherapy), respectively. During follow-up, 39 patients died, with 74.3% due to disease-related causes. Five-year OS rates were 44.6%, with variations by stage (stage II: 62.5%, stage III: 55.8%, stage IV: 32.4%; p=0.039) and age (65-75 years: 51.7%, >75 years: 34.7%; p=0.039). Conclusions TNM staging of the laryngeal cancer significantly influences the overall survival of elderly patients undergoing total laryngectomy for LC. Early diagnosis of the disease is crucial for patient survival.

PLK4 reflects extrathyroidal invasion, high tumor stage and poor prognosis in papillary thyroid carcinoma patients.

Objective: This study aimed to assess the value of PLK4 as a biomarker in papillary thyroid carcinoma (PTC). Methods: This study reviewed 230 PTC patients receiving surgical resections. PLK4 was detected in tumor tissues and samples of normal thyroid gland tissues by immunohistochemistry. Results: PLK4 was elevated in tumor tissues versus normal thyroid gland tissues (p < 0.001). Tumor PLK4 was linked with extrathyroidal invasion (p = 0.036), higher pathological tumor stage (p = 0.030), node stage (p = 0.045) and tumor/node/metastasis stage (p = 0.022) in PTC patients. Tumor PLK4 immunohistochemistry score >3 was linked with shortened disease-free survival (p = 0.026) and overall survival (p = 0.028) and independently predicted poorer disease-free survival (hazard ratio: 2.797; p = 0.040). Conclusion: Tumor PLK4 reflects extrathyroidal invasion, higher tumor stage and shortened survival in PTC.

Machine learning model to preoperatively predict T2/T3 staging of laryngeal and hypopharyngeal cancer based on the CT radiomic signature.

OBJECTIVES: To develop and assess a radiomics-based prediction model for distinguishing T2/T3 staging of laryngeal and hypopharyngeal squamous cell carcinoma (LHSCC) METHODS: A total of 118 patients with pathologically proven LHSCC were enrolled in this retrospective study. We performed feature processing based on 851 radiomic features derived from contrast-enhanced CT images and established multiple radiomic models by combining three feature selection methods and seven machine learning classifiers. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were used to assess the performance of the models. The radiomic signature obtained from the optimal model and statistically significant morphological image characteristics were incorporated into the predictive nomogram. The performance of the nomogram was assessed by calibration curve and decision curve analysis. RESULTS: Using analysis of variance (ANOVA) feature selection and logistic regression (LR) classifier produced the best model. The AUCs of the training, validation, and test sets were 0.919, 0.857, and 0.817, respectively. A nomogram based on the model integrating the radiomic signature and a morphological imaging characteristic (suspicious thyroid cartilage invasion) exhibited C-indexes of 0.899 (95% confidence interval (CI) 0.843-0.955), fitting well in calibration curves (p > 0.05). Decision curve analysis further confirmed the clinical usefulness of the nomogram. CONCLUSIONS: The nomogram based on the radiomics model derived from contrast-enhanced CT images had good diagnostic performance for distinguishing T2/T3 staging of LHSCC. CLINICAL RELEVANCE STATEMENT: Accurate T2/T3 staging assessment of LHSCC aids in determining whether laryngectomy or laryngeal preservation therapy should be performed. The nomogram based on the radiomics model derived from contrast-enhanced CT images has the potential to predict the T2/T3 staging of LHSCC, which can provide a non-invasive and robust approach for guiding the optimization of clinical decision-making. KEY POINTS: • Combining analysis of variance with logistic regression yielded the optimal radiomic model. • A nomogram based on the CT-radiomic signature has good performance for differentiating T2 from T3 staging of laryngeal and hypopharyngeal squamous cell carcinoma. • It provides a non-invasive and robust approach for guiding the optimization of clinical decision-making.

Data-driven optimization of version 9 American Joint Committee on Cancer staging system for anal cancer.

