The top-down layout of the tissues corresponds to average declining of cancer incidence in them by term hazard ratio (HR_CVD) proposed by authors [4] on the base of preexisted cardio-vascular diseases (CVD). For correct statistic assumption, we made slight rearrangement of these original data for each cancer site in both ranges of HR (with atherosclerosis HR_aCVD and without it HR_naCVD). After rearrangement the sites in both ranges has got the same number (nm.) from the first 1 to the last 20: 1-lung, 2-liver, 3-brain, 4-hematologic, 5-neuroendocrine, 6-renal, 7-pancreas, 8-lymphoid, 9-bladder, 10-colon, 11-lip oral-head-neck, 12-leukocytic, 13-rectum and anus, 14-thyroid, 15-soft tissue, 16-prostate, 17-skin, 18-ovarian, 19-breast, 20-uterine. The both corrected ranges were approximated by us with exponential functions. The composition and sequence of the lines of tissues sites, had kept fixed throughout of present study. All relations in article have approximated by different functions generated in Excel automatically, keeping the line of tissue sites fixed, without any additional rearrangement.

As the people in the control subgroup without CVD (noCVD) in [4] were younger, than those in subgroups with CVD (of 18 - 39 years vs. of 45 - 59 years old), it had to be seen, how far could this imbalance affect received results. To examine the influence of age-related disbalance in [4] , we test first the prognostic properties of HRs from [4] on extracted from [6] newly estimated by U.S. Census Bureau the cases (×10⁻⁵) and the death (×10⁻⁵) in 2024 for range of cancer sites, needed as a referent one, representing total 335,893 × 10⁶ of entire U.S. population. Thus, the data extracted from both original sources were used in our modification.

In parallel, we examined the dependence of cancer case and death in the range of tissues [6] from native content of different hematopoietic CD markers in them. These global data tabulated in [7] in term “consensus” ones. They were used in modification proposed by us earlier [5] . It consists in a conversion of each consensus value of marker (i) in tissue (z) into the ratio (relative unit, r.u.), normalized to the marker’s value for bone marrow (iBM). The parameter (iz:iBM) permits to evaluate real content of each marker in different tissues, ignoring the errors related to detection of different (i) in different (z). The primary level of transcribed mRNA molecules (nTPM) for each of eight CD proteins in each of twenty organs were normalized preliminary to the corresponding nTPM in bone marrow (BM). Each relative unit, r.u., corresponds one of eight CD-proteins and one of twenty tissues. The average relative unit M in twenty normal tissue cites (n = 20) for each of eight single markers formed the line: 7.3 of CD34, 3.9 of CD25, 3.2 of CD133, 1.6 of CD31, 1.0 of CD2, 0.41 of CD47, 0.16 of Ki67, and 0.1 of CD247. So, the CD34 marker dominates in tissues, and the CD247 (unmatured T-cells) is least represented quantitatively.

By same manner we investigate the relation of CD markers in tissues from [7] and HRs from [4] .

Finally, all newly received data were combined and discussed with comprehensive conclusion. Therefore, we would like to contribute the new arguments in the problem of selective influence of CVD on incidence of cancer in different tissue sites, basing on the data received by us earlier [5] [8] - [11] .

The eight protein markers for each of twenty tissue/organ included: marker of proliferation Ki67, hematopoietic stem cells CD133 and CD34, Treg CD25, vascular CD31, total T-cells CD2, CD47 and CD247. All of CD247 indicate young post-thymic T-cells, which function is still unclear, because they often are discussed as a contributing to the suppression of immune surveillance, antitumor activity, and contributing to neoplastic progression [12] . They unite γ, δ T-cells, T-reg, naive CD4 T-cells, NK Tcells, memory CD8 T-cells, MAIT T-cells, double D4+ CD8+, CD4− CD8−, naive CD8 Tcells, memory CD4 T-cells.

Individual parameters were evaluated statistically with the calculation of the mean value M, standard deviation (SD) and standard error (SE). The average values of M were compared using the t-test and the probability of p. The data compared by mean value (M), standard deviation (SD), standard error (SE), t-criterion and probability p. The trends of the aggregate parameters evaluated by mathematical functions generated automatically in the Excel program. The coefficient of determination R² used as a statistical measure of the goodness of fit of the regression line to the data. R² confirmed by Equation (2) for its t -parameter:

$t=R^2\times \left(n-2\right)\times \left(1-R^2\right)$ (1)

Figure 1 presents the data extracted from [4] in our modification.

According exponential approximations, the both ranges of all cancer’s HRs are diminishing from lung site (x = 1) to uterine site (x = 20). According the formulae in the boxes, the maximum of HR for lung cancer (x = 1) in subgroups with atherosclerosis (HR_aCVD) is greater than in subgroup HR_naCVD (2.33 vs. 1.78) as well, as the rate of exponential reduction of HR per each site number (−0.056 vs. −0.037). The independent additional “atherosclerotic” HR (“d”) has maximum 0.59. The exponential rate of reduction of “d” is −0.24 per one cancer site, i.e. more than 6 times vs. rate −0.037 for HR_naCVD.

