Identification of a Risk Stratification Model to Predict Overall Survival and Surgical Benefit in Clear Cell Renal Cell Carcinoma With Distant Metastasis

Risk factors, prognostic predictors, and nomograms for pancreatic cancer patients with initially diagnosed synchronous liver metastasis

BACKGROUND

Liver metastasis (LM) remains a major cause of cancer-related death in patients with pancreatic cancer (PC) and is associated with a poor prognosis. Therefore, identifying the risk and prognostic factors in PC patients with LM (PCLM) is essential as it may aid in providing timely medical interventions to improve the prognosis of these patients. However, there are limited data on risk and prognostic factors in PCLM patients.

AIM

To investigate the risk and prognostic factors of PCLM and develop corresponding diagnostic and prognostic nomograms.

METHODS

Patients with primary PC diagnosed between 2010 and 2015 were reviewed from the Surveillance, Epidemiology, and Results Database. Risk factors were identified using multivariate logistic regression analysis to develop the diagnostic mode. The least absolute shrinkage and selection operator Cox regression model was used to determine the prognostic factors needed to develop the prognostic model. The performance of the two nomogram models was evaluated using receiver operating characteristic (ROC) curves, calibration plots, decision curve analysis (DCA), and risk subgroup classification. The Kaplan-Meier method with a log-rank test was used for survival analysis.

RESULTS

We enrolled 33459 patients with PC in this study. Of them, 11458 (34.2%) patients had LM at initial diagnosis. Age at diagnosis, primary site, lymph node metastasis, pathological type, tumor size, and pathological grade were identified as independent risk factors for LM in patients with PC. Age > 70 years, adenocarcinoma, poor or anaplastic differentiation, lung metastases, no surgery, and no chemotherapy were the independently associated risk factors for poor prognosis in patients with PCLM. The C- index of diagnostic and prognostic nomograms were 0.731 and 0.753, respectively. The two nomograms could accurately predict the occurrence and prognosis of patients with PCLM based on the observed analysis results of ROC curves, calibration plots, and DCA curves. The prognostic nomogram could stratify patients into prognostic groups and perform well in internal validation.

CONCLUSION

Our study identified the risk and prognostic factors in patients with PCLM and developed corresponding diagnostic and prognostic nomograms to help clinicians in subsequent clinical evaluation and intervention. External validation is required to confirm these results.

Pre-treatment systemic immune-inflammation index as a non-invasive biomarker for predicting clinical outcomes in patients with renal cell carcinoma: a meta-analysis of 20 studies

INTRODUCTION

To systematically assess the predictive significance of systemic immune-inflammation index (SII) in renal cell carcinoma (RCC).

METHODS

Relevant studies published before November 2022 were retrieved from public databases. Hazard ratio (HR), standardised mean difference (SMD) and relative risk (RR) were calculated to estimate associations of SII with prognosis, treatment responses and clinicopathological features.

RESULTS

Twenty studies involving 6887 patients were eligible. The meta-analysis results revealed a high SII level was associated with worse overall survival (HR: 1.45, p < 0.001), progression-free survival (HR: 1.63, p = 0.001), cancer-specific survival (HR: 1.86, p < 0.001), lower overall response rate (RR: 0.62, p = 0.003), disease control rate (RR: 0.69, p = 0.002), larger tumour size (SMD: 0.39, p = 0.001), poorer IMDC risk (RR: 7.09, p < 0.001), higher Fuhrman grade (RR: 1.54, p = 0.004), tumour stage (RR: 1.67, p = 0.045), the presence of distant metastasis (brain: RR, 2.04, p = 0.001; bone: RR, 1.33, p = 0.024) and tumour necrosis (RR: 1.57, p = 0.031). Subgroup analysis showed SII predicted OS and PFS for non-Asian, but CSS for both Asian and non-Asian populations.

CONCLUSION

Pre-treatment SII may be a promising predictor of clinical outcomes for RCC patients.