INTRODUCTION: The American Joint Committee on Cancer (AJCC) staging system undergoes periodic revisions to maintain contemporary survival outcomes related to stage. Recently, the AJCC has developed a novel, systematic approach incorporating survival data to refine stage groupings. The objective of this study was to demonstrate data-driven optimization of the version 9 AJCC staging system for anal cancer assessed through a defined validation approach. METHODS: The National Cancer Database was queried for patients diagnosed with anal cancer in 2012 through 2017. Kaplan-Meier methods analyzed 5-year survival by individual clinical T category, N category, M category, and overall stage. Cox proportional hazards models validated overall survival of the revised TNM stage groupings. RESULTS: Overall, 24,328 cases of anal cancer were included. Evaluation of the 8th edition AJCC stage groups demonstrated a lack of hierarchical prognostic order. Survival at 5 years for stage I was 84.4%, 77.4% for stage IIA, and 63.7% for stage IIB; however, stage IIIA disease demonstrated a 73.0% survival, followed by 58.4% for stage IIIB, 59.9% for stage IIIC, and 22.5% for stage IV (p <.001). Thus, stage IIB was redefined as T1-2N1M0, whereas Stage IIIA was redefined as T3N0-1M0. Reevaluation of 5-year survival based on data-informed stage groupings now demonstrates hierarchical prognostic order and validated via Cox proportional hazards models. CONCLUSION: The 8th edition AJCC survival data demonstrated a lack of hierarchical prognostic order and informed revised stage groupings in the version 9 AJCC staging system for anal cancer. Thus, a validated data-driven optimization approach can be implemented for staging revisions across all disease sites moving forward.

CT radiomics in the identification of preoperative understaging in patients with clinical stage T1-2N0 esophageal squamous cell carcinoma.

Background: Predicting preoperative understaging in patients with clinical stage T1-2N0 (cT1-2N0) esophageal squamous cell carcinoma (ESCC) is critical to customizing patient treatment. Radiomics analysis can provide additional information that reflects potential biological heterogeneity based on computed tomography (CT) images. However, to the best of our knowledge, no studies have focused on identifying CT radiomics features to predict preoperative understaging in patients with cT1-2N0 ESCC. Thus, we sought to develop a CT-based radiomics model to predict preoperative understaging in patients with cT1-2N0 esophageal cancer, and to explore the value of the model in disease-free survival (DFS) prediction. Methods: A total of 196 patients who underwent radical surgery for cT1-2N0 ESCC were retrospectively recruited from two hospitals. Among the 196 patients, 134 from Peking University Cancer Hospital were included in the training cohort, and 62 from Henan Cancer Hospital were included in the external validation cohort. Radiomics features were extracted from patients' CT images. Least absolute shrinkage and selection operator (LASSO) regression was used for feature selection and model construction. A clinical model was also built based on clinical characteristics, and the tumor size [the length, thickness and the thickness-to-length ratio (TLR)] was evaluated on the CT images. A radiomics nomogram was established based on multivariate logistic regression. The diagnostic performance of the models in predicting preoperative understaging was assessed by the area under the receiver operating characteristic curve (AUC). Kaplan-Meier curves with the log-rank test were employed to analyze the correlation between the nomogram and DFS. Results: Of the patients, 50.0% (67/134) and 51.6% (32/62) were understaged in the training and validation groups, respectively. The radiomics scores and the TLRs of the tumors were included in the nomogram. The AUCs of the nomogram for predicting preoperative understaging were 0.874 [95% confidence interval (CI): 0.815-0.933] in the training cohort and 0.812 (95% CI: 0.703-0.912) in the external validation cohort. The diagnostic performance of the nomogram was superior to that of the clinical model (P<0.05). The nomogram was an independent predictor of DFS in patients with cT1-2N0 ESCC. Conclusions: The proposed CT-based radiomics model could be used to predict preoperative understaging in patients with cT1-2N0 ESCC who have undergone radical surgery.

Clinicopathological characteristics correlated with programmed cell death-ligand 1 expression in advanced lung adenocarcinoma.