Thus, highest reduction’s rate of the atherosclerotic component of malignization d = 0.59e^−0.24x (in bi-exponential formula HR_aCVD = d + 1.78e^−0.037x) leads to its quick declining, which covered only 6 - 7 first sites (lung, liver, brain, lymphoid, hematologic, neuro endocrine). The HR for last 7 sites in the range of 12 - 20 (thyroid, soft tissue, prostate, melanoma, ovarian, breast, uterine) declines faster at atherosclerosis (HR_aCVD) than exponential rate −0.037 per one cancer site, which is typical for HR_naCVD. This our brief interpretation of original data extracted from [4] was developing according argument given in Methods 2.1.

Table 1 presents the results of testing HR with big data [6] . The estimated new cancers cases and deaths in adult population of US for the current calendar year 2024 [6] had distributed with new model methodology for range in twenty sites, which are identical to the same shown on Figure 1 , and interrelations of parameters evaluated by approximation in Excel program ( Table 1 ).

*The different mode of approximation (linear, exponential, or logarithmic) have preconditioned by best level of probability p for found relation. The valid equations are shown by bold fonts.

First result of the test is, that the numbers of the estimated cases (incidence) in US population 2024 in different sites do not fit the both types of HR as well, as their derivatives (d = HR_aCVD − HR_naCVD) and (ratio HR_aCVD:HR_naCVD).

The fail of all of them to prognose the numbers of cancer cases 2024 in general US population seems logic, because only 9.9% adult Americans have CVD (without hypertension) [13] , whereas small population of 2.7 × 10⁷ used in [4] was enriched with CVD as fare as 21% without hypertention too (coronary artery disease, peripheral arterial disease, cerebrovascular disease, heart failure, valvular disease, and congenital heart disease, and atrial fibrillation). Moreover, the proportion of people with a CVD used in [4] was enriched up to 8.2%, in comparing with aCVD incidence 0.1% - 0.6% persons-year estimated in US middle-aged adults [14] .

Second incorrection is the younger control subgroup noCVD vs. subgroups with CVD, in term of the percentage of the people +70 years old [4] . The distributions of age, built by us according data given in [4] , shows the excess people +70 in common CVD subgroup (18.8%) over control noCVD (1.7%). The entire adult population of US have people +70 about 5% (4.5 ÷ 5.5) only [15] , that is much closer to 1.7%, than to 18.8%.

Figure 2 shows the example of strong relation between the death rate from cancer and from CVD (per 10⁵ people) by age in US population 2000 [16] . This natural correlation may simulate the false dependency of cancer incidence from HR in [4] due to disbalance of age distribution in control noCVD and in the both CVD subgroups. Thus, the authors of [4] used the subpopulations with artificially enhanced content CVD and cancer death in older people tested vs. younger control noCVD. Consequently, they could interpret wrongly the nonspecific (age-related) increasing of the both incidence cancer and CVD, as a causal relationship between them.

The positive dependence of estimated deaths 2024 [6] on atherosclerosis HR_aCVD and derivates of [4] in Table 1 reflects the regularity shown on Figure 2 also. Thus, the excess of patients +70 years old in subgroup CVD [4] can be a cause of age-related increasing both cases CVD and cancer death as well, which was wrongly accepted as a result of CVD, not the age. It explains the absent of applicability HR_CVD from [4] to estimated cases of cancer in the test-population 2024, which is free from artificially enriched part of +70 people ( Table 1 ). Thus, our investigation confirms only the positive predictive power of atherosclerotic CVD, i.e. HR_aCVD from [4] , for number of estimated cancer’s death in the range of twenty sites of the test-population US 2024 [6] .

However, the analysis completed above, does not clarify the reasons for different sensitivity of organs to malignization, relation of malignization with cardio-vascular pathology, and prevalence of deadly atherosclerosis in sites at the beginning the range shown on Figure 1 .

A range of tissues, similar to shown on Figure 1 , we analyzed and discussed earlier in term of fifth years cancer’s survival in population of England [5] . We first argued the necessity to analyze of markers originated from bone marrow in different normal tissues instead their presence in the blood. The range of survival in sites turned out to be dependent to the content of CD 34 and CD31 in the sixteen tissues [5] . Partially, it was shown, that the lack of “CD34-stemness” and “CD31-vascularity” in liver, lung, esophagus, pancreas, and stomach shortens the time of local growth of future tumors in them, and bring closer a transition of logarithmic type of malignant biomass development into slower quasi-linear one, characterized with concomitant hypoxia, followed metastases, and death at lower survival [10] . Oppositely, higher preexisting level of CD34 and CD31markers in such normal tissues as bladder, breast, prostate, testis, thyroid, uterus predestined the better 5years survival in populations of England, delaying metastasis. The likeness of two cancer site ranges in two articles gave us opportunity to check an applicability our vision of resident CD-markers inside of normal tissues as the universal predictors both a cancers and CVDs level, in term of HR.

