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The mean shift algorithm has been shown to perform well in optical image segmentation. However, the conventional mean shift algorithm performs poorly if it is directly used with Synthetic Aperture Radar (SAR) images due to the large dynamic range and strong speckle noise. Recently, the Generalized Mean Shift (GMS) algorithm with an adaptive variable asymmetric bandwidth has been proposed for Polarimetric SAR (PolSAR) image filtering. In this paper, the GMS algorithm is further developed for PolSAR image segmentation. A new merging predicate that is defined in the joint spatial-range domain is derived based on the GMS algorithm. A pre-sorting strategy and a post-processing step are also introduced into the GMS segmentation algorithm. The proposed algorithm can be directly used for PolSAR image superpixel segmentation without any pre-processing steps. Experiments using Airborne SAR (AirSAR) and Experimental SAR (ESAR) L-band PolSAR data demonstrate the effectiveness of the proposed superpixel segmentation algorithm. The parameter settings, stability, quality, and efficiency of the GMS algorithm are also discussed at the end of this paper.

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Background Circular RNA (circRNA) is a novel molecular marker and target candidate that is closely associated with tumor invasion and migration. The mechanism of action of hsa_circ_0005035 (circ‐IGF1R) in non‐small cell lung cancer remains unclear. In this study, we aimed to study the mechanism of action of circ‐IGF1R in lung cancer. Methods We screened circ‐IGF1R, one of the most notable differential expressions, from the Gene Expression Omnibus database, GSE104854, for further research. The expression level of circ‐IGF1R was examined using quantitative reverse transcription‐polymerase chain reaction (qRT‐PCR) in five different lung cancer cell lines and 50 pairs of lung cancer and adjacent tissues. Wound‐healing and Transwell assays were used for verifying the biological function of circ‐IGF1R. The effect of overexpressing circ‐IGF1R on the transcriptome of whole lung cancer cells was explored in lung cancer cell lines using RNA‐seq. Results The expression level of circ‐IGF1R was notably lower in lung cancer tissues and lung cancer cell lines than in the adjacent normal tissues and cells (P < 0.0001). In addition, the expression level of circ‐IGF1R was associated with larger tumors (T2/T3/T4) and lymph node metastasis (N1/ N2/N3) (P < 0.05). The overexpression of circ‐IGF1R significantly inhibited the invasion and migration of the lung cancer cells. The potential network of circ‐IGF1R–miR‐1270–VANGL2 was preliminarily determined, and the expression patterns of miR‐1270 and VANGL2 were verified in lung cancer cell lines. Conclusion Circ‐IGF1R may inhibit lung cancer invasion and migration through a potential network of circ‐IGF1R–miR‐1270–VANGL2.

Background Prognostic genes in the tumor microenvironment play an important role in immune biological processes and the response of cancer to immunotherapy. Thus, we aimed to assess new biomarkers that are associated with immune/stromal cells in lung adenocarcinomas (LUAD) using the ESTIMATE algorithm, which also significantly affects the prognosis of cancer. Methods The RNA sequencing (RNA-Seq) and clinical data of LUAD were downloaded from the the Cancer Genome Atlas (TCGA ). The immune and stromal scores were calculated for each sample using the ESTIMATE algorithm. The LUAD gene chip expression profile data and the clinical data (GSE37745, GSE11969, and GSE50081) were downloaded from the Gene Expression Omnibus (GEO) for subsequent validation analysis. Differentially expressed genes were calculated between high and low score groups. Univariate Cox regression analysis was performed on differentially expressed genes (DEGs) between the two groups to obtain initial prognosis genes. These were verified by three independent LUAD cohorts from the GEO database. Multivariate Cox regression was used to identify overall survival-related DEGs. UALCAN and the Human Protein Atlas were used to analyze the mRNA /protein expression levels of the target genes. Immune cell infiltration was evaluated using the Tumor Immune Estimation Resource (TIMER) and CIBERSORT methods, and stromal cell infiltration was assessed using xCell. Results In this study, immune scores and stromal scores are significantly associated with the clinical characteristics of LUAD, including T stage, M stage, pathological stage, and overall survival time. 530 DEGs (18 upregulated and 512 downregulated) were found to coexist in the difference analysis with the immune scores and stromal scores subgroup. Univariate Cox regression analysis showed that 286 of the 530 DEGs were survival-related genes (p < 0.05). Of the 286 genes initially identified, nine prognosis-related genes (CSF2RB, ITK, FLT3, CD79A, CCR4, CCR6, DOK2, AMPD1, and IGJ) were validated from three separate LUAD cohorts. In addition, functional analysis of DEGs also showed that various immunoregulatory molecular pathways, including regulation of immune response and the chemokine signaling pathways, were involved. Five genes (CCR6, ITK, CCR4, DOK2, and AMPD1) were identified as independent prognostic indicators of LUAD in specific data sets. The relationship between the expression levels of these genes and immune genes was assessed. We found that CCR6 mRNA and protein expression levels of LUAD were greater than in normal tissues. We evaluated the infiltration of immune cells and stromal cells in groups with high and low levels of expression of CCR6 in the TCGA LUAD cohort. In summary, we found a series of prognosis-related genes that were associated with the LUAD tumor microenvironment.

The simple linear iterative clustering (SLIC) algorithm shows good performance in superpixel generation for optical imagery. However, SLIC can perform poorly when there is too much noise in the image. To solve this problem, we have improved the cluster center initialization step and the postprocessing step, and then introduce the SLIC superpixel segmentation algorithm to the polarimetric synthetic aperture radar (PolSAR) image processing field. Experiments using AirSAR and ESAR L-band PolSAR data show that the improved SLIC algorithm can overcome the effect of speckle noise in PolSAR imagery, and it shows a better performance in detail preservation than the original SLIC algorithm and the normalized cuts superpixel segmentation algorithm.Index Terms-Polarimetric synthetic aperture radar (SAR) (PolSAR), SAR, segmentation, simple linear iterative clustering (SLIC), superpixel.

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