Background: Recent studies have shown that immune checkpoint inhibitors (ICIs) targeting programmed cell death-ligand 1 (PD-L1) have potential benefits in patients with non-small cell lung cancer (NSCLC) subgroups, while the clinicopathological characteristics associated with PD-L1 expression have not been well established. The purpose of this study was to detect the expression level of PD-L1 in tumor tissues of patients with advanced lung adenocarcinoma (ADC) and analyze its possible relationship with clinicopathological characteristics, so as to identify the predictors of PD-L1 expression. Methods: This retrospective study was conducted by analyzing the clinicopathological and imaging characteristics of hospitalized advanced lung ADC patients with PD-L1 available data and admitted to the respiratory department of our hospital. The expression level of PD-L1 in fresh-frozen tumor tissue samples of 136 advanced ADC patients was analyzed by immunohistochemistry. The patients were divided into positive and negative groups based on a cut-off of 1% PD-L1 expression level. Subsequently, the significant correlation between PD-L1 levels and clinicopathological features were evaluated. The predictive performance of clinicopathological characteristics on PD-L1 expression was evaluated and the optimal cut-off values were identified by plotting the receiver operating characteristic (ROC) curve. Results: The expression level of PD-L1 was related to sex, clinical stage, serum carcinoembryonic antigen (CEA), neuron specific enolase (NSE), white blood cell (WBC), and tumor (T) and metastasis (M) stage. Multivariate logistic regression analysis showed the CEA, NSE, T stage, and WBC were independent predictors of PD-L1 positive expression in lung ADC patients. The ROC curve suggested the model combining CEA with NSE [area under the curve (AUC) =0.815] could better predict the expression levels of PD-L1. The optimal cut-off values for identifying advanced lung ADC patients with PD-L1 positive were CEA ≤13.38 ng/mL and NSE ≤42.35 ng/mL, with sensitivity and specificity of 85.4% and 55.6%, and 92.7% and 32.1%, respectively. Conclusions: Some commonly used clinicopathological features are related to the histological expression of PD-L1. The serum CEA, NSE, T stage, and WBC values can be used as indicators to predict the expression level of PD-L1 in advanced lung ADC, and are used as predictors to evaluate the efficacy of ICIs before treatment.

The tumor-stroma ratio and the immune microenvironment improve the prognostic prediction of pancreatic ductal adenocarcinoma.

Tumor-infiltrating immune cells and fibroblasts are significant components of the tumor microenvironment (TME) of pancreatic ductal adenocarcinoma (PDAC), and they participate in tumor progression as closely as tumor cells. However, the relationship between the features of the TME and patient outcomes and the interactions among TME components are still unclear. In this study, we evaluated the PDAC TME in terms of the quantity and location of cluster of differentiation (CD)4+ T cells, CD8+ T cells, macrophages, stromal maturity, and tumor-stroma ratio (TSR), as evaluated by immunohistochemical staining of serial whole-tissue sections from 116 patients with PDAC. The density of T cells and macrophages (mainly activated macrophages) was significantly higher at the invasive margins (IMs) than at the tumor center (TC). CD4+ T cells were significantly association with all the other tumor-associated immune cells (TAIs) including CD8, CD68 and CD206 positive cells. Tumors of the non-mature (intermediate and immature) stroma type harbored significantly more CD8+ T cells at the IMs and more CD68+ macrophages at the IMs and the TC. The density of CD4+, CD8+, and CD206+ cells at the TC; CD206+ cells at the IMs; and tumor-node-metastasis (TNM) staging were independent risk factors for patient outcomes, and the c-index of the risk nomogram for predicting the survival probability based on the TME features and TNM staging was 0.772 (95% confidence interval: 0.713-0.832). PDAC harbored a significantly immunosuppressive TME, of which the IMs were the hot zones for TAIs, while cells at the TC were more predictive of prognosis. Our results indicated that the model based on the features of the TME and TNM staging could predict patient outcomes.

Survival outcomes used to generate version 9 American Joint Committee on Cancer staging system for anal cancer.

The American Joint Committee on Cancer (AJCC) staging system for all cancer sites, including anal cancer, is the standard for cancer staging in the United States. The AJCC staging criteria are dynamic, and periodic updates are conducted to optimize AJCC staging definitions through a panel of experts charged with evaluating new evidence to implement changes. With greater availability of large data sets, the AJCC has since restructured and updated its processes, incorporating prospectively collected data to validate stage group revisions in the version 9 AJCC staging system, including anal cancer. Survival analysis using AJCC eighth edition staging guidelines revealed a lack of hierarchical order in which stage IIIA anal cancer was associated with a better prognosis than stage IIB disease, suggesting that, for anal cancer, tumor (T) category has a greater effect on survival than lymph node (N) category. Accordingly, version 9 stage groups have been appropriately adjusted to reflect contemporary long-term outcomes. This article highlights the changes to the now published AJCC staging system for anal cancer, which: (1) redefined stage IIB as T1-T2N1M0 disease, (2) redefined stage IIIA as T3N0-N1M0 disease, and (3) eliminated stage 0 disease from its guidelines altogether.