The numbers of twenty sites used in present investigation, relates quantitatively to only one of eight CD marker, namely stem CD34, which increases along with number of sites, from lung (nb.1) to the uteri (nb.20):

CD34 r.u. = 0.45 × nb. + 2.62, R² = 0.25, p = 0.02 (2)

The equation shows, that the less of CD34 in normal tissue is, the worse curability of cancer in it, and vice versa.

Table 2 shows the calculated relation of some hematopoietic markers, chosen us earlier [5] , with HR and estimated cases of 2024 by range of main tissue’s sites, as the potential targets for specific cancers. The data Table 2 does not confirm the optimism about the prognostic perspectives of CD25 positive Treg [18] . Other marker of normal tissues CD47, which is the signal “don’t-eat-me” to macrophages [3] , does not involved in CVD, or spontaneous malignization in the range of investigated sites, at least statistically.

The formulas have chosen according the best probability of an approximations of the four used: by linear, logarithmic, exponential, or power functions. The minus before “x” in formulas means its opposite quantitative association of “y” with “x”. The valid equations are shown by bold fonts. The mode of changing mortality: * by increasing of the cases, for CD34; **by increasing of the death for CD31 (y = 0.124x + 0.084, R² = 0.34, p = 0.007);** by increasing of death for CD2 (y = 0.27x^0.50, R² = 0.35, p=0.007); ***by insignificant decreasing of the death, for CD133; **** by insignificant increasing of the death, for CD247.

According to Table 2 , a number of CD34 marker in tissues declines HR_naCVD and atherosclerotic component d = HR_aCVD − HRn_aCVD (p = 0.05), i.e. heals the vascular pathology, but promotes the cancer cases (p = 0.04). The hematopoietic “stemness” CD34 in tissues induces initial growth in situ, because the number of cancer cases (incidence) 2024 becomes proportional to tissues’ content of stem marker CD34 (p = 0.04). Thus, “stemness” CD34 only simulate the decline of relative mortality by increasing of numbers of cases, i.e. denominator in fractional number “death: cases”.

The CD31 acts similar way with cases, but increase the deaths also. Thus, CD34 originated from BM in the healthy tissues, i.e. the CD34 “stemness” is a promotor of incidence new cases of cancer, but a protector for CVD. CD31” vascularity” in combination with CD34 only, is responsible for cancer death. Vascular endothelium marker CD31 associates with CD34 directly in endothelial cells, being a descendants of stem cells. It tends in tissues closer to the stem CD133 and CD34, than the “immune-competent, protective” T-cells marker CD2. For instance, a CD31 tissue’s content depends on CD2 less probable (y = 1.46x^0.29, R² = 0.21, p = 0.04), than on CD34 values (y = 0.6e^0.094x, R² = 0.49, p < 0.001).

Figure 3 illustrates the main results of the Table 2 .

The decreasing of HR_CVD associates with increasing of CD34 in tissues, and accompanies by multiplying of new cases of cancer, that points on favorite influence of “stemness” on both cardiovascular and malignant tissues. The increasing of CD31 with CD34 along with decreasing of HR_CVD confirms the protective role of CD31 in genesis of atherosclerosis [19] . The pattern of atherosclerosis (HR_aCVD − HR_naCVD) at the beginning of tissues range disappears further, as CD34 and CD31 increases in favorite sites at the end of range with excess of CD34 in prostate, ovarian, breast, and uteri. Moreover, both atherosclerosis and CVD are obviously suppressed at these sites due to higher content of CD34 in them.

The number of CD2 (total T cells population) increases together with the number of cancer cases (p = 0.035; Table 2 ) and the number of death (p = 0.007), that is the shock for doctrine of immune cells defense against cancer. The content of CD2 relate positively with progenitor marker of “vascularity” CD31 (y = 0.46x^0.72, R² = 0.21 p = 0.04), and young T-cells CD247 (y = 2.5199x^0.62, R² = 0.57, p < 0.001. Thus, the function of sub-fractions of CD 2 and CD247 young post thymic T cells seems rather protumor, though it used to associate with essential role in adaptive immune response [7] .

The number of CD247 are responsible for proliferation of normal tissues (Ki67 = 0.65 + 0.01, R² = 0.67, p < 0.001). Thus, high CD247 and Ki67 with low CD34 and CD31are rather harmful for sites at the beginning of the range ( Figure 4 ). The better curable sites at the end of the range relate to lack of CD247 and Ki67, but they are naturally abounded with CD34. We classify CD247, as nearest descendants of CD34, because they, as the witnesses, follow to CD34 oppositely, together with Ki67 ( Figure 4 ), manifesting early steps of differentiative activity of CD34. Interesting, that minimal numbers of CD34 (x), i.e. bad curability, combines closely with high number of proliferation’s marker Ki67 in normal tissues, y = −0.19ln(x) + 0.47, R² = 0.36, p = 0.005 ( Figure 4 ). As it can be logically associating with high number of T cells, the absurd of immune defense against cancer is getting stronger. Hence, bad curability relates to minimum CD34 and maximums CD2, CD247 together with Ki67, and vice versa. Some proves of that are in [20] .