Risk-based screening and prognostic analysis for second primary malignancies in kidney cancer patients: a retrospective cohort study based on large-scale population and Mendelian randomization analysis

TL;DR

This study identifies risk factors and develops models to predict second cancers in kidney cancer patients, exploring genetic links to improve treatment strategies.

Contribution

The study introduces nomograms and genetic analyses to predict and understand second primary malignancies in kidney cancer patients.

Findings

Kidney cancer patients have a 42% higher risk of developing second primary malignancies compared to the general population.

Mendelian randomization suggests kidney cancer may causally increase risks for cancers in the stomach, colon, rectum, lung, prostate, bladder, skin, and eye.

Transcriptome-wide analysis identified 19 susceptibility genes linked to four cancer types.

Abstract

Background: Second primary malignancy (SPM) significantly impacts the survival of patients. This study endeavors to identify risk and prognostic factors of developing SPM after the first primary kidney cancer (FPKC), develop nomograms and explore potential mechanisms to optimize treatment strategies.

Methods: Data of patients diagnosed with FPKC between 2000 and 2020 were obtained from the SEER database. The standardized incidence ratio (SIR) was calculated to assess the relative risk of developing SPM in FPKC patients. Competing risk model as well as Cox regression analyses were employed to identify independent risk and prognostic factors, and nomograms were constructed and evaluated. Finally, to understand how FPKC influences the risk of developing SPM, we carried out Mendelian randomization (MR) and transcriptome-wide association study (TWAS) analyses.

Results: A total of 72408 and 5295 patients were included in stage I and II analysis, respectively. Risk distribution analysis revealed that FPKC patients exhibited a higher SPM risk than general population (SIR = 1.42, 95% CI: 1.40-1.44). Independent predictive factors were identified for model construction, and nomograms were developed. AUC of ROC, calibration curves and DCA illustrated excellent calibration and clinical applicability of the models. MR analyses indicated that kidney cancer might causally increase the risk of cancer in stomach, colon, rectum, lung, prostate, bladder, skin and eye. TWAS analysis identified 19 susceptibility genes associated with four types of cancers.

Conclusion: This study successfully established nomograms, delving into the potential mechanisms of developing SPM after FPKC. All these findings will promote the optimization of treatment strategies.

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