Advances in post-operative prognostic models for hepatocellular carcinoma. / è‚ç»†èƒžç™Œæœ¯åŽé¢„åŽæ¨¡åž‹çš„ç ”ç©¶è¿›å±•.

Hepatocellular carcinoma (HCC) is one of the most common malignancies and a leading cause of cancer-related death worldwide. Surgery remains the primary and most successful therapy option for the treatment of early- and mid-stage HCCs, but the high heterogeneity of HCC renders prognostic prediction challenging. The construction of relevant prognostic models helps to stratify the prognosis of surgically treated patients and guide personalized clinical decision-making, thereby improving patient survival rates. Currently, the prognostic assessment of HCC is based on several commonly used staging systems, such as Tumor-Node-Metastasis (TNM), Cancer of the Liver Italian Program (CLIP), and Barcelona Clinic Liver Cancer (BCLC). Given the insufficiency of these staging systems and the aim to improve the accuracy of prognostic prediction, researchers have incorporated further prognostic factors, such as microvascular infiltration, and proposed some new prognostic models for HCC. To provide insights into the prospects of clinical oncology research, this review describes the commonly used HCC staging systems and new models proposed in recent years.

ALDH2 as a potential stem cell-related biomarker in lung adenocarcinoma: Comprehensive multi-omics analysis.

Lung adenocarcinoma (LUAD) is the most prevalent lung cancer and one of the leading causes of death. Previous research found a link between LUAD and Aldehyde Dehydrogenase 2 (ALDH2), a member of aldehyde dehydrogenase gene (ALDH) superfamily. In this study, we identified additional useful prognostic markers for early LUAD identification and targeting LUAD therapy by analyzing the expression level, epigenetic mechanism, and signaling activities of ALDH2 in LUAD patients. The obtained results demonstrated that ALDH2 gene and protein expression significantly downregulated in LUAD patient samples. Furthermore, The American Joint Committee on Cancer (AJCC) reported that diminished ALDH2 expression was closely linked to worse overall survival (OS) in different stages of LUAD. Considerably, ALDH2 showed aberrant DNA methylation status in LUAD cancer. ALDH2 was found to be downregulated in the proteomic expression profile of several cell biology signaling pathways, particularly stem cell-related pathways. Finally, the relationship of ALDH2 activity with stem cell-related factors and immune system were reported. In conclusion, the downregulation of ALDH2, abnormal DNA methylation, and the consequent deficit of stemness signaling pathways are relevant prognostic and therapeutic markers in LUAD.

Tumor deposits in colorectal cancer: Refining their definition in the TNM system.

Tumor deposits (TDs) are discontinuous tumor spread in the mesocolon/mesorectum which is found in approximately 20% of colorectal cancer (CRC) and negatively affects survival. We have a history of repeated revisions on TD definition and categorization in the tumor-node-metastasis (TNM) system leading to stage migration. Since 1997, TDs have been categorized as T or N factors depending on their size (TNM5) or contour (TNM6). In 2009, TNM7 provided the category of N1c for TDs in a case without positive lymph nodes (LNs), which is also used in TNM8. However, increasing evidence suggests that these revisions are suboptimal and only "partially" successful. Specifically, the N1c rule is certainly useful for oncologists who are having difficulty with TDs in a case with no positive LNs. However, it has failed to maximize the value of the TNM system because of the underused prognostic information of individual TDs. Recently, the potential value of an alternative staging method has been highlighted in several studies using the "counting method." For this method, all nodular type TDs are individually counted together with positive LNs to derive the final pN, yielding a prognostic and diagnostic value that is superior to existing TNM systems. The TNM system has long stuck to the origin of TDs in providing its categorization, but it is time to make way for alternative options and initiate an international discussion on optimal treatment of TDs in tumor staging; otherwise, a proportion of patients end up missing an opportunity to receive the optimal adjuvant treatment.

