Association between copy-number alteration of +20q, -14q and -18p and cross-sensitivity to tyrosine kinase inhibitors in clear-cell renal cell carcinoma

Pan-cancer analysis of kinesin family members with potential implications in prognosis and immunological role in human cancer

Background

Kinesin is a molecular motor for transporting "goods" within cells and plays a key role in many types of tumors. The multi-angle study of kinesin at the pan-cancer level is conducive to understanding its role in tumorigenesis and development and clinical treatment potential.

Methods

We evaluated the expression of KIF genes, performed differential analysis by using the R package limma, and explored the pan-cancer prognosis of KIF genes by univariate Cox regression analysis. To evaluate the pan-cancer role of KIF genes as a whole, we defined the KIFscore with the help of gene set variation analysis (GSVA) and explored the KIFscores across normal tissues, tumor cell lines, and 33 tumor types in TCGA. Next, we used spearman correlation analysis to extensively study the correlation between the KIFscore and tumor prognosis and be-tween the KIFscore and clinical indicators. We also identified the relationship between the KIFscore and genomic variation and immune molecular signatures by multiplatform analysis. Finally, we identified the key genes in clear cell renal cell carcinoma (ccRCC) through machine learning algorithms and verified the candidate genes by CCK8, wound healing assay, Transwell assay, and flow cytometry.

Results

In most cancers, KIFscores are high and they act as a risk factor for cancer. The KIFscore was significantly associated with copy number variation (CNV), tumor mutation burden (TMB), immune subtypes, DNA repair deficiency, and tumor stemness indexes. Moreover, in almost all cancer species, the KIFscore was positively correlated with T cell CD4+ TH2, the common lymphoid pro-genitor, and the T cell follicular helper. In addition, it was negatively correlated with CXCL16, CCL14, TNFSF13, and TNFRSF14 and positively correlated with ULBP1, MICB, and CD276. Machine learning helped us to identify four hub-genes in ccRCC. The suitable gene, KIF14, is highly expressed in ccRCC and promotes tumor cell proliferation, migration, and invasion.

Conclusion

Our study shows that the KIF genes play an important pan-cancer role and may become a potential new target for a variety of tumor treatments in the future. Furthermore, KIF14, a key molecule in the KIF genes, can provide a new idea for the ccRCC treatment.

Risk prediction for metastasis of clear cell renal cell carcinoma using digital multiplex ligation-dependent probe amplification

Precise quantification of copy‐number alterations (CNAs) in a tumor genome is difficult. We have applied a comprehensive copy‐number analysis method, digital multiplex ligation‐dependent probe amplification (digitalMLPA), for targeted gene copy‐number analysis in clear cell renal cell carcinoma (ccRCC). Copy‐number status of all chromosomal arms and 11 genes was determined in 60 ccRCC samples. Chromosome 3p loss and 5q gain, known as early changes in ccRCC development, as well as losses at 9p and 14q were detected in 56/60 (93.3%), 31/60 (51.7%), 11/60 (18.3%), and 33/60 (55%), respectively. Through gene expression analysis, a significant positive correlation was detected in terms of 14q loss determined using digitalMLPA and downregulation of mRNA expression ratios with HIF1A and L2HGDH (P = .0253 and .0117, respectively). Patients with early metastasis (<1 y) (n = 18) showed CNAs in 6 arms (in median), whereas metastasis‐free patients (n = 34) showed those in significantly less arms (3 arms in median) (P = .0289). In particular, biallelic deletion of CDKN2A/2B was associated with multiple CNAs (≥7 arms) in 3 tumors. Together with sequence‐level mutations in genes VHL, PBRM1, SETD2, and BAP1, we performed multiple correspondence analysis, which identified the association of 9p loss and 4q loss with early metastasis (both P < .05). This analysis indicated the association of 4p loss and 1p loss with poor survival (both, P < .05). These findings suggest that CNAs have essential roles in aggressiveness of ccRCC. We showed that our approach of measuring CNA through digitalMLPA will facilitate the selection of patients who may develop metastasis.