Additional file 1 of LncRNA LBX2-AS1 promotes proliferation and migratory capacity of clear cell renal cell carcinoma through mitophagy

Additional file 1. Table S1. Sequences of primers used in qPCR

Image_4_Comprehensive investigation into cuproptosis in the characterization of clinical features, molecular characteristics, and immune situations of clear cell renal cell carcinoma.tif

BackgroundCopper-induced cell death has been widely investigated in human diseases as a form of programmed cell death (PCD). The newly recognized mechanism underlying copper-induced cell death provided us creative insights into the copper-related toxicity in cells, and this form of PCD was termed cuproptosis.MethodsThrough consensus clustering analysis, ccRCC patients from TCGA database were classified into different subgroups with distinct cuproptosis-based molecular patterns. Analyses of clinical significance, long-term survival, and immune features were performed on subgroups accordingly. The cuproptosis-based risk signature and nomogram were constructed and validated relying on the ccRCC cohort as well. The cuproptosis scoring system was generated to better characterize ccRCC patients. Finally, in vitro validation was conducted using ccRCC clinical samples and cell lines.ResultPatients from different subgroups displayed diverse clinicopathological features, survival outcomes, tumor microenvironment (TME) characteristics, immune-related score, and therapeutic responses. The prognostic model and cuproptosis score were well validated and proved to efficiently distinguish the high risk/score and low risk/score patients, which revealed the great predictive value. The cuproptosis score also tended out to be intimately associated with the prognosis and immune features of ccRCC patients. Additionally, the hub cuproptosis-associated gene (CAG) FDX1 presented a dysregulated expression pattern in human ccRCC samples, and it was confirmed to effectively promote the killing effects of copper ionophore elesclomol as a direct target. In vitro functional assays revealed the prominent anti-cancer role of FDX1 in ccRCC.ConclusionCuproptosis played an indispensable role in the regulation of TME features, tumor progression, and long-term prognosis of ccRCC.</p

Fluorescence imaging projective surgical navigation system.

<p>(a) Schematic diagram of the projective surgical navigation system; (b) Projective surgical navigation system in working mode.</p

ROC of our new model, PSA, PSAD and f/t. The AUC of these predictors were 0.789, 0.566, 0.664 and 0.654 respectively.

<p>ROC of our new model, PSA, PSAD and f/t. The AUC of these predictors were 0.789, 0.566, 0.664 and 0.654 respectively.</p

Phantom study to show the projective accuracy as the function of the height difference between the projection plane and the reference plane.

<p>(a)—(e): As the projection plane moves from 40 mm above to 40 mm below the registered reference plane, the projected image is biased from left to the right of the actual phantom. (c): If the projection plane is identical to the reference plane, the projected image and the actual phantom are co-registered without bias. (f) Linear relationship is observed between the projection bias and the height difference without height correction, while the projection bias is controlled within 1 mm for all the height differences after the height correction algorithm is applied.</p

Benchtop and Animal Validation of a Projective Imaging System for Potential Use in Intraoperative Surgical Guidance

<div><p>We propose a projective navigation system for fluorescence imaging and image display in a natural mode of visual perception. The system consists of an excitation light source, a monochromatic charge coupled device (CCD) camera, a host computer, a projector, a proximity sensor and a Complementary metal–oxide–semiconductor (CMOS) camera. With perspective transformation and calibration, our surgical navigation system is able to achieve an overall imaging speed higher than 60 frames per second, with a latency of 330 ms, a spatial sensitivity better than 0.5 mm in both vertical and horizontal directions, and a projection bias less than 1 mm. The technical feasibility of image-guided surgery is demonstrated in both agar-agar gel phantoms and an ex vivo chicken breast model embedding Indocyanine Green (ICG). The biological utility of the system is demonstrated in vivo in a classic model of ICG hepatic metabolism. Our benchtop, ex vivo and in vivo experiments demonstrate the clinical potential for intraoperative delineation of disease margin and image-guided resection surgery.</p></div

In vivo, ICG concentration–time course in mouse liver following ICG injection.

<p>(a) The nude mouse before dissection at 3 hours after ICG tail intravenous injection; (b) After the mouse is sacrificed by cervical vertebra dislocation, we dissect its abdomen and observe the anatomy map. (c) The fluorescence image captured by NIR camera. The area into the white frame is used to calculate fluorescence intensity. (d)The image of nude mouse under projective navigation system captured by nude eye. (e) Corresponding fluorescence intensity basis time courses.</p

Management for patients with PSA from 4 to 10 ng/ml based on our new model.

<p>Management for patients with PSA from 4 to 10 ng/ml based on our new model.</p

Comparison of patients' characteristics between two stages.

<p>*This table showed most patients' basic information at their first visits (patients with watchful waiting) or latest visits before biopsies (patients with biopsies).</p>#<p>In stage two, positive rate was discussed in patients with biopsies only.</p>a<p>. Student's t-test for age, PSA, fPSA, PV, PSAD, f/t and PCaR distributions between two stages.</p>b<p>. Two-sided χ2-test or Fish's exact test for DRE findings, Hypoechoic, No. of subjects with biopsies, Positive cases and Gleason score between two stages.</p><p>Comparison of patients' characteristics between two stages.</p

Nomogram for predicting a positive prostate biopsy.

<p>Locate patient values on each axis, and compare to the ‘Point’ axis to determine how many points are attributed to each variable. Then, locate the sum of the points for all variables on the ‘Total Points’ line to determine the individual probability of prostate cancer on the ‘PCaR’ line.</p