Combination of Hepatitis B Virus Pre-S2 Gene Deletion Mutation and Tumor-Node-Metastasis Stage Predicts Higher Hepatocellular Carcinoma Recurrence after Curative Surgical Resection.

Hepatocellular carcinoma (HCC) is one of the most frequent and life-threatening human cancers worldwide. Despite curative resection surgery, the high recurrence rate of HCC leads to poor patient survival. Chronic hepatitis B virus (HBV) infection is a major etiological factor for HCC. HBV pre-S2 gene deletion mutation leads to the expression of an important oncoprotein called a pre-S2 mutant. It represents an independent prognostic biomarker for HCC recurrence. This study aimed to identify other independent prognostic biomarkers from clinicopathological characteristics of 75 HBV-related HCC patients receiving resection surgery and to validate their potential to be combined with pre-S2 gene deletion mutation as a combination biomarker for HCC recurrence. Patients with both the presence of pre-S2 gene deletion mutation and tumor-node-metastasis (TNM) stage IIIA-IIIC had a higher HCC recurrence risk than patients with either one or none of these two factors. Moreover, the combination of pre-S2 gene deletion mutation and TNM stage exhibited better performance than either of these two factors alone in discriminating patients from patients without HCC recurrence. Collectively, this study proposed that the TNM stage held significance as a combination biomarker with pre-S2 gene deletion mutation with a greater performance in predicting HCC recurrence after curative surgical resection.

Pathological tumor volume as a simple quantitative predictive factor of survival in oral squamous cell carcinoma.

Oral squamous cell carcinoma (OSCC) is the eighth most common type of cancer in the world. Knowledge of prognostic factors of survival in OSCC is key. Several clinical and pathological prognostic factors have been investigated to develop a prognostic model of survival for patients with oral cancer. The present study focused on the association between pathological tumor volume (PTV) and overall survival time in patients with OSCC, regardless of cervical nodal status. The present study was a prospective study and covered 65 consecutive patients who received surgical treatment for oral cancer. The PTV was calculated according to dimensions of the postoperative specimen. Other pathological parameters as perineural and perivascular tumor spreading and extra-nodular propagation were also determined. The data were analyzed using the IBM SPSS 25.0 software. Cox PH regression model was built to analyze association between the PTV and survival time. Survival time was defined as the period from surgery to a target event or last contact. The results of the present study showed that PTV >4.24 cm3 was significantly associated with shorter overall survival time in patients with OSCC. The PTV value was higher in patients with metastasis and in patients with higher pathological tumor and node stage. In conclusion, PTV was an important pathological prognostic factor for survival in patients with OSCC.

Raman microspectroscopy based TNM staging and grading of breast cancer.

The tumor-node-metastasis (TNM) system is the most common way that doctors determine the anatomical extent of cancer on the basis of clinical and pathological criteria. In this study, a spectral histopathological study has been carried out to bridge Raman micro spectroscopy with the breast cancer TNM system. A total of seventy breast tissue samples, including healthy tissue, early, middle, and advanced cancer, were investigated to provide detailed insights into compositional and structural variations that accompany breast malignant evolution. After evaluating the main spectral variations in all tissue types, the generalized discriminant analysis (GDA) pathological diagnostic model was established to discriminate the TNM staging and grading information. Moreover, micro-Raman images were reconstructed by K-means clustering analysis (KCA) for visualizing the lobular acinar in healthy tissue and ductal structures in all early, middle and advanced breast cancer tissue groups. While, univariate imaging techniques were adapted to describe the distribution differences of biochemical components such as tryptophan, ß-carotene, proteins, and lipids in the scanned regions. The achieved spectral histopathological results not only established a spectra-structure correlations via tissue biochemical profiles but also provided important data and discriminative model references for in vivo Raman-based breast cancer diagnosis.

