Background: Large-scale loss-of-function screening database such as Cancer Dependency Map (Depmap) provide abundant resources. Investigation of these potential dependency genes from human cancer cell lines in the real-world patients cohort would evaluate their prognostic value thus facilitate their clinical application and guide drug development.

Tumor-associated macrophages (TAMs) promote tumor cell growth and metastasis in various human cancers. However, the role of TAMs in renal cell carcinoma (RCC) is rarely investigated. Herein, we observed that the infiltration of TAMs was obviously elevated in RCC tumor tissues, high infiltration of TAMs was closely associated with tumor progression and poor prognosis in RCC patients. In vitro assays further indicated that the conditioned medium of TAMs (TAMs CM) facilitated migration, invasion, and epithelial-mesenchymal transition (EMT) in RCC cells. Moreover, we found that IL-6 was involved in the functions of TAMs in RCC; IL-6 neutralizing antibody (IL-6NA) partly abolished the effect of TAMs on RCC cells. In addition, we demonstrated that TAMs might exert their roles by activating STAT3 signaling in RCC, and IL-6 was responsible for TAMs-induced STAT3 signaling activation. In conclusion, our results revealed that high infiltration of TAMs may promote RCC cells migration, invasion, and EMT via modulating IL-6/STAT3 signaling, further suggesting a potential of novel treatment strategies targeting TAMs or IL-6 for metastatic RCC.

Tumor cells circulating in the peripheral blood are the prime cause of cancer metastasis and death, thus the identification and discrimination of these rare cells are crucial in the diagnostic of cancer. As a label-free detection method without invasion, Raman spectroscopy has already been indicated as a promising method for cell identification. This study uses a confocal Raman spectrometer with 532 nm laser excitation to obtain the Raman spectrum of living cells from the kidney, liver, lung, skin, and breast. Multivariate statistical methods are applied to classify the Raman spectra of these cells. The results validate that these cells can be distinguished from each other. Among the models built to predict unknown cell types, the quadratic discriminant analysis model had the highest accuracy. The demonstrated analysis model, based on the Raman spectrum of cells, is propitious and has great potential in the field of biomedical for classifying circulating tumor cells in the future.