Successful first-line treatment of simultaneous multiple primary malignancies of lung adenocarcinoma and renal clear cell carcinoma: A case report

Background

Multiple Primary Malignancies (MPMs) refer to the occurrence of two or more primary malignancies in the same organ or multiple organs and tissues of the same patient simultaneously or sequentially, with an incidence rate ranging from 2-17%. According to the difference in the time of occurrence of each primary tumor, MPMs can be classified as simultaneous malignancies and heterochronic malignancies. The former refers to the occurrence of two or more malignancies one after another within 6 months, while the latter refers to the occurrence of two malignancies at an interval of more than 6 months. Currently, there is a lack of effective treatment options for MPMs both nationally and internationally.

Case presentation

The patient was a 65-year-old male smoker with a definite diagnosis of advanced lung adenocarcinoma with kirsten rat sarcoma viral oncogene (KRAS) mutation, concomitant with primary renal clear cell carcinoma (RCCC), who had a progression-free survival (PFS) for 7 months after first-line treatment with albumin-bound paclitaxel and cisplatin in combination with sintilimab.

Conclusion

In this paper, we report a case of advanced lung adenocarcinoma combined with RCCC as a concurrent double primary malignancy, which achieved a satisfactory outcome after first-line chemotherapy combined with immunotherapy, with the aim of exploring effective treatment modalities for this type of MPMs, in order to improve the survival and prognosis of the patient.

Development and Validation of a Predictive Model to Evaluate the Risk of Bone Metastasis in Kidney Cancer

Background

Bone is a common target of metastasis in kidney cancer, and accurately predicting the risk of bone metastases (BMs) facilitates risk stratification and precision medicine in kidney cancer.

Methods

Patients diagnosed with kidney cancer were extracted from the Surveillance, Epidemiology, and End Results (SEER) database to comprise the training group from 2010 to 2017, and the validation group was drawn from our academic medical center. Univariate and multivariate logistic regression analyses explored the statistical relationships between the included variables and BM. Statistically significant risk factors were applied to develop a nomogram. Calibration plots, receiver operating characteristic (ROC) curves, probability density functions (PDF), and clinical utility curves (CUC) were used to verify the predictive performance. Kaplan-Meier (KM) curves demonstrated survival differences between two subgroups of kidney cancer with and without BMs. A convenient web calculator was provided for users via “shiny” package.

Results

A total of 43,503 patients were recruited in this study, of which 42,650 were training group cases and 853 validation group cases. The variables included in the nomogram were sex, pathological grade, T-stage, N-stage, sequence number, brain metastases, liver metastasis, pulmonary metastasis, histological type, primary site, and laterality. The calibration plots confirmed good agreement between the prediction model and the actual results. The area under the curve (AUC) values in the training and validation groups were 0.952 (95% CI, 0.950–0.954) and 0.836 (95% CI, 0.809–0.860), respectively. Based on CUC, we recommend a threshold probability of 5% to guide the diagnosis of BMs.

Conclusions

The comprehensive predictive tool consisting of nomogram and web calculator contributes to risk stratification which helped clinicians identify high-risk cases and provide personalized treatment options.

TF-RBP-AS Triplet Analysis Reveals the Mechanisms of Aberrant Alternative Splicing Events in Kidney Cancer: Implications for Their Possible Clinical Use as Prognostic and Therapeutic Biomarkers

Aberrant alternative splicing (AS) is increasingly linked to cancer; however, how AS contributes to cancer development still remains largely unknown. AS events (ASEs) are largely regulated by RNA-binding proteins (RBPs) whose ability can be modulated by a variety of genetic and epigenetic mechanisms. In this study, we used a computational framework to investigate the roles of transcription factors (TFs) on regulating RBP-AS interactions. A total of 6519 TF–RBP–AS triplets were identified, including 290 TFs, 175 RBPs, and 16 ASEs from TCGA–KIRC RNA sequencing data. TF function categories were defined according to correlation changes between RBP expression and their targeted ASEs. The results suggested that most TFs affected multiple targets, and six different classes of TF-mediated transcriptional dysregulations were identified. Then, regulatory networks were constructed for TF–RBP–AS triplets. Further pathway-enrichment analysis showed that these TFs and RBPs involved in triplets were enriched in a variety of pathways that were associated with cancer development and progression. Survival analysis showed that some triplets were highly associated with survival rates. These findings demonstrated that the integration of TFs into alternative splicing regulatory networks can help us in understanding the roles of alternative splicing in cancer.