Advances in post-operative prognostic models for hepatocellular carcinoma / 浙江大学学报（英文版）（B辑：生物医学和生物技术）

Hepatocellular carcinoma (HCC) is one of the most common malignancies and a leading cause of cancer-related death worldwide. Surgery remains the primary and most successful therapy option for the treatment of early- and mid-stage HCCs, but the high heterogeneity of HCC renders prognostic prediction challenging. The construction of relevant prognostic models helps to stratify the prognosis of surgically treated patients and guide personalized clinical decision-making, thereby improving patient survival rates. Currently, the prognostic assessment of HCC is based on several commonly used staging systems, such as Tumor-Node-Metastasis (TNM), Cancer of the Liver Italian Program (CLIP), and Barcelona Clinic Liver Cancer (BCLC). Given the insufficiency of these staging systems and the aim to improve the accuracy of prognostic prediction, researchers have incorporated further prognostic factors, such as microvascular infiltration, and proposed some new prognostic models for HCC. To provide insights into the prospects of clinical oncology research, this review describes the commonly used HCC staging systems and new models proposed in recent years.

In Silico Identification of Genes Associated with Breast Cancer Progression and Prognosis and Novel Therapeutic Targets.

Molecular mechanisms underlying breast cancer (BC) progression are complex and remain unclear. In this study, we used bioinformatic tools to identify genes associated with tumor progression mechanisms and novel therapeutic targets in BC. We identified genes with stepwise upregulated expression overlapping between the T and N stages during BC progression using LinkedOmics. We compared the expression level of each gene in BC tissues with that in normal breast tissues and evaluated differences in expression in their intrinsic subtypes and their prognostic value using UALCAN and GEPIA2. We also investigated the dependency of BC cell lines on these genes and whether they are potential therapeutic targets using DepMap. SPDEF, TRIM3, ABCB9, HSPB1, RHBG, SPINT1, EPN3, LRFN2, and PRPH were found to be involved in BC progression. High expression of ABCB9 and SPINT1 was associated with a poor prognosis. SPDEF, TRIM3, ABCB9, RHBG, SPINT1, and PRPH were found to be essential for survival in some BC cell lines (gene effect score < −0.5). PRPH was newly discovered to be involved in the progression of BC and the growth and survival of BC cell lines. Hence, SPDEF, TRIM3, ABCB9, RHBG, SPINT1, and PRPH may serve as novel potential therapeutic targets in BC.

Insight into Classification and Risk Stratification of Head and Neck Squamous Cell Carcinoma in Era of Emerging Biomarkers with Focus on Histopathologic Parameters.

Tumor-node-metastasis (TNM) staging system is the cornerstone for treatment planning of head and neck squamous cell carcinoma (HNSCC). Many prognostic biomarkers have been introduced as modifiers to further improve the TNM classification of HNSCC. Here, we provide an overview on the use of the recent prognostic biomarkers, with a focus on histopathologic parameters, in improving the risk stratification of HNSCC and their application in the next generation of HNSCC staging systems.

Preoperative neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio are correlated with tumor-node-metastasis stages in patients with non-small cell lung cancer.

Background: The aim of this study is to explore the value of neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) for predicting the tumor-node-metastasis (TNM) stages in non-small cell lung cancer (NSCLC) patients. Methods: This retrospective study included 205 NSCLC patients receiving surgical treatment. We used receiver operating curve analysis to confirm the optimal cutoff values of NLR and PLR. Results: The result showed that the thresholds for NLR and PLR were 1.8 and 103.59, respectively. NLR (P = 0.037; relative risk (RR), 3.027; 95% confidence interval (CI): 1.608-8.581) and PLR (P = 0.001; RR, 3.662; 95% CI: 1.342-9.992) were risks factors in predicting advanced TNM stages (Stage III/IV, all P < 0.05). In addition, NLR with T stage- and N stage-dependent increase may be a potential and independent predictive marker for T and N stage (all P < 0.05); the PLR was identified as a marker for T stage (P = 0.028) but not for N stage. Furthermore, we investigated the combination of NLR and PLR (CNP). A risk stratification based on CNP index was carried out as follows: low risk (NLR ≤1.8 and PLR ≤ 103.59), intermediate risk (either NLR >1.8 or PLR > 103.59), and high risk (both NLR >1.8 and PLR >103.59). The probabilities for developing advanced TNM stage were 6.4% for low, 20.4% for intermediate, and 47.1% for high-risk group (P < 0.001). Conclusion: The levels of preoperative NLR and PLR were capable of indicating advanced TNM stages. According to the CNP index, patients were divided into three risk groups with different significance.